{"id":21221,"date":"2024-07-18T09:34:16","date_gmt":"2024-07-18T01:34:16","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=21221"},"modified":"2024-07-18T09:34:16","modified_gmt":"2024-07-18T01:34:16","slug":"%e5%9f%ba%e4%ba%8e-pytorch-%e7%9a%84%e4%ba%ba%e8%84%b8%e5%85%b3%e9%94%ae%e7%82%b9%e6%a3%80%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2024\/07\/18\/21221\/","title":{"rendered":"\u57fa\u4e8e PyTorch \u7684\u4eba\u8138\u5173\u952e\u70b9\u68c0\u6d4b"},"content":{"rendered":"<div class=\"article-desc\">\u4f60\u662f\u5426\u60f3\u8fc7Instagram\u662f\u5982\u4f55\u7ed9\u4f60\u7684\u8138\u4e0a\u5e94\u7528\u60ca\u4eba\u7684\u6ee4\u955c\u7684\uff1f\u8be5\u8f6f\u4ef6\u68c0\u6d4b\u4f60\u8138\u4e0a\u7684\u5173\u952e\u70b9\u5e76\u5728\u5176\u4e0a\u6295\u5f71\u4e00\u4e2a\u906e\u7f69\u3002\u672c\u6559\u7a0b\u5c06\u6559\u4f60\u5982\u4f55\u4f7f\u7528PyTorch\u6784\u5efa\u4e00\u4e2a\u7c7b\u4f3c\u7684\u8f6f\u4ef6\u3002<\/div>\n<div><img data-dominant-color=\"4a87ac\" data-has-transparency=\"false\" style=\"--dominant-color: #4a87ac;\" loading=\"lazy\" decoding=\"async\" class=\"not-transparent alignnone size-full wp-image-21223\" src=\"https:\/\/aiforumimage.oss-cn-shanghai.aliyuncs.com\/wp-content\/uploads\/2024\/07\/29670e3888cdc1fe1e8256e68accee9c70d6e2-300x178-1.jpg\" width=\"300\" height=\"178\" alt=\"\" srcset=\"https:\/\/aiforumimage.oss-cn-shanghai.aliyuncs.com\/wp-content\/uploads\/2024\/07\/29670e3888cdc1fe1e8256e68accee9c70d6e2-300x178-1.jpg 300w, https:\/\/aiforumimage.oss-cn-shanghai.aliyuncs.com\/wp-content\/uploads\/2024\/07\/29670e3888cdc1fe1e8256e68accee9c70d6e2-300x178-1-150x89.jpg 150w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/div>\n<div id=\"postspictures\" class=\"article-content\">\n<div id=\"container\" class=\"container am-engine\" data-v-1d7a5742=\"\" data-element=\"root\">\n<p>\u8ba1\u7b97\u673a\u771f\u7684\u80fd\u7406\u89e3\u4eba\u8138\u5417\uff1f\u4f60\u662f\u5426\u60f3\u8fc7Instagram\u662f\u5982\u4f55\u7ed9\u4f60\u7684\u8138\u4e0a\u5e94\u7528\u60ca\u4eba\u7684\u6ee4\u955c\u7684\uff1f\u8be5\u8f6f\u4ef6\u68c0\u6d4b\u4f60\u8138\u4e0a\u7684\u5173\u952e\u70b9\u5e76\u5728\u5176\u4e0a\u6295\u5f71\u4e00\u4e2a\u906e\u7f69\u3002\u672c\u6559\u7a0b\u5c06\u6559\u4f60\u5982\u4f55\u4f7f\u7528PyTorch\u6784\u5efa\u4e00\u4e2a\u7c7b\u4f3c\u7684\u8f6f\u4ef6\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s7.51cto.com\/oss\/202407\/17\/29670e3888cdc1fe1e8256e68accee9c70d6e2.jpg\" data-type=\"block\" \/><\/p>\n<p>&nbsp;<\/p>\n<h4>\u6570\u636e\u96c6<\/h4>\n<p>\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5b98\u65b9\u7684DLib\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b6666\u5f20\u5c3a\u5bf8\u4e0d\u540c\u7684\u56fe\u50cf\u3002\u6b64\u5916\uff0clabels_ibug_300W_train.xml\uff08\u968f\u6570\u636e\u96c6\u63d0\u4f9b\uff09\u5305\u542b\u6bcf\u5f20\u4eba\u8138\u768468\u4e2a\u5173\u952e\u70b9\u7684\u5750\u6807\u3002\u4e0b\u9762\u7684\u811a\u672c\u5c06\u5728Colab\u7b14\u8bb0\u672c\u4e2d\u4e0b\u8f7d\u6570\u636e\u96c6\u5e76\u89e3\u538b\u7f29\u3002<\/p>\n<div>\n<div class=\"hljs-cto\">\n<div class=\"hljs-cto\"><button class=\"copy_btn disable\" data-clipboard-target=\"#code_id_0\">\u590d\u5236<\/button><\/p>\n<div class=\"code-toolbar\">\n<pre class=\"has-pre-numbering language-javascript\" tabindex=\"0\"><code class=\"language-javascript\"><span class=\"token keyword\">if<\/span> not os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span><span class=\"token function\">exists<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'\/content\/ibug_300W_large_face_landmark_dataset'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n    <span class=\"token operator\">!<\/span>wget http<span class=\"token operator\">:<\/span><span class=\"token operator\">\/<\/span><span class=\"token operator\">\/<\/span>dlib<span class=\"token punctuation\">.<\/span>net<span class=\"token operator\">\/<\/span>files<span class=\"token operator\">\/<\/span>data<span class=\"token operator\">\/<\/span>ibug_300W_large_face_landmark_dataset<span class=\"token punctuation\">.<\/span>tar<span class=\"token punctuation\">.<\/span>gz\r\n    <span class=\"token operator\">!<\/span>tar <span class=\"token operator\">-<\/span>xvzf <span class=\"token string\">'ibug_300W_large_face_landmark_dataset.tar.gz'<\/span>    \r\n    <span class=\"token operator\">!<\/span>rm <span class=\"token operator\">-<\/span>r <span class=\"token string\">'ibug_300W_large_face_landmark_dataset.tar.gz'<\/span><\/code><\/pre>\n<ul id=\"code_id_0\" class=\"pre-numbering\">\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<\/ul>\n<div class=\"toolbar\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u8fd9\u662f\u6570\u636e\u96c6\u4e2d\u7684\u4e00\u5f20\u6837\u672c\u56fe\u50cf\u3002\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u4eba\u8138\u53ea\u5360\u6574\u4e2a\u56fe\u50cf\u7684\u4e00\u5c0f\u90e8\u5206\u3002\u5982\u679c\u6211\u4eec\u5c06\u5b8c\u6574\u56fe\u50cf\u8f93\u5165\u795e\u7ecf\u7f51\u7edc\uff0c\u5b83\u4e5f\u4f1a\u5904\u7406\u80cc\u666f\uff08\u65e0\u5173\u4fe1\u606f\uff09\uff0c\u8fd9\u4f1a\u4f7f\u6a21\u578b\u96be\u4ee5\u5b66\u4e60\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u9700\u8981\u88c1\u526a\u56fe\u50cf\uff0c\u4ec5\u8f93\u5165\u4eba\u8138\u90e8\u5206\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s7.51cto.com\/oss\/202407\/17\/896f61601fa73d471fa803ad59fade6c79e33f.webp\" data-type=\"block\" \/><\/p>\n<p>\u6570\u636e\u96c6\u4e2d\u7684\u6837\u672c\u56fe\u50cf\u548c\u5173\u952e\u70b9<\/p>\n<h4>\u6570\u636e\u9884\u5904\u7406<\/h4>\n<p>\u4e3a\u4e86\u9632\u6b62\u795e\u7ecf\u7f51\u7edc\u8fc7\u62df\u5408\u8bad\u7ec3\u6570\u636e\u96c6\uff0c\u6211\u4eec\u9700\u8981\u968f\u673a\u53d8\u6362\u6570\u636e\u96c6\u3002\u6211\u4eec\u5c06\u5bf9\u8bad\u7ec3\u548c\u9a8c\u8bc1\u6570\u636e\u96c6\u5e94\u7528\u4ee5\u4e0b\u64cd\u4f5c\uff1a<\/p>\n<ul data-id=\"u738a58b-FBpIbwbU\">\n<li data-id=\"ld70c578-x5ehIlBe\">\u7531\u4e8e\u4eba\u8138\u53ea\u5360\u6574\u4e2a\u56fe\u50cf\u7684\u4e00\u5c0f\u90e8\u5206\uff0c\u6240\u4ee5\u88c1\u526a\u56fe\u50cf\u5e76\u4ec5\u4f7f\u7528\u4eba\u8138\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<li data-id=\"ld70c578-ROkVgoD2\">\u5c06\u88c1\u526a\u540e\u7684\u4eba\u8138\u8c03\u6574\u4e3a\uff08224&#215;224\uff09\u7684\u56fe\u50cf\u3002<\/li>\n<li data-id=\"ld70c578-hcHTsT06\">\u968f\u673a\u6539\u53d8\u8c03\u6574\u540e\u7684\u4eba\u8138\u7684\u4eae\u5ea6\u548c\u9971\u548c\u5ea6\u3002<\/li>\n<li data-id=\"ld70c578-fevvbWA2\">\u5728\u4e0a\u8ff0\u4e09\u4e2a\u8f6c\u6362\u4e4b\u540e\uff0c\u968f\u673a\u65cb\u8f6c\u4eba\u8138\u3002<\/li>\n<li data-id=\"ld70c578-wVpi8t0e\">\u5c06\u56fe\u50cf\u548c\u5173\u952e\u70b9\u8f6c\u6362\u4e3atorch\u5f20\u91cf\uff0c\u5e76\u5728[-1, 1]\u4e4b\u95f4\u8fdb\u884c\u5f52\u4e00\u5316\u3002<\/li>\n<\/ul>\n<div>\n<div class=\"hljs-cto\">\n<div class=\"hljs-cto\"><button class=\"copy_btn disable\" data-clipboard-target=\"#code_id_1\">\u590d\u5236<\/button><\/p>\n<div class=\"code-toolbar\">\n<pre class=\"has-pre-numbering language-javascript\" tabindex=\"0\"><code class=\"language-javascript\"><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">Transforms<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n    def <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        pass\r\n    \r\n    def <span class=\"token function\">rotate<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> angle<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        angle <span class=\"token operator\">=<\/span> random<span class=\"token punctuation\">.