h5自适应网站建设是什么意思,简述酒店类网站开发的策略,做网站怎么租用服务器吗,图标的网站目录
效果
生成图片特征
查找踢足球的人
测试图片
模型信息
image_model.onnx
text_model.onnx
项目
代码
Form1.cs
Clip.cs
下载 C# Onnx Chinese CLIP 通过一句话从图库中搜出来符合要求的图片
效果
生成图片特征 查找踢足球的人 测试图片 模型信息
image_mod…目录
效果
生成图片特征
查找踢足球的人
测试图片
模型信息
image_model.onnx
text_model.onnx
项目
代码
Form1.cs
Clip.cs
下载 C# Onnx Chinese CLIP 通过一句话从图库中搜出来符合要求的图片
效果
生成图片特征 查找踢足球的人 测试图片 模型信息
image_model.onnx
Inputs ------------------------- nameimage tensorFloat[1, 3, 224, 224] ---------------------------------------------------------------
Outputs ------------------------- nameunnorm_image_features tensorFloat[1, 512] ---------------------------------------------------------------
text_model.onnx
Inputs ------------------------- nametext tensorInt64[1, 52] ---------------------------------------------------------------
Outputs ------------------------- nameunnorm_text_features tensorFloat[1, 512] ---------------------------------------------------------------
项目 代码
Form1.cs 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(); } Clip mynet new Clip(model/image_model.onnx, model/text_model.onnx, model/myvocab.txt); float[] imagedir_features; string image_dir test_img; StringBuilder sb new StringBuilder(); private void button2_Click(object sender, EventArgs e) { //特征向量 可以存二进制文件或者向量数据库 imagedir_features mynet.generate_imagedir_features(image_dir); txtInfo.Text 生成完成; txtInfo.Text 有 mynet.imgnum 张图片,特征向量长度 imagedir_features.Length; } private void button3_Click(object sender, EventArgs e) { if (imagedir_features null) { MessageBox.Show(请先生成图片特征); return; } sb.Clear(); txtInfo.Text ; lblInfo.Text ; pictureBox1.Image null; string input_text txt_input_text.Text; if (string.IsNullOrEmpty(input_text)) { return; } ListDictionarystring, float top5imglist mynet.input_text_search_image(input_text, imagedir_features, mynet.imglist); sb.AppendLine(top5:); foreach (var item in top5imglist) { sb.AppendLine(Path.GetFileName(item.Keys.First()) 相似度: item[item.Keys.First()].ToString(F2)); } txtInfo.Text sb.ToString(); lblInfo.Text Path.GetFileName(top5imglist[0].Keys.First()); pictureBox1.Image new Bitmap(top5imglist[0].Keys.First()); } private void Form1_Load(object sender, EventArgs e) { } } } 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();}Clip mynet new Clip(model/image_model.onnx, model/text_model.onnx, model/myvocab.txt);float[] imagedir_features;string image_dir test_img;StringBuilder sb new StringBuilder();private void button2_Click(object sender, EventArgs e){//特征向量 可以存二进制文件或者向量数据库imagedir_features mynet.generate_imagedir_features(image_dir);txtInfo.Text 生成完成;txtInfo.Text 有 mynet.imgnum 张图片,特征向量长度 imagedir_features.Length;}private void button3_Click(object sender, EventArgs e){if (imagedir_features null){MessageBox.Show(请先生成图片特征);return;}sb.Clear();txtInfo.Text ;lblInfo.Text ;pictureBox1.Image null;string input_text txt_input_text.Text;if (string.IsNullOrEmpty(input_text)){return;}ListDictionarystring, float top5imglist mynet.input_text_search_image(input_text, imagedir_features, mynet.imglist);sb.AppendLine(top5:);foreach (var item in top5imglist){sb.AppendLine(Path.GetFileName(item.Keys.First()) 相似度: item[item.Keys.First()].ToString(F2));}txtInfo.Text sb.ToString();lblInfo.Text Path.GetFileName(top5imglist[0].Keys.First());pictureBox1.Image new Bitmap(top5imglist[0].Keys.First());}private void Form1_Load(object sender, EventArgs e){}}
}Clip.cs
