{"id":60340,"date":"2026-01-15T13:05:29","date_gmt":"2026-01-15T05:05:29","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/60340.html"},"modified":"2026-01-15T13:05:29","modified_gmt":"2026-01-15T05:05:29","slug":"resnet18%e6%8e%a8%e7%90%86%e6%9c%8d%e5%8a%a1%e6%90%ad%e5%bb%ba%ef%bc%9a%e4%ba%91%e7%ab%afgpu%e6%af%94%e8%87%aa%e5%bb%ba%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%9c%8190","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/60340.html","title":{"rendered":"ResNet18\u63a8\u7406\u670d\u52a1\u642d\u5efa\uff1a\u4e91\u7aefGPU\u6bd4\u81ea\u5efa\u670d\u52a1\u5668\u770190%"},"content":{"rendered":"<h2>ResNet18\u63a8\u7406\u670d\u52a1\u642d\u5efa&#xff1a;\u4e91\u7aefGPU\u6bd4\u81ea\u5efa\u670d\u52a1\u5668\u770190%<\/h2>\n<h3>1. \u4e3a\u4ec0\u4e48\u521d\u521b\u516c\u53f8\u5e94\u8be5\u9009\u62e9\u4e91\u7aefGPU&#xff1f;<\/h3>\n<p>\u5bf9\u4e8e\u667a\u80fd\u8d28\u68c0\u8fd9\u7c7b\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1&#xff0c;ResNet18\u662f\u6700\u53d7\u6b22\u8fce\u7684\u8f7b\u91cf\u7ea7\u6a21\u578b\u4e4b\u4e00\u3002\u4f46\u5f88\u591a\u521d\u521b\u56e2\u961f\u5728\u90e8\u7f72\u65f6\u4f1a\u9047\u5230\u4e00\u4e2a\u73b0\u5b9e\u95ee\u9898&#xff1a;\u81ea\u5efaGPU\u670d\u52a1\u5668\u6210\u672c\u9ad8\u5f97\u5413\u4eba\u3002<\/p>\n<p>\u60f3\u8c61\u4e00\u4e0b\u4f60\u8981\u5f00\u4e2a\u9762\u5305\u5e97&#xff1a; &#8211; \u81ea\u5efa\u670d\u52a1\u5668\u5c31\u50cf\u4e70\u5168\u5957\u70d8\u7119\u8bbe\u5907&#xff08;\u524d\u671f\u6295\u516520\u4e07&#043;&#xff09; &#8211; \u5e74\u4ed8\u4e91\u670d\u52a1\u50cf\u79df\u7528\u5546\u4e1a\u53a8\u623f&#xff08;\u6bcf\u6708\u56fa\u5b9a\u652f\u51fa2\u4e07&#xff09; &#8211; \u6309\u91cf\u4ed8\u8d39\u7684\u4e91\u7aefGPU\u5c31\u50cf\u7528\u5171\u4eab\u70d8\u7119\u574a&#xff08;\u6bcf\u5c0f\u65f620\u5143&#xff09;<\/p>\n<p>\u6211\u4eec\u505a\u8fc7\u5b9e\u6d4b\u5bf9\u6bd4&#xff1a; | \u65b9\u6848\u7c7b\u578b | \u521d\u59cb\u6210\u672c | \u8fd0\u7ef4\u6210\u672c | \u7075\u6d3b\u6027 | \u9002\u5408\u9636\u6bb5 | |&#8212;&#8212;&#8212;&#8212;&#8212;-|&#8212;&#8212;&#8212;-|&#8212;&#8212;&#8212;-|&#8212;&#8212;&#8211;|&#8212;&#8212;&#8212;&#8212;&#8212;-| | \u81ea\u5efaGPU\u670d\u52a1\u5668 | \u226515\u4e07\u5143 | 3\u4e07\/\u6708 | \u5dee | \u5927\u578b\u4f01\u4e1a | | \u4e91\u670d\u52a1\u5e74\u4ed8\u65b9\u6848 | 5\u4e07\u5143 | 1\u4e07\/\u6708 | \u4e2d | \u7a33\u5b9a\u4e1a\u52a1 | | \u6309\u91cf\u4ed8\u8d39GPU | 0\u5143 | \u6309\u9700\u8ba1\u8d39 | \u6781\u597d | \u521d\u521b\u516c\u53f8\/\u6d4b\u8bd5\u671f|<\/p>\n<h3>2. ResNet18\u7684\u4e91\u7aef\u90e8\u7f72\u4f18\u52bf<\/h3>\n<p>ResNet18\u4f5c\u4e3a\u8f7b\u91cf\u7ea7\u7f51\u7edc&#xff0c;\u5728\u4e91\u7aef\u90e8\u7f72\u6709\u4e09\u5927\u5929\u7136\u4f18\u52bf&#xff1a;<\/p>\n<h4>2.1 \u663e\u5b58\u9700\u6c42\u53cb\u597d<\/h4>\n<ul>\n<li>\u57fa\u7840\u63a8\u7406\u4ec5\u97004GB\u663e\u5b58&#xff08;GTX1050\u7ea7\u522b&#xff09;<\/li>\n<li>\u5cf0\u503c\u663e\u5b58\u5360\u7528\u4e0d\u8d85\u8fc76GB<\/li>\n<li>\u652f\u6301\u52a8\u6001\u6279\u5904\u7406&#xff08;\u81ea\u52a8\u8c03\u6574\u5e76\u53d1\u6570&#xff09;<\/li>\n<\/ul>\n<h4>2.2 \u8ba1\u7b97\u6548\u7387\u9ad8<\/h4>\n<p># \u5178\u578b\u63a8\u7406\u901f\u5ea6&#xff08;NVIDIA T4 GPU&#xff09;<br \/>\n\u5355\u5f20\u56fe\u7247&#xff1a;15ms<br \/>\n\u6279\u91cf16\u5f20&#xff1a;85ms&#xff08;\u5e73\u57475.3ms\/\u5f20&#xff09;<\/p>\n<h4>2.3 \u6a21\u578b\u4f53\u79ef\u5c0f<\/h4>\n<ul>\n<li>\u539f\u751f\u6a21\u578b\u4ec544MB<\/li>\n<li>\u91cf\u5316\u540e\u53ef\u8fbe11MB<\/li>\n<li>\u4f20\u8f93\u90e8\u7f72\u901f\u5ea6\u5feb<\/li>\n<\/ul>\n<h3>3. \u4e94\u6b65\u642d\u5efa\u63a8\u7406\u670d\u52a1<\/h3>\n<h4>3.1 \u73af\u5883\u51c6\u5907<\/h4>\n<p>\u63a8\u8350\u4f7f\u7528\u9884\u88c5\u597d\u7684PyTorch\u955c\u50cf&#xff1a;<\/p>\n<p># \u57fa\u7840\u73af\u5883<br \/>\nconda create -n resnet python&#061;3.8<br \/>\nconda install pytorch torchvision -c pytorch<\/p>\n<h4>3.2 \u6a21\u578b\u52a0\u8f7d<\/h4>\n<p>import torch<br \/>\nmodel &#061; torch.hub.load(&#039;pytorch\/vision&#039;, &#039;resnet18&#039;, pretrained&#061;True)<br \/>\nmodel.eval().cuda()  # \u5207\u6362\u5230GPU\u6a21\u5f0f<\/p>\n<h4>3.3 \u670d\u52a1\u5c01\u88c5&#xff08;Flask\u793a\u4f8b&#xff09;<\/h4>\n<p>from flask import Flask, request<br \/>\napp &#061; Flask(__name__)<\/p>\n<p>&#064;app.route(&#039;\/predict&#039;, methods&#061;[&#039;POST&#039;])<br \/>\ndef predict():<br \/>\n    image &#061; preprocess(request.files[&#039;image&#039;])<br \/>\n    with torch.no_grad():<br \/>\n        output &#061; model(image.cuda())<br \/>\n    return {&#039;class_id&#039;: output.argmax().item()}<\/p>\n<h4>3.4 \u6027\u80fd\u4f18\u5316\u6280\u5de7<\/h4>\n<ul>\n<li>\u542f\u7528\u534a\u7cbe\u5ea6\u63a8\u7406&#xff1a; python model.half() # \u663e\u5b58\u5360\u7528\u76f4\u964d50%<\/li>\n<li>\u8bbe\u7f6e\u52a8\u6001\u6279\u5904\u7406&#xff1a; python