<\/span><span class=\"token function\">uniform<\/span><span class=\"token punctuation\">(<\/span><span class=\"token operator\">-<\/span>angle<span class=\"token punctuation\">,<\/span> <span class=\"token operator\">+<\/span>angle<span class=\"token punctuation\">)<\/span>\r\n\r\n        transformation_matrix <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">tensor<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>\r\n            <span class=\"token punctuation\">[<\/span><span class=\"token operator\">+<\/span><span class=\"token function\">cos<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">radians<\/span><span class=\"token punctuation\">(<\/span>angle<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token operator\">-<\/span><span class=\"token function\">sin<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">radians<\/span><span class=\"token punctuation\">(<\/span>angle<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> \r\n            <span class=\"token punctuation\">[<\/span><span class=\"token operator\">+<\/span><span class=\"token function\">sin<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">radians<\/span><span class=\"token punctuation\">(<\/span>angle<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token operator\">+<\/span><span class=\"token function\">cos<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">radians<\/span><span class=\"token punctuation\">(<\/span>angle<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">]<\/span>\r\n        <span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\n        image <span class=\"token operator\">=<\/span> imutils<span class=\"token punctuation\">.<\/span><span class=\"token function\">rotate<\/span><span class=\"token punctuation\">(<\/span>np<span class=\"token punctuation\">.<\/span><span class=\"token function\">array<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> angle<span class=\"token punctuation\">)<\/span>\r\n\r\n        landmarks <span class=\"token operator\">=<\/span> landmarks <span class=\"token operator\">-<\/span> <span class=\"token number\">0.5<\/span>\r\n        new_landmarks <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token function\">matmul<\/span><span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">,<\/span> transformation_matrix<span class=\"token punctuation\">)<\/span>\r\n        new_landmarks <span class=\"token operator\">=<\/span> new_landmarks <span class=\"token operator\">+<\/span> <span class=\"token number\">0.5<\/span>\r\n        <span class=\"token keyword\">return<\/span> Image<span class=\"token punctuation\">.<\/span><span class=\"token function\">fromarray<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> new_landmarks\r\n\r\n    def <span class=\"token function\">resize<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> img_size<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">resize<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> img_size<span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> landmarks\r\n\r\n    def <span class=\"token function\">color_jitter<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        color_jitter <span class=\"token operator\">=<\/span> transforms<span class=\"token punctuation\">.<\/span><span class=\"token function\">ColorJitter<\/span><span class=\"token punctuation\">(<\/span>brightness<span class=\"token operator\">=<\/span><span class=\"token number\">0.3<\/span><span class=\"token punctuation\">,<\/span> \r\n                                              contrast<span class=\"token operator\">=<\/span><span class=\"token number\">0.3<\/span><span class=\"token punctuation\">,<\/span>\r\n                                              saturation<span class=\"token operator\">=<\/span><span class=\"token number\">0.3<\/span><span class=\"token punctuation\">,<\/span> \r\n                                              hue<span class=\"token operator\">=<\/span><span class=\"token number\">0.1<\/span><span class=\"token punctuation\">)<\/span>\r\n        image <span class=\"token operator\">=<\/span> <span class=\"token function\">color_jitter<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> landmarks\r\n\r\n    def <span class=\"token function\">crop_face<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> crops<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        left <span class=\"token operator\">=<\/span> <span class=\"token function\">int<\/span><span class=\"token punctuation\">(<\/span>crops<span class=\"token punctuation\">[<\/span><span class=\"token string\">'left'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n        top <span class=\"token operator\">=<\/span> <span class=\"token function\">int<\/span><span class=\"token punctuation\">(<\/span>crops<span class=\"token punctuation\">[<\/span><span class=\"token string\">'top'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n        width <span class=\"token operator\">=<\/span> <span class=\"token function\">int<\/span><span class=\"token punctuation\">(<\/span>crops<span class=\"token punctuation\">[<\/span><span class=\"token string\">'width'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n        height <span class=\"token operator\">=<\/span> <span class=\"token function\">int<\/span><span class=\"token punctuation\">(<\/span>crops<span class=\"token punctuation\">[<\/span><span class=\"token string\">'height'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\n        image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">crop<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> top<span class=\"token punctuation\">,<\/span> left<span class=\"token punctuation\">,<\/span> height<span class=\"token punctuation\">,<\/span> width<span class=\"token punctuation\">)<\/span>\r\n\r\n        img_shape <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token function\">array<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>shape\r\n        landmarks <span class=\"token operator\">=<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">tensor<\/span><span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">-<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">tensor<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">[<\/span>left<span class=\"token punctuation\">,<\/span> top<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n        landmarks <span class=\"token operator\">=<\/span> landmarks <span class=\"token operator\">\/<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">tensor<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>img_shape<span class=\"token punctuation\">[<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> img_shape<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> landmarks\r\n\r\n    def <span class=\"token function\">__call__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> crops<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        image <span class=\"token operator\">=<\/span> Image<span class=\"token punctuation\">.