public class Clip { int inpWidth 224; int inpHeight 224; float[] mean new float[] { 0.48145466f, 0.4578275f, 0.40821073f }; float[] std new float[] { 0.26862954f, 0.26130258f, 0.27577711f }; int context_length 52; int len_text_feature 512; Net net; float[] image_features_input; SessionOptions options; InferenceSession onnx_session; Tensorlong input_tensor; ListNamedOnnxValue input_container; IDisposableReadOnlyCollectionDisposableNamedOnnxValue result_infer; DisposableNamedOnnxValue[] results_onnxvalue; Tensorfloat result_tensors; TokenizerBase tokenizer; int[] text_tokens_input; float[,] text_features_input; public int imgnum 0; public Liststring imglist new Liststring(); public Clip(string image_modelpath, string text_modelpath, string vocab_path) { net CvDnn.ReadNetFromOnnx(image_modelpath); // 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(text_modelpath, options);//model_path 为onnx模型文件的路径 // 创建输入容器 input_container new ListNamedOnnxValue(); load_tokenizer(vocab_path); } void load_tokenizer(string vocab_path) { tokenizer new TokenizerClipChinese(); tokenizer.load_tokenize(vocab_path); text_tokens_input new int[1024 * context_length]; } Mat normalize_(Mat src) { Cv2.CvtColor(src, src, ColorConversionCodes.BGR2RGB); Mat[] bgr src.Split(); for (int i 0; i bgr.Length; i) { bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]); } Cv2.Merge(bgr, src); foreach (Mat channel in bgr) { channel.Dispose(); } return src; } unsafe void generate_image_feature(Mat srcimg) { Mat temp_image new Mat(); Cv2.Resize(srcimg, temp_image, new Size(inpWidth, inpHeight), 0, 0, InterpolationFlags.Cubic); Mat normalized_mat normalize_(temp_image); Mat blob CvDnn.BlobFromImage(normalized_mat); net.SetInput(blob); //模型推理读取推理结果 Mat[] outs new Mat[1] { new Mat() }; string[] outBlobNames net.GetUnconnectedOutLayersNames().ToArray(); net.Forward(outs, outBlobNames); float* ptr_feat (float*)outs[0].Data; int len_image_feature outs[0].Size(1); //忽略第0维batchsize1, len_image_feature是定值512,跟len_text_feature相等的, 也可以写死在类成员变量里 image_features_input new float[len_image_feature]; float norm 0.0f; for (int i 0; i len_image_feature; i) { norm ptr_feat[i] * ptr_feat[i]; } norm (float)Math.Sqrt(norm); for (int i 0; i len_image_feature; i) { image_features_input[i] ptr_feat[i] / norm; } } unsafe void generate_text_feature(Liststring texts) { ListListint text_token new ListListint(texts.Count); for (int i 0; i texts.Count; i) { text_token.Add(new Listint()); } for (int i 0; i texts.Count; i) { tokenizer.encode_text(texts[i], text_token[i]); } if (text_token.Count * context_length text_tokens_input.Length) { text_tokens_input new int[text_token.Count * context_length]; } foreach (int i in text_tokens_input) { text_tokens_input[i] 0; } for (int i 0; i text_token.Count; i) { if (text_token[i].Count context_length) { Console.WriteLine(text_features index i ,bigger than context_length \n); continue; } for (int j 0; j text_token[i].Count; j) { text_tokens_input[i * context_length j] text_token[i][j]; } } int[] text_token_shape new int[] { 1, context_length }; text_features_input new float[text_token.Count, len_text_feature]; long[] text_tokens_input_64 new long[texts.Count * context_length]; for (int i 0; i text_tokens_input_64.Length; i) { text_tokens_input_64[i] text_tokens_input[i]; } for (int i 0; i text_token.Count; i) { input_tensor new DenseTensorlong(text_tokens_input_64, new[] { 1, 52 }); input_container.Clear(); input_container.Add(NamedOnnxValue.CreateFromTensor(text, input_tensor)); //运行 Inference 并获取结果 result_infer onnx_session.Run(input_container); // 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].AsTensorfloat(); float[] text_feature_ptr results_onnxvalue[0].AsTensorfloat().ToArray(); float norm 0.0f; for (int j 0; j len_text_feature; j) { norm text_feature_ptr[j] * text_feature_ptr[j]; } norm (float)Math.Sqrt(norm); for (int j 0; j len_text_feature; j) { text_features_input[i, j] text_feature_ptr[j] / norm; } } } void softmax(float[] input) { int length input.Length; float[] exp_x new float[length]; float maxVal input.Max(); float sum 0; for (int i 0; i