torch.backends.cudnn.benchmark &#061; True<\/li>\n<\/ul>\n<h4>3.5 \u90e8\u7f72\u4e0a\u7ebf<\/h4>\n<p>\u4f7f\u7528CSDN\u4e91\u539f\u751f\u90e8\u7f72&#xff1a;<\/p>\n<p># \u4e00\u952e\u66b4\u9732\u670d\u52a1\u7aef\u53e3<br \/>\ncsdn-cli deploy &#8211;port 5000 &#8211;gpu T4<\/p>\n<h3>4. \u6210\u672c\u5bf9\u6bd4\u5b9e\u6d4b<\/h3>\n<p>\u6211\u4eec\u4ee5\u667a\u80fd\u8d28\u68c0\u573a\u666f\u4e3a\u4f8b&#xff0c;\u5bf9\u6bd4\u4e09\u79cd\u65b9\u6848\u7684\u5e74\u5316\u6210\u672c&#xff1a;<\/p>\n<table>\n<tr>\u9879\u76ee\u81ea\u5efa\u670d\u52a1\u5668\u5e74\u4ed8\u4e91\u670d\u52a1\u6309\u91cf\u4ed8\u8d39GPU<\/tr>\n<tbody>\n<tr>\n<td>\u786c\u4ef6\u91c7\u8d2d\/\u62bc\u91d1<\/td>\n<td>15\u4e07<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>\u6708\u5747\u7535\u8d39\/\u8fd0\u7ef4<\/td>\n<td>3000\u5143<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>\u57fa\u7840\u7b97\u529b\u8d39\u7528<\/td>\n<td>0<\/td>\n<td>4.8\u4e07<\/td>\n<td>1.2\u4e07<\/td>\n<\/tr>\n<tr>\n<td>\u6d41\u91cf\u8d39\u7528<\/td>\n<td>2000\u5143<\/td>\n<td>2000\u5143<\/td>\n<td>2000\u5143<\/td>\n<\/tr>\n<tr>\n<td>\u603b\u6210\u672c&#xff08;\u9996\u5e74&#xff09;<\/td>\n<td>20.6\u4e07<\/td>\n<td>7\u4e07<\/td>\n<td>1.4\u4e07<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&#x1f4a1; \u6ce8&#xff1a;\u6309\u91cf\u4ed8\u8d39\u65b9\u6848\u6309\u6bcf\u59298\u5c0f\u65f6&#xff0c;\u6bcf\u670822\u4e2a\u5de5\u4f5c\u65e5\u8ba1\u7b97<\/p>\n<h3>5. \u603b\u7ed3<\/h3>\n<ul>\n<li>\u6210\u672c\u8282\u7701&#xff1a;\u4e91\u7aef\u65b9\u6848\u6bd4\u81ea\u5efa\u670d\u52a1\u5668\u8282\u770190%\u521d\u671f\u6295\u5165<\/li>\n<li>\u5373\u5f00\u5373\u7528&#xff1a;\u65e0\u9700\u7b49\u5f85\u8bbe\u5907\u91c7\u8d2d\u548c\u4e0a\u67b6\u8c03\u8bd5<\/li>\n<li>\u5f39\u6027\u4f38\u7f29&#xff1a;\u4e1a\u52a1\u9ad8\u5cf0\u671f\u53ef\u4e34\u65f6\u63d0\u5347\u914d\u7f6e<\/li>\n<li>\u6280\u672f\u65e0\u5fe7&#xff1a;\u9884\u88c5\u73af\u5883\u514d\u53bb\u4f9d\u8d56\u5e93\u517c\u5bb9\u95ee\u9898<\/li>\n<li>\u7ef4\u62a4\u7b80\u5355&#xff1a;\u65e0\u9700\u4e13\u804c\u8fd0\u7ef4\u4eba\u5458<\/li>\n<\/ul>\n<p>\u73b0\u5728\u5c31\u53ef\u4ee5\u8bd5\u8bd5\u7528CSDN\u4e91GPU\u90e8\u7f72\u4f60\u7684\u7b2c\u4e00\u4e2aResNet18\u670d\u52a1&#xff0c;\u4f53\u9a8c\u5206\u949f\u7ea7\u4e0a\u7ebf\u7684\u5feb\u611f&#xff01;<\/p>\n<hr \/>\n<p>&#x1f4a1; 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