<\/span><span class=\"token function\">fromarray<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span>\r\n        image<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span><span class=\"token function\">crop_face<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> crops<span class=\"token punctuation\">)<\/span>\r\n        image<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span><span class=\"token function\">resize<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">(<\/span><span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n        image<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span><span class=\"token function\">color_jitter<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">)<\/span>\r\n        image<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span><span class=\"token function\">rotate<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> angle<span class=\"token operator\">=<\/span><span class=\"token number\">10<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n        image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">to_tensor<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span>\r\n        image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">normalize<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> landmarks<\/code><\/pre>\n<ul id=\"code_id_1\" class=\"pre-numbering\">\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<li>46.<\/li>\n<li>47.<\/li>\n<li>48.<\/li>\n<li>49.<\/li>\n<li>50.<\/li>\n<li>51.<\/li>\n<li>52.<\/li>\n<li>53.<\/li>\n<li>54.<\/li>\n<\/ul>\n<div class=\"toolbar\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h4>\u6570\u636e\u96c6\u7c7b<\/h4>\n<p>\u73b0\u5728\u6211\u4eec\u5df2\u7ecf\u51c6\u5907\u597d\u4e86\u8f6c\u6362\uff0c\u8ba9\u6211\u4eec\u7f16\u5199\u6211\u4eec\u7684\u6570\u636e\u96c6\u7c7b\u3002labels_ibug_300W_train.xml\u5305\u542b\u56fe\u50cf\u8def\u5f84\u3001\u5173\u952e\u70b9\u548c\u8fb9\u754c\u6846\u7684\u5750\u6807\uff08\u7528\u4e8e\u88c1\u526a\u4eba\u8138\uff09\u3002\u6211\u4eec\u5c06\u8fd9\u4e9b\u503c\u5b58\u50a8\u5728\u5217\u8868\u4e2d\uff0c\u4ee5\u4fbf\u5728\u8bad\u7ec3\u671f\u95f4\u8f7b\u677e\u8bbf\u95ee\u3002\u5728\u672c\u6587\u7ae0\u4e2d\uff0c\u795e\u7ecf\u7f51\u7edc\u5c06\u5728\u7070\u5ea6\u56fe\u50cf\u4e0a\u8fdb\u884c\u8bad\u7ec3\u3002<\/p>\n<div>\n<div class=\"hljs-cto\">\n<div class=\"hljs-cto\"><button class=\"copy_btn disable\" data-clipboard-target=\"#code_id_2\">\u590d\u5236<\/button><\/p>\n<div class=\"code-toolbar\">\n<pre class=\"has-pre-numbering language-javascript\" tabindex=\"0\"><code class=\"language-javascript\">\r\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">FaceLandmarksDataset<\/span><span class=\"token punctuation\">(<\/span>Dataset<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n\r\n    def <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> transform<span class=\"token operator\">=<\/span>None<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n\r\n        tree <span class=\"token operator\">=<\/span> <span class=\"token constant\">ET<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">parse<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'ibug_300W_large_face_landmark_dataset\/labels_ibug_300W_train.xml'<\/span><span class=\"token punctuation\">)<\/span>\r\n        root <span class=\"token operator\">=<\/span> tree<span class=\"token punctuation\">.<\/span><span class=\"token function\">getroot<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\n        self<span class=\"token punctuation\">.<\/span>image_filenames <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>landmarks <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>crops <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>transform <span class=\"token operator\">=<\/span> transform\r\n        self<span class=\"token punctuation\">.<\/span>root_dir <span class=\"token operator\">=<\/span> <span class=\"token string\">'ibug_300W_large_face_landmark_dataset'<\/span>\r\n        \r\n        <span class=\"token keyword\">for<\/span> filename <span class=\"token keyword\">in<\/span> root<span class=\"token punctuation\">[<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">]<\/span><span class=\"token operator\">:<\/span>\r\n            self<span class=\"token punctuation\">.<\/span>image_filenames<span class=\"token punctuation\">.<\/span><span class=\"token function\">append<\/span><span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>path<span class=\"token punctuation\">.<\/span><span class=\"token function\">join<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>root_dir<span class=\"token punctuation\">,<\/span> filename<span class=\"token punctuation\">.<\/span>attrib<span class=\"token punctuation\">[<\/span><span class=\"token string\">'file'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\n            self<span class=\"token punctuation\">.<\/span>crops<span class=\"token punctuation\">.<\/span><span class=\"token function\">append<\/span><span class=\"token punctuation\">(<\/span>filename<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>attrib<span class=\"token punctuation\">)<\/span>\r\n\r\n            landmark <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>\r\n            <span class=\"token keyword\">for<\/span> num <span class=\"token keyword\">in<\/span> <span class=\"token function\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">68<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n                x_coordinate <span class=\"token operator\">=<\/span> <span class=\"token function\">int<\/span><span class=\"token punctuation\">(<\/span>filename<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span>num<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>attrib<span class=\"token punctuation\">[<\/span><span class=\"token string\">'x'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n                y_coordinate <span class=\"token operator\">=<\/span> <span class=\"token function\">int<\/span><span class=\"token punctuation\">(<\/span>filename<span class=\"token punctuation\">[<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">[<\/span>num<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">.<\/span>attrib<span class=\"token punctuation\">[<\/span><span class=\"token string\">'y'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n                landmark<span class=\"token punctuation\">.