length; i) { float expval (float)Math.Exp(input[i] - maxVal); exp_x[i] expval; sum expval; } for (int i 0; i length; i) { input[i] exp_x[i] / sum; } } int[] argsort_ascend(float[] array) { int array_len array.Length; int[] array_index new int[array_len]; for (int i 0; i array_len; i) { array_index[i] i; } var temp array_index.ToList(); temp.Sort((pos1, pos2) { if (array[pos1] array[pos2]) { return -1; } else if (array[pos1] array[pos2]) { return 0; } else { return 0; } }); return temp.ToArray(); } public ListDictionarystring, float input_text_search_image(string text, float[] image_features, Liststring imglist) { int imgnum imglist.Count; Liststring texts new Liststring { text }; generate_text_feature(texts); float[] logits_per_image new float[imgnum]; for (int i 0; i imgnum; i) { float sum 0; for (int j 0; j len_text_feature; j) { sum image_features[i * len_text_feature j] * text_features_input[0, j]; //图片特征向量跟文本特征向量做内积 } logits_per_image[i] 100 * sum; } softmax(logits_per_image); int[] index argsort_ascend(logits_per_image); ListDictionarystring, float top5imglist new ListDictionarystring, float(5); for (int i 0; i 5; i) { int ind index[imgnum - 1 - i]; Dictionarystring, float result new Dictionarystring, float(); result.Add(imglist[ind], logits_per_image[ind]); top5imglist.Add(result); } return top5imglist; } public float[] generate_imagedir_features(string image_dir) { imglist Common.listdir(image_dir); imgnum imglist.Count; Console.WriteLine(遍历到 imgnum 张图片); float[] imagedir_features new float[0]; for (int i 0; i imgnum; i) { string imgpath imglist[i]; Mat srcimg Cv2.ImRead(imgpath); generate_image_feature(srcimg); imagedir_features imagedir_features.Concat(image_features_input).ToArray(); srcimg.Dispose(); } return imagedir_features; } }
public class Clip{int inpWidth 224;int inpHeight 224;float[] mean new float[] { 0.48145466f, 0.4578275f, 0.40821073f };float[] std new float[] { 0.26862954f, 0.26130258f, 0.27577711f };int context_length 52;int len_text_feature 512;Net net;float[] image_features_input;SessionOptions options;InferenceSession onnx_session;Tensorlong input_tensor;ListNamedOnnxValue input_container;IDisposableReadOnlyCollectionDisposableNamedOnnxValue result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensorfloat result_tensors;TokenizerBase tokenizer;int[] text_tokens_input;float[,] text_features_input;public int imgnum 0;public Liststring imglist new Liststring();public Clip(string image_modelpath, string text_modelpath, string vocab_path){net CvDnn.ReadNetFromOnnx(image_modelpath);// 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(text_modelpath, options);//model_path 为onnx模型文件的路径// 创建输入容器input_container new ListNamedOnnxValue();load_tokenizer(vocab_path);}void load_tokenizer(string vocab_path){tokenizer new TokenizerClipChinese();tokenizer.load_tokenize(vocab_path);text_tokens_input new int[1024 * context_length];}Mat normalize_(Mat src){Cv2.CvtColor(src, src, ColorConversionCodes.BGR2RGB);Mat[] bgr src.Split();for (int i 0; i bgr.Length; i){bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]);}Cv2.Merge(bgr, src);foreach (Mat channel in bgr){channel.Dispose();}return src;}unsafe void generate_image_feature(Mat srcimg){Mat temp_image new Mat();Cv2.Resize(srcimg, temp_image, new Size(inpWidth, inpHeight), 0, 0, InterpolationFlags.Cubic);Mat normalized_mat normalize_(temp_image);Mat blob CvDnn.BlobFromImage(normalized_mat);net.SetInput(blob);//模型推理读取推理结果Mat[] outs new Mat[1] { new Mat() };string[] outBlobNames net.GetUnconnectedOutLayersNames().ToArray();net.Forward(outs, outBlobNames);float* ptr_feat (float*)outs[0].Data;int len_image_feature outs[0].Size(1); //忽略第0维batchsize1, len_image_feature是定值512,跟len_text_feature相等的, 