{"id":86356,"date":"2026-07-28T14:08:51","date_gmt":"2026-07-28T06:08:51","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/86356.html"},"modified":"2026-07-28T14:08:51","modified_gmt":"2026-07-28T06:08:51","slug":"cv_resnet50_face-reconstruction%e9%95%9c%e5%83%8f%e5%85%8d%e9%85%8d%e7%bd%ae%e5%ae%9e%e6%93%8d%ef%bc%9aconda-env%e5%af%bc%e5%87%ba-%e5%af%bc%e5%85%a5%e4%b8%8e%e8%b7%a8%e6%9c%8d%e5%8a%a1%e5%99%a8","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/86356.html","title":{"rendered":"cv_resnet50_face-reconstruction\u955c\u50cf\u514d\u914d\u7f6e\u5b9e\u64cd\uff1aconda env\u5bfc\u51fa\/\u5bfc\u5165\u4e0e\u8de8\u670d\u52a1\u5668\u8fc1\u79fb\u65b9\u6848"},"content":{"rendered":"<h2>cv_resnet50_face-reconstruction\u955c\u50cf\u514d\u914d\u7f6e\u5b9e\u64cd&#xff1a;conda env\u5bfc\u51fa\/\u5bfc\u5165\u4e0e\u8de8\u670d\u52a1\u5668\u8fc1\u79fb\u65b9\u6848<\/h2>\n<p>\u4f60\u662f\u4e0d\u662f\u4e5f\u9047\u5230\u8fc7\u8fd9\u79cd\u60c5\u51b5&#xff1a;\u597d\u4e0d\u5bb9\u6613\u5728\u4e00\u53f0\u670d\u52a1\u5668\u4e0a\u914d\u597d\u4e86\u6df1\u5ea6\u5b66\u4e60\u73af\u5883&#xff0c;\u8dd1\u901a\u4e86\u9879\u76ee&#xff0c;\u7ed3\u679c\u6362\u53f0\u673a\u5668\u6216\u8005\u60f3\u5206\u4eab\u7ed9\u540c\u4e8b\u65f6&#xff0c;\u53c8\u5f97\u4ece\u5934\u518d\u6765\u4e00\u904d&#xff1f;\u5b89\u88c5\u4f9d\u8d56\u3001\u89e3\u51b3\u7248\u672c\u51b2\u7a81\u3001\u5904\u7406\u7f51\u7edc\u95ee\u9898\u2026\u2026\u5149\u662f\u60f3\u60f3\u5c31\u5934\u75bc\u3002<\/p>\n<p>\u4eca\u5929\u6211\u8981\u5206\u4eab\u7684&#xff0c;\u5c31\u662f\u4e00\u4e2a\u80fd\u8ba9\u4f60\u5f7b\u5e95\u544a\u522b\u8fd9\u79cd\u70e6\u607c\u7684\u5b9e\u6218\u65b9\u6848\u3002\u6211\u4eec\u4ee5cv_resnet50_face-reconstruction\u8fd9\u4e2a\u4eba\u8138\u91cd\u5efa\u9879\u76ee\u4e3a\u4f8b&#xff0c;\u624b\u628a\u624b\u6559\u4f60\u5982\u4f55\u628a\u6574\u4e2aconda\u865a\u62df\u73af\u5883\u6253\u5305\u5e26\u8d70&#xff0c;\u5b9e\u73b0\u771f\u6b63\u7684\u201c\u4e00\u6b21\u914d\u7f6e&#xff0c;\u5904\u5904\u8fd0\u884c\u201d\u3002<\/p>\n<h3>1. \u9879\u76ee\u7b80\u4ecb\u4e0e\u73af\u5883\u73b0\u72b6<\/h3>\n<p>cv_resnet50_face-reconstruction\u662f\u4e00\u4e2a\u57fa\u4e8eResNet50\u5b9e\u73b0\u4eba\u8138\u91cd\u5efa\u7684\u5b9e\u7528\u9879\u76ee\u3002\u5b83\u7684\u6700\u5927\u4f18\u70b9\u5c31\u662f\u5f00\u7bb1\u5373\u7528\u2014\u2014\u9879\u76ee\u56e2\u961f\u5df2\u7ecf\u8d34\u5fc3\u5730\u79fb\u9664\u4e86\u6240\u6709\u6d77\u5916\u4f9d\u8d56&#xff0c;\u9002\u914d\u4e86\u56fd\u5185\u7f51\u7edc\u73af\u5883&#xff0c;\u4f60\u4e0d\u9700\u8981\u7ffb\u5899\u4e0b\u8f7d\u4efb\u4f55\u6a21\u578b&#xff0c;\u4e5f\u4e0d\u9700\u8981\u6298\u817e\u590d\u6742\u7684\u914d\u7f6e\u3002<\/p>\n<h4>1.1 \u5f53\u524d\u73af\u5883\u5feb\u901f\u56de\u987e<\/h4>\n<p>\u6309\u7167\u9879\u76ee\u8bf4\u660e&#xff0c;\u4f60\u9700\u8981\u5728torch27\u8fd9\u4e2aconda\u865a\u62df\u73af\u5883\u4e2d\u5b89\u88c5\u4ee5\u4e0b\u6838\u5fc3\u4f9d\u8d56&#xff1a;<\/p>\n<p>pip install torch&#061;&#061;2.5.0 torchvision&#061;&#061;0.20.0 opencv-python&#061;&#061;4.9.0.80 modelscope<\/p>\n<p>\u5b89\u88c5\u5b8c\u6210\u540e&#xff0c;\u53ea\u9700\u8981\u4e09\u6b65\u5c31\u80fd\u8fd0\u884c\u4eba\u8138\u91cd\u5efa&#xff1a;<\/p>\n<p># 1. \u6fc0\u6d3b\u73af\u5883<br \/>\nsource activate torch27<\/p>\n<p># 2. \u8fdb\u5165\u9879\u76ee\u76ee\u5f55<br \/>\ncd cv_resnet50_face-reconstruction<\/p>\n<p># 3. \u8fd0\u884c\u811a\u672c<br \/>\npython test.py<\/p>\n<p>\u9879\u76ee\u4f1a\u8bfb\u53d6\u6839\u76ee\u5f55\u4e0b\u7684test_face.jpg&#xff08;\u9700\u8981\u4f60\u63d0\u524d\u51c6\u5907\u597d\u4e00\u5f20\u6e05\u6670\u7684\u4eba\u8138\u7167\u7247&#xff09;&#xff0c;\u7136\u540e\u751f\u6210\u91cd\u5efa\u540e\u7684reconstructed_face.jpg\u3002<\/p>\n<p>\u6574\u4e2a\u8fc7\u7a0b\u770b\u4f3c\u7b80\u5355&#xff0c;\u4f46\u95ee\u9898\u6765\u4e86&#xff1a;\u5982\u679c\u4f60\u8981\u5728\u53e6\u4e00\u53f0\u670d\u52a1\u5668\u4e0a\u8fd0\u884c&#xff0c;\u6216\u8005\u60f3\u628a\u8fd9\u4e2a\u73af\u5883\u5206\u4eab\u7ed9\u56e2\u961f\u5176\u4ed6\u6210\u5458&#xff0c;\u96be\u9053\u8981\u8ba9\u4ed6\u4eec\u4e5f\u91cd\u590d\u4e00\u904d\u6240\u6709\u7684\u5b89\u88c5\u6b65\u9aa4\u5417&#xff1f;<\/p>\n<h3>2. \u4e3a\u4ec0\u4e48\u9700\u8981\u73af\u5883\u8fc1\u79fb&#xff1f;<\/h3>\n<p>\u5728\u6df1\u5165\u6280\u672f\u7ec6\u8282\u4e4b\u524d&#xff0c;\u6211\u4eec\u5148\u804a\u804a\u73af\u5883\u8fc1\u79fb\u7684\u5b9e\u9645\u4ef7\u503c\u3002\u4f60\u53ef\u80fd\u89c9\u5f97\u201c\u4e0d\u5c31\u662f\u51e0\u4e2apip install\u547d\u4ee4\u5417&#xff0c;\u590d\u5236\u7c98\u8d34\u4e00\u4e0b\u4e0d\u5c31\u884c\u4e86&#xff1f;\u201d\u4f46\u73b0\u5b9e\u5f80\u5f80\u6bd4\u60f3\u8c61\u4e2d\u590d\u6742\u3002<\/p>\n<h4>2.1 \u73af\u5883\u590d\u73b0\u7684\u4e09\u5927\u75db\u70b9<\/h4>\n<p>\u4f9d\u8d56\u7248\u672c\u51b2\u7a81\u662f\u6700\u5e38\u89c1\u7684\u95ee\u9898\u3002\u6bd4\u5982\u4f60\u7684\u9879\u76ee\u9700\u8981torch&#061;&#061;2.5.0&#xff0c;\u4f46\u65b0\u673a\u5668\u4e0a\u5df2\u7ecf\u88c5\u4e86torch&#061;&#061;2.3.0&#xff0c;\u76f4\u63a5\u5b89\u88c5\u4f1a\u5bfc\u81f4\u7248\u672c\u51b2\u7a81\u3002\u66f4\u9ebb\u70e6\u7684\u662f&#xff0c;\u6709\u4e9b\u4f9d\u8d56\u5305\u6709\u590d\u6742\u7684\u4f9d\u8d56\u6811&#xff0c;A\u5305\u9700\u8981B\u5305\u76841.0\u7248\u672c&#xff0c;C\u5305\u5374\u9700\u8981B\u5305\u76842.0\u7248\u672c&#xff0c;\u624b\u52a8\u89e3\u51b3\u8fd9\u4e9b\u51b2\u7a81\u80fd\u8ba9\u4eba\u5d29\u6e83\u3002<\/p>\n<p>\u7f51\u7edc\u73af\u5883\u9650\u5236\u662f\u53e6\u4e00\u4e2a\u5927\u95ee\u9898\u3002\u867d\u7136cv_resnet50_face-reconstruction\u9879\u76ee\u672c\u8eab\u79fb\u9664\u4e86\u6d77\u5916\u4f9d\u8d56&#xff0c;\u4f46\u5982\u679c\u4f60\u5728\u5176\u4ed6\u9879\u76ee\u4e2d\u7528\u4e86\u9700\u8981\u4ecePyPI\u6216GitHub\u4e0b\u8f7d\u7684\u5305&#xff0c;\u56fd\u5185\u7f51\u7edc\u8bbf\u95ee\u6162\u751a\u81f3\u65e0\u6cd5\u8bbf\u95ee\u7684\u60c5\u51b5\u65f6\u6709\u53d1\u751f\u3002<\/p>\n<p>\u7cfb\u7edf\u5dee\u5f02\u4e5f\u4e0d\u5bb9\u5ffd\u89c6\u3002Linux\u548cWindows\u4e0b\u7684\u73af\u5883\u914d\u7f6e\u5b8c\u5168\u4e0d\u540c&#xff0c;\u751a\u81f3\u4e0d\u540cLinux\u53d1\u884c\u7248\u4e4b\u95f4\u4e5f\u53ef\u80fd\u5b58\u5728\u5e93\u6587\u4ef6\u5dee\u5f02\u3002\u4f60\u5728Ubuntu\u4e0a\u914d\u597d\u7684\u73af\u5883&#xff0c;\u5230\u4e86CentOS\u4e0a\u53ef\u80fd\u5c31\u8dd1\u4e0d\u8d77\u6765\u4e86\u3002<\/p>\n<h4>2.2 Conda\u73af\u5883\u8fc1\u79fb\u7684\u4f18\u52bf<\/h4>\n<p>Conda\u73af\u5883\u8fc1\u79fb\u65b9\u6848\u80fd\u5b8c\u7f8e\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898&#xff1a;<\/p>\n<li>\u5b8c\u6574\u6027&#xff1a;\u6253\u5305\u7684\u662f\u6574\u4e2a\u73af\u5883&#xff0c;\u5305\u62ecPython\u89e3\u91ca\u5668\u3001\u6240\u6709\u4f9d\u8d56\u5305\u53ca\u5176\u7279\u5b9a\u7248\u672c<\/li>\n<li>\u9694\u79bb\u6027&#xff1a;\u73af\u5883\u72ec\u7acb\u4e8e\u7cfb\u7edfPython&#xff0c;\u4e0d\u4f1a\u5f71\u54cd\u5176\u4ed6\u9879\u76ee<\/li>\n<li>\u53ef\u79fb\u690d\u6027&#xff1a;\u4e00\u6b21\u5bfc\u51fa&#xff0c;\u53ef\u4ee5\u5728\u4efb\u4f55\u652f\u6301conda\u7684\u673a\u5668\u4e0a\u6062\u590d<\/li>\n<li>\u6548\u7387