<\/span><span class=\"token function\">append<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>x_coordinate<span class=\"token punctuation\">,<\/span> y_coordinate<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n            self<span class=\"token punctuation\">.<\/span>landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">append<\/span><span class=\"token punctuation\">(<\/span>landmark<span class=\"token punctuation\">)<\/span>\r\n\r\n        self<span class=\"token punctuation\">.<\/span>landmarks <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token function\">array<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>landmarks<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">astype<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'float32'<\/span><span class=\"token punctuation\">)<\/span>     \r\n\r\n        assert <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>image_filenames<span class=\"token punctuation\">)<\/span> <span class=\"token operator\">==<\/span> <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>landmarks<span class=\"token punctuation\">)<\/span>\r\n\r\n    def <span class=\"token function\">__len__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        <span class=\"token keyword\">return<\/span> <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>image_filenames<span class=\"token punctuation\">)<\/span>\r\n\r\n    def <span class=\"token function\">__getitem__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> index<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        image <span class=\"token operator\">=<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token function\">imread<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>image_filenames<span class=\"token punctuation\">[<\/span>index<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span>\r\n        landmarks <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span>landmarks<span class=\"token punctuation\">[<\/span>index<span class=\"token punctuation\">]<\/span>\r\n        \r\n        <span class=\"token keyword\">if<\/span> self<span class=\"token punctuation\">.<\/span>transform<span class=\"token operator\">:<\/span>\r\n            image<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> self<span class=\"token punctuation\">.<\/span><span class=\"token function\">transform<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">,<\/span> self<span class=\"token punctuation\">.<\/span>crops<span class=\"token punctuation\">[<\/span>index<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\n        landmarks <span class=\"token operator\">=<\/span> landmarks <span class=\"token operator\">-<\/span> <span class=\"token number\">0.5<\/span>\r\n\r\n        <span class=\"token keyword\">return<\/span> image<span class=\"token punctuation\">,<\/span> landmarks\r\n\r\ndataset <span class=\"token operator\">=<\/span> <span class=\"token function\">FaceLandmarksDataset<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">Transforms<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/code><\/pre>\n<ul id=\"code_id_2\" class=\"pre-numbering\">\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<\/ul>\n<div class=\"toolbar\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u6ce8\u610f\uff1alandmarks = landmarks &#8211; 0.5 \u662f\u4e3a\u4e86\u5c06\u5173\u952e\u70b9\u5c45\u4e2d\uff0c\u56e0\u4e3a\u4e2d\u5fc3\u5316\u7684\u8f93\u51fa\u5bf9\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60\u66f4\u5bb9\u6613\u3002\u7ecf\u8fc7\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u96c6\u8f93\u51fa\u5982\u4e0b\u6240\u793a\uff08\u5173\u952e\u70b9\u5df2\u7ecf\u7ed8\u5236\u5728\u56fe\u50cf\u4e2d\uff09\uff1a<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s4.51cto.com\/oss\/202407\/17\/73e1737236fb7d560b659958c64f4d724e7765.webp\" data-type=\"block\" \/><\/p>\n<p>\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u6837\u672c<\/p>\n<h4>\u795e\u7ecf\u7f51\u7edc<\/h4>\n<p>\u6211\u4eec\u5c06\u4f7f\u7528ResNet18\u4f5c\u4e3a\u57fa\u672c\u6846\u67b6\u3002\u6211\u4eec\u9700\u8981\u4fee\u6539\u7b2c\u4e00\u5c42\u548c\u6700\u540e\u4e00\u5c42\u4ee5\u9002\u5e94\u6211\u4eec\u7684\u76ee\u7684\u3002\u5728\u7b2c\u4e00\u5c42\u4e2d\uff0c\u6211\u4eec\u5c06\u8f93\u5165\u901a\u9053\u6570\u8bbe\u4e3a1\uff0c\u4ee5\u4fbf\u795e\u7ecf\u7f51\u7edc\u63a5\u53d7\u7070\u5ea6\u56fe\u50cf\u3002\u540c\u6837\uff0c\u5728\u6700\u540e\u4e00\u5c42\u4e2d\uff0c\u8f93\u51fa\u901a\u9053\u6570\u5e94\u4e3a68 * 2 = 136\uff0c\u4ee5\u4fbf\u6a21\u578b\u9884\u6d4b\u6bcf\u5f20\u4eba\u8138\u768468\u4e2a\u5173\u952e\u70b9\u7684\uff08x\uff0cy\uff09\u5750\u6807\u3002<\/p>\n<div>\n<div class=\"hljs-cto\">\n<div class=\"hljs-cto\"><button class=\"copy_btn disable\" data-clipboard-target=\"#code_id_3\">\u590d\u5236<\/button><\/p>\n<div class=\"code-toolbar\">\n<pre class=\"has-pre-numbering language-javascript\" tabindex=\"0\"><code class=\"language-javascript\"><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">Network<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n    def <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span>num_classes<span class=\"token operator\">=<\/span><span class=\"token number\">136<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        <span class=\"token keyword\">super<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model_name<span class=\"token operator\">=<\/span><span class=\"token string\">'resnet18'<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model<span class=\"token operator\">=<\/span>models<span class=\"token punctuation\">.<\/span><span class=\"token function\">resnet18<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>conv1<span class=\"token operator\">=<\/span>nn<span class=\"token punctuation\">.<\/span><span class=\"token function\">Conv2d<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> kernel_size<span class=\"token operator\">=<\/span><span class=\"token number\">7<\/span><span class=\"token punctuation\">,<\/span> stride<span class=\"token operator\">=<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> bias<span class=\"token operator\">=<\/span>False<span class=\"token punctuation\">)<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>fc<span class=\"token operator\">=<\/span>nn<span class=\"token punctuation\">.<\/span><span class=\"token function\">Linear<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>fc<span class=\"token punctuation\">.<\/span>in_features<span class=\"token punctuation\">,<\/span> num_classes<span class=\"token punctuation\">)<\/span>\r\n        \r\n    def <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        x<span class=\"token operator\">=<\/span>self<span class=\"token punctuation\">.