也可以写死在类成员变量里image_features_input new float[len_image_feature];float norm 0.0f;for (int i 0; i len_image_feature; i){norm ptr_feat[i] * ptr_feat[i];}norm (float)Math.Sqrt(norm);for (int i 0; i len_image_feature; i){image_features_input[i] ptr_feat[i] / norm;}}unsafe void generate_text_feature(Liststring texts){ListListint text_token new ListListint(texts.Count);for (int i 0; i texts.Count; i){text_token.Add(new Listint());}for (int i 0; i texts.Count; i){tokenizer.encode_text(texts[i], text_token[i]);}if (text_token.Count * context_length text_tokens_input.Length){text_tokens_input new int[text_token.Count * context_length];}foreach (int i in text_tokens_input) { text_tokens_input[i] 0; }for (int i 0; i text_token.Count; i){if (text_token[i].Count context_length){Console.WriteLine(text_features index i ,bigger than context_length \n);continue;}for (int j 0; j text_token[i].Count; j){text_tokens_input[i * context_length j] text_token[i][j];}}int[] text_token_shape new int[] { 1, context_length };text_features_input new float[text_token.Count, len_text_feature];long[] text_tokens_input_64 new long[texts.Count * context_length];for (int i 0; i text_tokens_input_64.Length; i){text_tokens_input_64[i] text_tokens_input[i];}for (int i 0; i text_token.Count; i){input_tensor new DenseTensorlong(text_tokens_input_64, new[] { 1, 52 });input_container.Clear();input_container.Add(NamedOnnxValue.CreateFromTensor(text, input_tensor));//运行 Inference 并获取结果result_infer onnx_session.Run(input_container);// 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].AsTensorfloat();float[] text_feature_ptr results_onnxvalue[0].AsTensorfloat().ToArray();float norm 0.0f;for (int j 0; j len_text_feature; j){norm text_feature_ptr[j] * text_feature_ptr[j];}norm (float)Math.Sqrt(norm);for (int j 0; j len_text_feature; j){text_features_input[i, j] text_feature_ptr[j] / norm;}}}void softmax(float[] input){int length input.Length;float[] exp_x new float[length];float maxVal input.Max();float sum 0;for (int i 0; i length; i){float expval (float)Math.Exp(input[i] - maxVal);exp_x[i] expval;sum expval;}for (int i 0; i length; i){input[i] exp_x[i] / sum;}}int[] argsort_ascend(float[] array){int array_len array.Length;int[] array_index new int[array_len];for (int i 0; i array_len; i){array_index[i] i;}var temp array_index.ToList();temp.Sort((pos1, pos2) {if (array[pos1] array[pos2]){return -1;}else if (array[pos1] array[pos2]){return 0;}else{return 0;}});return temp.ToArray();}public ListDictionarystring, float input_text_search_image(string text, float[] image_features, Liststring imglist){int imgnum imglist.Count;Liststring texts new Liststring { text };generate_text_feature(texts);float[] logits_per_image new float[imgnum];for (int i 0; i imgnum; i){float sum 0;for (int j 0; j len_text_feature; j){sum image_features[i * len_text_feature j] * text_features_input[0, j]; //图片特征向量跟文本特征向量做内积}logits_per_image[i] 100 * sum;}softmax(logits_per_image);int[] index argsort_ascend(logits_per_image);ListDictionarystring, float top5imglist new ListDictionarystring, float(5);for (int i 0; i 5; i){int ind index[imgnum - 1 - i];Dictionarystring, float result new Dictionarystring, float();result.Add(imglist[ind], logits_per_image[ind]);top5imglist.Add(result);}return top5imglist;}public float[] generate_imagedir_features(string image_dir){imglist Common.listdir(image_dir);imgnum imglist.Count;Console.WriteLine(遍历到 imgnum 张图片);float[] imagedir_features new float[0];for (int i 0; i imgnum; i){string imgpath imglist[i];Mat srcimg Cv2.ImRead(imgpath);generate_image_feature(srcimg);imagedir_features imagedir_features.Concat(image_features_input).ToArray();srcimg.Dispose();}return imagedir_features;}}
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