&#xff1a;\u7701\u53bb\u4e86\u91cd\u590d\u4e0b\u8f7d\u3001\u7f16\u8bd1\u3001\u5b89\u88c5\u7684\u65f6\u95f4<\/li>\n<p>\u63a5\u4e0b\u6765&#xff0c;\u6211\u5c31\u5e26\u4f60\u4e00\u6b65\u6b65\u5b9e\u73b0\u8fd9\u4e2a\u201c\u6253\u5305\u5e26\u8d70\u201d\u7684\u9b54\u6cd5\u3002<\/p>\n<h3>3. \u73af\u5883\u5bfc\u51fa&#xff1a;\u5b8c\u6574\u6253\u5305\u4f60\u7684\u5de5\u4f5c\u73af\u5883<\/h3>\n<p>\u73af\u5883\u5bfc\u51fa\u7684\u6838\u5fc3\u601d\u60f3\u5f88\u7b80\u5355&#xff1a;\u628a\u5f53\u524d\u73af\u5883\u4e2d\u7684\u6240\u6709\u5305\u4fe1\u606f\u8bb0\u5f55\u4e0b\u6765&#xff0c;\u7136\u540e\u6839\u636e\u8fd9\u4e9b\u4fe1\u606f\u5728\u65b0\u73af\u5883\u4e2d\u91cd\u65b0\u5b89\u88c5\u3002\u4f46\u6211\u4eec\u8981\u505a\u7684\u6bd4\u8fd9\u66f4\u5f7b\u5e95\u2014\u2014\u76f4\u63a5\u6253\u5305\u6574\u4e2a\u73af\u5883&#xff0c;\u5305\u62ecPython\u89e3\u91ca\u5668\u672c\u8eab\u3002<\/p>\n<h4>3.1 \u65b9\u6cd5\u4e00&#xff1a;\u5bfc\u51fa\u73af\u5883\u914d\u7f6e\u6587\u4ef6&#xff08;\u63a8\u8350&#xff09;<\/h4>\n<p>\u8fd9\u662f\u6700\u8f7b\u91cf\u3001\u6700\u901a\u7528\u7684\u65b9\u6cd5\u3002\u5b83\u4e0d\u6253\u5305\u5b9e\u9645\u7684\u5305\u6587\u4ef6&#xff0c;\u800c\u662f\u751f\u6210\u4e00\u4e2a\u5305\u542b\u6240\u6709\u5305\u540d\u548c\u7248\u672c\u53f7\u7684\u914d\u7f6e\u6587\u4ef6\u3002<\/p>\n<p>\u9996\u5148&#xff0c;\u786e\u4fdd\u4f60\u5df2\u7ecf\u6fc0\u6d3b\u4e86torch27\u73af\u5883&#xff1a;<\/p>\n<p>source activate torch27<\/p>\n<p>\u7136\u540e\u4f7f\u7528conda\u547d\u4ee4\u5bfc\u51fa\u73af\u5883\u914d\u7f6e&#xff1a;<\/p>\n<p>conda env export &#8211;name torch27 &gt; torch27_environment.yaml<\/p>\n<p>\u8fd9\u6761\u547d\u4ee4\u4f1a\u751f\u6210\u4e00\u4e2atorch27_environment.yaml\u6587\u4ef6&#xff0c;\u5185\u5bb9\u5927\u81f4\u5982\u4e0b&#xff1a;<\/p>\n<p>name: torch27<br \/>\nchannels:<br \/>\n  &#8211; defaults<br \/>\n  &#8211; conda-forge<br \/>\ndependencies:<br \/>\n  &#8211; python&#061;3.9.0<br \/>\n  &#8211; pip&#061;23.0.0<br \/>\n  &#8211; pytorch&#061;2.5.0<br \/>\n  &#8211; torchvision&#061;0.20.0<br \/>\n  &#8211; opencv&#061;4.9.0<br \/>\n  &#8211; pip:<br \/>\n    &#8211; modelscope&#061;&#061;1.0.0<br \/>\n    &#8211; \u5176\u4ed6\u901a\u8fc7pip\u5b89\u88c5\u7684\u5305&#8230;<\/p>\n<p>\u8fd9\u4e2a\u65b9\u6cd5\u7684\u4f18\u70b9&#xff1a;<\/p>\n<ul>\n<li>\u6587\u4ef6\u5f88\u5c0f&#xff0c;\u901a\u5e38\u53ea\u6709\u51e0KB<\/li>\n<li>\u53ef\u4ee5\u8de8\u5e73\u53f0\u4f7f\u7528&#xff08;Linux\u3001Windows\u3001macOS&#xff09;<\/li>\n<li>\u5141\u8bb8conda\u5728\u6062\u590d\u65f6\u6839\u636e\u65b0\u673a\u5668\u7684\u7cfb\u7edf\u9009\u62e9\u6700\u4f18\u7684\u5305\u7248\u672c<\/li>\n<\/ul>\n<p>\u9700\u8981\u6ce8\u610f\u7684\u5730\u65b9&#xff1a;<\/p>\n<ul>\n<li>\u6062\u590d\u65f6\u9700\u8981\u8054\u7f51\u4e0b\u8f7d\u6240\u6709\u5305<\/li>\n<li>\u5982\u679c\u67d0\u4e9b\u5305\u5728\u65b0\u5e73\u53f0\u7684conda\u9891\u9053\u4e2d\u4e0d\u53ef\u7528&#xff0c;\u53ef\u80fd\u9700\u8981\u624b\u52a8\u5904\u7406<\/li>\n<\/ul>\n<h4>3.2 \u65b9\u6cd5\u4e8c&#xff1a;\u514b\u9686\u6574\u4e2a\u73af\u5883\u76ee\u5f55&#xff08;\u5feb\u901f\u4f46\u5360\u7528\u7a7a\u95f4&#xff09;<\/h4>\n<p>\u5982\u679c\u4f60\u9700\u8981\u5728\u5b8c\u5168\u76f8\u540c\u7684\u64cd\u4f5c\u7cfb\u7edf\u548c\u67b6\u6784\u7684\u673a\u5668\u95f4\u8fc1\u79fb&#xff0c;\u6216\u8005\u6ca1\u6709\u7f51\u7edc\u73af\u5883&#xff0c;\u53ef\u4ee5\u8003\u8651\u76f4\u63a5\u590d\u5236\u6574\u4e2a\u73af\u5883\u76ee\u5f55\u3002<\/p>\n<p>\u9996\u5148\u627e\u5230\u4f60\u7684conda\u73af\u5883\u6240\u5728\u8def\u5f84&#xff1a;<\/p>\n<p>conda info &#8211;envs<\/p>\n<p>\u4f60\u4f1a\u770b\u5230\u7c7b\u4f3c\u8fd9\u6837\u7684\u8f93\u51fa&#xff1a;<\/p>\n<p># conda environments:<br \/>\n#<br \/>\nbase                  *  \/home\/user\/miniconda3<br \/>\ntorch27                  \/home\/user\/miniconda3\/envs\/torch27<\/p>\n<p>torch27\u73af\u5883\u5c31\u5728\/home\/user\/miniconda3\/envs\/torch27\u8fd9\u4e2a\u76ee\u5f55\u4e0b\u3002\u4f60\u53ef\u4ee5\u76f4\u63a5\u6253\u5305\u8fd9\u4e2a\u76ee\u5f55&#xff1a;<\/p>\n<p># \u6253\u5305\u73af\u5883\u76ee\u5f55<br \/>\ntar -czvf torch27_env.tar.gz -C \/home\/user\/miniconda3\/envs torch27<\/p>\n<p># \u5982\u679c\u4f60\u5728\u73af\u5883\u76ee\u5f55\u5185&#xff0c;\u4e5f\u53ef\u4ee5\u8fd9\u6837<br \/>\ncd \/home\/user\/miniconda3\/envs<br \/>\ntar -czvf torch27_env.tar.gz