<\/span><span class=\"token function\">model<\/span><span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token keyword\">return<\/span> x<\/code><\/pre>\n<ul id=\"code_id_3\" class=\"pre-numbering\">\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<\/ul>\n<div class=\"toolbar\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h4>\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc<\/h4>\n<p>\u6211\u4eec\u5c06\u4f7f\u7528\u9884\u6d4b\u5173\u952e\u70b9\u548c\u771f\u5b9e\u5173\u952e\u70b9\u4e4b\u95f4\u7684\u5747\u65b9\u8bef\u5dee\u4f5c\u4e3a\u635f\u5931\u51fd\u6570\u3002\u8bf7\u8bb0\u4f4f\uff0c\u8981\u907f\u514d\u68af\u5ea6\u7206\u70b8\uff0c\u5b66\u4e60\u7387\u5e94\u4fdd\u6301\u4f4e\u3002\u6bcf\u5f53\u9a8c\u8bc1\u635f\u5931\u8fbe\u5230\u65b0\u7684\u6700\u5c0f\u503c\u65f6\uff0c\u7f51\u7edc\u6743\u91cd\u5c06\u88ab\u4fdd\u5b58\u3002\u81f3\u5c11\u8bad\u7ec320\u4e2aepochs\u4ee5\u83b7\u5f97\u6700\u4f73\u6027\u80fd\u3002<\/p>\n<div>\n<div class=\"hljs-cto\">\n<div class=\"hljs-cto\"><button class=\"copy_btn disable\" data-clipboard-target=\"#code_id_4\">\u590d\u5236<\/button><\/p>\n<div class=\"code-toolbar\">\n<pre class=\"has-pre-numbering language-javascript\" tabindex=\"0\"><code class=\"language-javascript\">\r\nnetwork <span class=\"token operator\">=<\/span> <span class=\"token function\">Network<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\nnetwork<span class=\"token punctuation\">.<\/span><span class=\"token function\">cuda<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>    \r\n\r\ncriterion <span class=\"token operator\">=<\/span> nn<span class=\"token punctuation\">.<\/span><span class=\"token function\">MSELoss<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\noptimizer <span class=\"token operator\">=<\/span> optim<span class=\"token punctuation\">.<\/span><span class=\"token function\">Adam<\/span><span class=\"token punctuation\">(<\/span>network<span class=\"token punctuation\">.<\/span><span class=\"token function\">parameters<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> lr<span class=\"token operator\">=<\/span><span class=\"token number\">0.0001<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\nloss_min <span class=\"token operator\">=<\/span> np<span class=\"token punctuation\">.<\/span>inf\r\nnum_epochs <span class=\"token operator\">=<\/span> <span class=\"token number\">10<\/span>\r\n\r\nstart_time <span class=\"token operator\">=<\/span> time<span class=\"token punctuation\">.<\/span><span class=\"token function\">time<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n<span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token function\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span>num_epochs<span class=\"token operator\">+<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n    \r\n    loss_train <span class=\"token operator\">=<\/span> <span class=\"token number\">0<\/span>\r\n    loss_valid <span class=\"token operator\">=<\/span> <span class=\"token number\">0<\/span>\r\n    running_loss <span class=\"token operator\">=<\/span> <span class=\"token number\">0<\/span>\r\n    \r\n    network<span class=\"token punctuation\">.<\/span><span class=\"token function\">train<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n    <span class=\"token keyword\">for<\/span> step <span class=\"token keyword\">in<\/span> <span class=\"token function\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span><span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span><span class=\"token operator\">+<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n    \r\n        images<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> <span class=\"token function\">next<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">iter<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n        images <span class=\"token operator\">=<\/span> images<span class=\"token punctuation\">.<\/span><span class=\"token function\">cuda<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        landmarks <span class=\"token operator\">=<\/span> landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">view<\/span><span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">size<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">cuda<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> \r\n        \r\n        predictions <span class=\"token operator\">=<\/span> <span class=\"token function\">network<\/span><span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span>\r\n        \r\n        # clear all the gradients before calculating them\r\n        optimizer<span class=\"token punctuation\">.<\/span><span class=\"token function\">zero_grad<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n        # find the loss <span class=\"token keyword\">for<\/span> the current step\r\n        loss_train_step <span class=\"token operator\">=<\/span> <span class=\"token function\">criterion<\/span><span class=\"token punctuation\">(<\/span>predictions<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">)<\/span>\r\n        \r\n        # calculate the gradients\r\n        loss_train_step<span class=\"token punctuation\">.<\/span><span class=\"token function\">backward<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n        # update the parameters\r\n        optimizer<span class=\"token punctuation\">.<\/span><span class=\"token function\">step<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n        loss_train <span class=\"token operator\">+=<\/span> loss_train_step<span class=\"token punctuation\">.<\/span><span class=\"token function\">item<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        running_loss <span class=\"token operator\">=<\/span> loss_train<span class=\"token operator\">\/<\/span>step\r\n        \r\n        <span class=\"token function\">print_overwrite<\/span><span class=\"token punctuation\">(<\/span>step<span class=\"token punctuation\">,<\/span> <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> running_loss<span class=\"token punctuation\">,<\/span> <span class=\"token string\">'train'<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n    network<span class=\"token punctuation\">.<\/span><span class=\"token function\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> \r\n    <span class=\"token keyword\">with<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">no_grad<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        \r\n        <span class=\"token keyword\">for<\/span> step <span class=\"token keyword\">in<\/span> <span class=\"token function\">range<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span><span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>valid_loader<span class=\"token punctuation\">)<\/span><span class=\"token operator\">+<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n            \r\n            images<span class=\"token punctuation\">,<\/span> landmarks <span class=\"token operator\">=<\/span> <span class=\"token function\">next<\/span><span class=\"token punctuation\">(<\/span><span class=\"token function\">iter<\/span><span class=\"token punctuation\">(<\/span>valid_loader<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n            images <span class=\"token operator\">=<\/span> images<span class=\"token punctuation\">.<\/span><span class=\"token function\">cuda<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n            landmarks <span class=\"token operator\">=<\/span> landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">view<\/span><span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">size<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span><span class=\"token operator\">-<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">cuda<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        \r\n            predictions <span class=\"token operator\">=<\/span> <span class=\"token function\">network<\/span><span class=\"token punctuation\">(<\/span>images<span class=\"token punctuation\">)<\/span>\r\n\r\n            # find the loss <span class=\"token keyword\">for<\/span> the current step\r\n            loss_valid_step <span class=\"token operator\">=<\/span> <span class=\"token function\">criterion<\/span><span class=\"token punctuation\">(<\/span>predictions<span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">)<\/span>\r\n\r\n            loss_valid <span class=\"token operator\">+=<\/span> loss_valid_step<span class=\"token punctuation\">.<\/span><span class=\"token function\">item<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n            running_loss <span class=\"token operator\">=<\/span> loss_valid<span class=\"token operator\">\/<\/span>step\r\n\r\n            <span class=\"token function\">print_overwrite<\/span><span class=\"token punctuation\">(<\/span>step<span class=\"token punctuation\">,<\/span> <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>valid_loader<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> running_loss<span class=\"token punctuation\">,<\/span> <span class=\"token string\">'valid'<\/span><span class=\"token punctuation\">)<\/span>\r\n    \r\n    loss_train <span class=\"token operator\">\/=<\/span> <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span>\r\n    loss_valid <span class=\"token operator\">\/=<\/span> <span class=\"token function\">len<\/span><span class=\"token punctuation\">(<\/span>valid_loader<span class=\"token punctuation\">)<\/span>\r\n    \r\n    <span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'\\n--------------------------------------------------'<\/span><span class=\"token punctuation\">)<\/span>\r\n    <span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'Epoch: {}  Train Loss: {:.4f}  Valid Loss: {:.4f}'<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">format<\/span><span class=\"token punctuation\">(<\/span>epoch<span class=\"token punctuation\">,<\/span> loss_train<span class=\"token punctuation\">,<\/span> loss_valid<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n    <span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'--------------------------------------------------'<\/span><span class=\"token punctuation\">)<\/span>\r\n    \r\n    <span class=\"token keyword\">if<\/span> loss_valid <span class=\"token operator\">&lt;<\/span> loss_min<span class=\"token operator\">:<\/span>\r\n        loss_min <span class=\"token operator\">=<\/span> loss_valid\r\n        torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">save<\/span><span class=\"token punctuation\">(<\/span>network<span class=\"token punctuation\">.<\/span><span class=\"token function\">state_dict<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">'\/content\/face_landmarks.pth'<\/span><span class=\"token punctuation\">)<\/span> \r\n        <span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"\\nMinimum Validation Loss of {:.4f} at epoch {}\/{}\"<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">format<\/span><span class=\"token punctuation\">(<\/span>loss_min<span class=\"token punctuation\">,<\/span> epoch<span class=\"token punctuation\">,<\/span> num_epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'Model Saved\\n'<\/span><span class=\"token punctuation\">)<\/span>\r\n     \r\n<span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'Training Complete'<\/span><span class=\"token punctuation\">)<\/span>\r\n<span class=\"token function\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">\"Total Elapsed Time : {} s\"<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">format<\/span><span class=\"token punctuation\">(<\/span>time<span class=\"token punctuation\">.<\/span><span class=\"token function\">time<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">-<\/span>start_time<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/code><\/pre>\n<ul id=\"code_id_4\" class=\"pre-numbering\">\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<li>46.<\/li>\n<li>47.<\/li>\n<li>48.<\/li>\n<li>49.<\/li>\n<li>50.<\/li>\n<li>51.<\/li>\n<li>52.<\/li>\n<li>53.<\/li>\n<li>54.<\/li>\n<li>55.<\/li>\n<li>56.<\/li>\n<li>57.<\/li>\n<li>58.<\/li>\n<li>59.<\/li>\n<li>60.<\/li>\n<li>61.<\/li>\n<li>62.<\/li>\n<li>63.<\/li>\n<li>64.<\/li>\n<li>65.<\/li>\n<li>66.<\/li>\n<li>67.<\/li>\n<li>68.<\/li>\n<li>69.<\/li>\n<li>70.<\/li>\n<li>71.<\/li>\n<li>72.<\/li>\n<li>73.<\/li>\n<li>74.<\/li>\n<li>75.<\/li>\n<li>76.<\/li>\n<li>77.<\/li>\n<li>78.<\/li>\n<li>79.<\/li>\n<\/ul>\n<div class=\"toolbar\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h4>\u5728\u672a\u77e5\u6570\u636e\u4e0a\u8fdb\u884c\u9884\u6d4b<\/h4>\n<p>\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6bb5\u5728\u672a\u77e5\u56fe\u50cf\u4e2d\u9884\u6d4b\u5173\u952e\u70b9\u3002<\/p>\n<div>\n<div class=\"hljs-cto\">\n<div class=\"hljs-cto\"><button class=\"copy_btn disable\" data-clipboard-target=\"#code_id_5\">\u590d\u5236<\/button><\/p>\n<div class=\"code-toolbar\">\n<pre class=\"has-pre-numbering language-javascript\" tabindex=\"0\"><code class=\"language-javascript\">\r\n<span class=\"token keyword\">import<\/span> time\r\n<span class=\"token keyword\">import<\/span> cv2\r\n<span class=\"token keyword\">import<\/span> os\r\n<span class=\"token keyword\">import<\/span> numpy <span class=\"token keyword\">as<\/span> np\r\n<span class=\"token keyword\">import<\/span> matplotlib<span class=\"token punctuation\">.<\/span>pyplot <span class=\"token keyword\">as<\/span> plt\r\n<span class=\"token keyword\">from<\/span> <span class=\"token constant\">PIL<\/span> <span class=\"token keyword\">import<\/span> Image\r\n<span class=\"token keyword\">import<\/span> imutils\r\n\r\n<span class=\"token keyword\">import<\/span> torch\r\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn\r\nfrom torchvision <span class=\"token keyword\">import<\/span> models\r\n<span class=\"token keyword\">import<\/span> torchvision<span class=\"token punctuation\">.<\/span>transforms<span class=\"token punctuation\">.