torch27<\/p>\n<p>\u8fd9\u4e2a\u65b9\u6cd5\u7684\u4f18\u70b9&#xff1a;<\/p>\n<ul>\n<li>\u6062\u590d\u901f\u5ea6\u6781\u5feb&#xff0c;\u4e0d\u9700\u8981\u91cd\u65b0\u4e0b\u8f7d\u548c\u5b89\u88c5<\/li>\n<li>\u5b8c\u5168\u79bb\u7ebf\u53ef\u7528<\/li>\n<li>\u4fdd\u7559\u4e86\u6240\u6709\u4e8c\u8fdb\u5236\u6587\u4ef6\u548c\u7f16\u8bd1\u7ed3\u679c<\/li>\n<\/ul>\n<p>\u7f3a\u70b9\u4e5f\u5f88\u660e\u663e&#xff1a;<\/p>\n<ul>\n<li>\u6253\u5305\u6587\u4ef6\u5f88\u5927&#xff08;\u901a\u5e38\u51e0\u767eMB\u5230\u51e0GB&#xff09;<\/li>\n<li>\u53ea\u80fd\u7528\u4e8e\u76f8\u540c\u64cd\u4f5c\u7cfb\u7edf\u548c\u67b6\u6784&#xff08;Linux\u5230Linux&#xff0c;\u4e14\u6700\u597d\u662f\u76f8\u540c\u53d1\u884c\u7248&#xff09;<\/li>\n<li>\u53ef\u80fd\u5305\u542b\u7edd\u5bf9\u8def\u5f84&#xff0c;\u9700\u8981\u5728\u65b0\u673a\u5668\u4e0a\u89e3\u538b\u5230\u76f8\u540c\u8def\u5f84<\/li>\n<\/ul>\n<h4>3.3 \u65b9\u6cd5\u4e09&#xff1a;\u4f7f\u7528conda-pack&#xff08;\u5e73\u8861\u65b9\u6848&#xff09;<\/h4>\n<p>conda-pack\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u6253\u5305conda\u73af\u5883\u7684\u5de5\u5177&#xff0c;\u5b83\u7ed3\u5408\u4e86\u524d\u4e24\u79cd\u65b9\u6cd5\u7684\u4f18\u70b9\u3002<\/p>\n<p>\u9996\u5148\u5b89\u88c5conda-pack&#xff1a;<\/p>\n<p>conda install -c conda-forge conda-pack<\/p>\n<p>\u7136\u540e\u6253\u5305\u4f60\u7684\u73af\u5883&#xff1a;<\/p>\n<p># \u6253\u5305\u5f53\u524d\u6fc0\u6d3b\u7684\u73af\u5883<br \/>\nconda pack -n torch27 -o torch27_env.tar.gz<\/p>\n<p># \u6216\u8005\u5148\u6fc0\u6d3b\u73af\u5883\u518d\u6253\u5305<br \/>\nsource activate torch27<br \/>\nconda pack -o torch27_env.tar.gz<\/p>\n<p>\u6253\u5305\u5b8c\u6210\u540e&#xff0c;\u4f60\u4f1a\u5f97\u5230\u4e00\u4e2atorch27_env.tar.gz\u6587\u4ef6&#xff0c;\u8fd9\u4e2a\u6587\u4ef6\u6bd4\u76f4\u63a5\u6253\u5305\u76ee\u5f55\u8981\u5c0f&#xff0c;\u4f46\u6bd4YAML\u6587\u4ef6\u5927\u3002<\/p>\n<h3>4. \u73af\u5883\u5bfc\u5165&#xff1a;\u5728\u65b0\u673a\u5668\u4e0a\u5feb\u901f\u6062\u590d<\/h3>\n<p>\u73b0\u5728\u6211\u4eec\u5df2\u7ecf\u6709\u4e86\u6253\u5305\u597d\u7684\u73af\u5883&#xff0c;\u63a5\u4e0b\u6765\u770b\u770b\u5982\u4f55\u5728\u65b0\u673a\u5668\u4e0a\u6062\u590d\u5b83\u3002<\/p>\n<h4>4.1 \u4eceYAML\u6587\u4ef6\u6062\u590d&#xff08;\u5bf9\u5e94\u65b9\u6cd5\u4e00&#xff09;<\/h4>\n<p>\u5728\u65b0\u673a\u5668\u4e0a\u5b89\u88c5conda&#xff08;\u5982\u679c\u8fd8\u6ca1\u5b89\u88c5\u7684\u8bdd&#xff09;&#xff0c;\u7136\u540e&#xff1a;<\/p>\n<p># \u5c06YAML\u6587\u4ef6\u590d\u5236\u5230\u65b0\u673a\u5668<br \/>\n# \u4f7f\u7528conda\u521b\u5efa\u73af\u5883<br \/>\nconda env create -f torch27_environment.yaml<\/p>\n<p># \u6fc0\u6d3b\u73af\u5883<br \/>\nconda activate