<\/span>functional <span class=\"token keyword\">as<\/span> <span class=\"token constant\">TF<\/span>\r\n#######################################################################\r\nimage_path <span class=\"token operator\">=<\/span> <span class=\"token string\">'pic.jpg'<\/span>\r\nweights_path <span class=\"token operator\">=<\/span> <span class=\"token string\">'face_landmarks.pth'<\/span>\r\nfrontal_face_cascade_path <span class=\"token operator\">=<\/span> <span class=\"token string\">'haarcascade_frontalface_default.xml'<\/span>\r\n#######################################################################\r\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">Network<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n    def <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span>num_classes<span class=\"token operator\">=<\/span><span class=\"token number\">136<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        <span class=\"token keyword\">super<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model_name<span class=\"token operator\">=<\/span><span class=\"token string\">'resnet18'<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model<span class=\"token operator\">=<\/span>models<span class=\"token punctuation\">.<\/span><span class=\"token function\">resnet18<\/span><span class=\"token punctuation\">(<\/span>pretrained<span class=\"token operator\">=<\/span>False<span class=\"token punctuation\">)<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>conv1<span class=\"token operator\">=<\/span>nn<span class=\"token punctuation\">.<\/span><span class=\"token function\">Conv2d<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> kernel_size<span class=\"token operator\">=<\/span><span class=\"token number\">7<\/span><span class=\"token punctuation\">,<\/span> stride<span class=\"token operator\">=<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span> padding<span class=\"token operator\">=<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> bias<span class=\"token operator\">=<\/span>False<span class=\"token punctuation\">)<\/span>\r\n        self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>fc<span class=\"token operator\">=<\/span>nn<span class=\"token punctuation\">.<\/span><span class=\"token function\">Linear<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">.<\/span>model<span class=\"token punctuation\">.<\/span>fc<span class=\"token punctuation\">.<\/span>in_features<span class=\"token punctuation\">,<\/span>num_classes<span class=\"token punctuation\">)<\/span>\r\n        \r\n    def <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        x<span class=\"token operator\">=<\/span>self<span class=\"token punctuation\">.<\/span><span class=\"token function\">model<\/span><span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span>\r\n        <span class=\"token keyword\">return<\/span> x\r\n\r\n#######################################################################\r\nface_cascade <span class=\"token operator\">=<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token function\">CascadeClassifier<\/span><span class=\"token punctuation\">(<\/span>frontal_face_cascade_path<span class=\"token punctuation\">)<\/span>\r\n\r\nbest_network <span class=\"token operator\">=<\/span> <span class=\"token function\">Network<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\nbest_network<span class=\"token punctuation\">.<\/span><span class=\"token function\">load_state_dict<\/span><span class=\"token punctuation\">(<\/span>torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">load<\/span><span class=\"token punctuation\">(<\/span>weights_path<span class=\"token punctuation\">,<\/span> map_location<span class=\"token operator\">=<\/span>torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">device<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'cpu'<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> \r\nbest_network<span class=\"token punctuation\">.<\/span><span class=\"token function\">eval<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\nimage <span class=\"token operator\">=<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token function\">imread<\/span><span class=\"token punctuation\">(<\/span>image_path<span class=\"token punctuation\">)<\/span>\r\ngrayscale_image <span class=\"token operator\">=<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token function\">cvtColor<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token constant\">COLOR_BGR2GRAY<\/span><span class=\"token punctuation\">)<\/span>\r\ndisplay_image <span class=\"token operator\">=<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token function\">cvtColor<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> cv2<span class=\"token punctuation\">.<\/span><span class=\"token constant\">COLOR_BGR2RGB<\/span><span class=\"token punctuation\">)<\/span>\r\nheight<span class=\"token punctuation\">,<\/span> width<span class=\"token punctuation\">,<\/span>_ <span class=\"token operator\">=<\/span> image<span class=\"token punctuation\">.<\/span>shape\r\n\r\nfaces <span class=\"token operator\">=<\/span> face_cascade<span class=\"token punctuation\">.<\/span><span class=\"token function\">detectMultiScale<\/span><span class=\"token punctuation\">(<\/span>grayscale_image<span class=\"token punctuation\">,<\/span> <span class=\"token number\">1.1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\nall_landmarks <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span>\r\n<span class=\"token keyword\">for<\/span> <span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">,<\/span> w<span class=\"token punctuation\">,<\/span> h<span class=\"token punctuation\">)<\/span> <span class=\"token keyword\">in<\/span> <span class=\"token literal-property property\">faces<\/span><span class=\"token operator\">:<\/span>\r\n    image <span class=\"token operator\">=<\/span> grayscale_image<span class=\"token punctuation\">[<\/span>y<span class=\"token operator\">:<\/span>y<span class=\"token operator\">+<\/span>h<span class=\"token punctuation\">,<\/span> <span class=\"token literal-property property\">x<\/span><span class=\"token operator\">:<\/span>x<span class=\"token operator\">+<\/span>w<span class=\"token punctuation\">]<\/span>\r\n    image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">resize<\/span><span class=\"token punctuation\">(<\/span>Image<span class=\"token punctuation\">.<\/span><span class=\"token function\">fromarray<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> size<span class=\"token operator\">=<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">224<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">224<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n    image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">to_tensor<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">)<\/span>\r\n    image <span class=\"token operator\">=<\/span> <span class=\"token constant\">TF<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">normalize<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0.5<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\n    <span class=\"token keyword\">with<\/span> torch<span class=\"token punctuation\">.<\/span><span class=\"token function\">no_grad<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token operator\">:<\/span>\r\n        landmarks <span class=\"token operator\">=<\/span> <span class=\"token function\">best_network<\/span><span class=\"token punctuation\">(<\/span>image<span class=\"token punctuation\">.<\/span><span class=\"token function\">unsqueeze<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> \r\n\r\n    landmarks <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">view<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">68<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">detach<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span><span class=\"token function\">numpy<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">+<\/span> <span class=\"token number\">0.5<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">*<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token function\">array<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">[<\/span>w<span class=\"token punctuation\">,<\/span> h<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span> <span class=\"token operator\">+<\/span> np<span class=\"token punctuation\">.<\/span><span class=\"token function\">array<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">[<\/span>x<span class=\"token punctuation\">,<\/span> y<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span>\r\n    all_landmarks<span class=\"token punctuation\">.<\/span><span class=\"token function\">append<\/span><span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">)<\/span>\r\n\r\nplt<span class=\"token punctuation\">.<\/span><span class=\"token function\">figure<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\r\nplt<span class=\"token punctuation\">.<\/span><span class=\"token function\">imshow<\/span><span class=\"token punctuation\">(<\/span>display_image<span class=\"token punctuation\">)<\/span>\r\n<span class=\"token keyword\">for<\/span> landmarks <span class=\"token keyword\">in<\/span> <span class=\"token literal-property property\">all_landmarks<\/span><span class=\"token operator\">:<\/span>\r\n    plt<span class=\"token punctuation\">.<\/span><span class=\"token function\">scatter<\/span><span class=\"token punctuation\">(<\/span>landmarks<span class=\"token punctuation\">[<\/span><span class=\"token operator\">:<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">0<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> landmarks<span class=\"token punctuation\">[<\/span><span class=\"token operator\">:<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> c <span class=\"token operator\">=<\/span> <span class=\"token string\">'c'<\/span><span class=\"token punctuation\">,<\/span> s <span class=\"token operator\">=<\/span> <span class=\"token number\">5<\/span><span class=\"token punctuation\">)<\/span>\r\n\r\nplt<span class=\"token punctuation\">.<\/span><span class=\"token function\">show<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/code><\/pre>\n<ul id=\"code_id_5\" class=\"pre-numbering\">\n<li>1.<\/li>\n<li>2.<\/li>\n<li>3.<\/li>\n<li>4.<\/li>\n<li>5.<\/li>\n<li>6.<\/li>\n<li>7.<\/li>\n<li>8.<\/li>\n<li>9.<\/li>\n<li>10.<\/li>\n<li>11.<\/li>\n<li>12.<\/li>\n<li>13.<\/li>\n<li>14.<\/li>\n<li>15.<\/li>\n<li>16.<\/li>\n<li>17.<\/li>\n<li>18.<\/li>\n<li>19.<\/li>\n<li>20.<\/li>\n<li>21.<\/li>\n<li>22.<\/li>\n<li>23.<\/li>\n<li>24.<\/li>\n<li>25.<\/li>\n<li>26.<\/li>\n<li>27.<\/li>\n<li>28.<\/li>\n<li>29.<\/li>\n<li>30.<\/li>\n<li>31.<\/li>\n<li>32.<\/li>\n<li>33.<\/li>\n<li>34.<\/li>\n<li>35.<\/li>\n<li>36.<\/li>\n<li>37.<\/li>\n<li>38.<\/li>\n<li>39.<\/li>\n<li>40.<\/li>\n<li>41.<\/li>\n<li>42.<\/li>\n<li>43.<\/li>\n<li>44.<\/li>\n<li>45.<\/li>\n<li>46.<\/li>\n<li>47.<\/li>\n<li>48.<\/li>\n<li>49.<\/li>\n<li>50.<\/li>\n<li>51.<\/li>\n<li>52.<\/li>\n<li>53.<\/li>\n<li>54.<\/li>\n<li>55.<\/li>\n<li>56.<\/li>\n<li>57.<\/li>\n<li>58.<\/li>\n<li>59.<\/li>\n<li>60.<\/li>\n<li>61.<\/li>\n<li>62.<\/li>\n<li>63.<\/li>\n<\/ul>\n<div class=\"toolbar\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>OpenCV Haar\u7ea7\u8054\u5206\u7c7b\u5668\u7528\u4e8e\u68c0\u6d4b\u56fe\u50cf\u4e2d\u7684\u4eba\u8138\u3002\u4f7f\u7528Haar\u7ea7\u8054\u8fdb\u884c\u5bf9\u8c61\u68c0\u6d4b\u662f\u4e00\u79cd\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5\uff0c\u5176\u4e2d\u4f7f\u7528\u4e00\u7ec4\u8f93\u5165\u6570\u636e\u5bf9\u7ea7\u8054\u51fd\u6570\u8fdb\u884c\u8bad\u7ec3\u3002OpenCV\u5df2\u7ecf\u5305\u542b\u4e86\u8bb8\u591a\u9884\u8bad\u7ec3\u7684\u5206\u7c7b\u5668\uff0c\u7528\u4e8e\u4eba\u8138\u3001\u773c\u775b\u3001\u884c\u4eba\u7b49\u7b49\u3002\u5728\u6211\u4eec\u7684\u6848\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u4eba\u8138\u5206\u7c7b\u5668\uff0c\u4f60\u9700\u8981\u4e0b\u8f7d\u9884\u8bad\u7ec3\u7684\u5206\u7c7b\u5668XML\u6587\u4ef6\u5e76\u5c06\u5176\u4fdd\u5b58\u5230\u4f60\u7684\u5de5\u4f5c\u76ee\u5f55\u4e2d\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s8.51cto.com\/oss\/202407\/17\/b6e2a64482d037c5561329858401e8d939a840.webp\" data-type=\"block\" \/><\/p>\n<p>\u4eba\u8138\u68c0\u6d4b<\/p>\n<p>\u5728\u8f93\u5165\u56fe\u50cf\u4e2d\u68c0\u6d4b\u5230\u7684\u4eba\u8138\u5c06\u88ab\u88c1\u526a\u3001\u8c03\u6574\u5927\u5c0f\u4e3a\uff08224\uff0c224\uff09\u5e76\u8f93\u5165\u5230\u6211\u4eec\u8bad\u7ec3\u597d\u7684\u795e\u7ecf\u7f51\u7edc\u4e2d\u4ee5\u9884\u6d4b\u5176\u4e2d\u7684\u5173\u952e\u70b9\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s8.51cto.com\/oss\/202407\/17\/24267d734cd694fef1b7734e2740ebc76249dc.webp\" data-type=\"block\" \/><\/p>\n<p>\u88c1\u526a\u4eba\u8138\u4e0a\u7684\u5173\u952e\u70b9<\/p>\n<p>\u5728\u88c1\u526a\u7684\u4eba\u8138\u4e0a\u53e0\u52a0\u9884\u6d4b\u7684\u5173\u952e\u70b9\u3002\u7ed3\u679c\u5982\u4e0b\u56fe\u6240\u793a\u3002\u76f8\u5f53\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\uff0c\u4e0d\u662f\u5417\uff1f<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s3.51cto.com\/oss\/202407\/17\/28a623c0567b49feca4099a4b92f528d899972.webp\" data-type=\"block\" \/><\/p>\n<p>\u6700\u7ec8\u7ed3\u679c<\/p>\n<p>\u540c\u6837\uff0c\u5728\u591a\u4e2a\u4eba\u8138\u4e0a\u8fdb\u884c\u5173\u952e\u70b9\u68c0\u6d4b\uff1a<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/s6.51cto.com\/oss\/202407\/17\/87e272c44ccd4879bad3421fc4bdf96d747ebb.webp\" data-type=\"block\" \/><\/p>\n<p>&nbsp;<\/p>\n<p>\u5728\u8fd9\u91cc\uff0c\u4f60\u53ef\u4ee5\u770b\u5230OpenCV Haar\u7ea7\u8054\u5206\u7c7b\u5668\u5df2\u7ecf\u68c0\u6d4b\u5230\u4e86\u591a\u4e2a\u4eba\u8138\uff0c\u5305\u62ec\u4e00\u4e2a\u8bef\u62a5\uff08\u4e00\u4e2a\u62f3\u5934\u88ab\u9884\u6d4b\u4e3a\u4eba\u8138\uff09\u3002<\/p>\n<\/div>\n<\/div>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_21221\" class=\"pvc_stats total_only  \" data-element-id=\"21221\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" version=\"1.0\" viewBox=\"0 0 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