torch27<\/p>\n<p>conda\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u5e76\u5b89\u88c5\u6240\u6709\u4f9d\u8d56\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u53ef\u80fd\u9700\u8981\u4e00\u4e9b\u65f6\u95f4&#xff0c;\u53d6\u51b3\u4e8e\u4f60\u7684\u7f51\u7edc\u901f\u5ea6\u548c\u73af\u5883\u5927\u5c0f\u3002<\/p>\n<p>\u5982\u679c\u9047\u5230\u67d0\u4e2a\u5305\u65e0\u6cd5\u901a\u8fc7conda\u5b89\u88c5&#xff08;\u901a\u5e38\u663e\u793a\u4e3a&#034;Solving environment: failed&#034;&#xff09;&#xff0c;\u4f60\u53ef\u4ee5\u5c1d\u8bd5&#xff1a;<\/p>\n<p># \u5148\u521b\u5efa\u73af\u5883\u4f46\u4e0d\u5b89\u88c5\u5305<br \/>\nconda create -n torch27 python&#061;3.9.0<\/p>\n<p># \u6fc0\u6d3b\u73af\u5883<br \/>\nconda activate torch27<\/p>\n<p># \u624b\u52a8\u5b89\u88c5\u6709\u95ee\u9898\u7684\u5305<br \/>\npip install \u5305\u540d&#061;&#061;\u7248\u672c\u53f7<\/p>\n<h4>4.2 \u4ece\u6253\u5305\u76ee\u5f55\u6062\u590d&#xff08;\u5bf9\u5e94\u65b9\u6cd5\u4e8c&#xff09;<\/h4>\n<p>\u5c06\u6253\u5305\u6587\u4ef6\u590d\u5236\u5230\u65b0\u673a\u5668&#xff0c;\u7136\u540e\u89e3\u538b\u5230conda\u7684\u73af\u5883\u76ee\u5f55&#xff1a;<\/p>\n<p># \u627e\u5230conda\u73af\u5883\u76ee\u5f55&#xff08;\u5047\u8bbe\u662f~\/miniconda3\/envs&#xff09;<br \/>\nconda info &#8211;envs<\/p>\n<p># \u89e3\u538b\u5230\u73af\u5883\u76ee\u5f55<br \/>\nmkdir -p ~\/miniconda3\/envs\/torch27<br \/>\ntar -xzvf torch27_env.tar.gz -C ~\/miniconda3\/envs\/torch27<\/p>\n<p># \u6fc0\u6d3b\u73af\u5883<br \/>\nconda activate torch27<\/p>\n<p>\u91cd\u8981\u63d0\u793a&#xff1a;\u8fd9\u79cd\u65b9\u6cd5\u8981\u6c42\u65b0\u673a\u5668\u7684conda\u73af\u5883\u8def\u5f84\u548c\u539f\u673a\u5668\u76f8\u540c\u3002\u5982\u679c\u8def\u5f84\u4e0d\u540c&#xff0c;\u4f60\u53ef\u80fd\u9700\u8981\u4fee\u6539\u73af\u5883\u4e2d\u7684\u4e00\u4e9b\u7edd\u5bf9\u8def\u5f84\u3002<\/p>\n<h4>4.3 \u4f7f\u7528conda-pack\u6062\u590d&#xff08;\u5bf9\u5e94\u65b9\u6cd5\u4e09&#xff09;<\/h4>\n<p>\u8fd9\u662f\u6700\u7b80\u5355\u7684\u65b9\u6cd5&#xff1a;<\/p>\n<p># \u521b\u5efa\u76ee\u6807\u76ee\u5f55<br \/>\nmkdir -p ~\/miniconda3\/envs\/torch27<\/p>\n<p># \u89e3\u538b\u5230\u76ee\u6807\u76ee\u5f55<br \/>\ntar -xzvf torch27_env.tar.gz -C ~\/miniconda3\/envs\/torch27<\/p>\n<p># \u6fc0\u6d3b\u73af\u5883<br \/>\nconda activate torch27<\/p>\n<p>conda-pack\u6253\u5305\u7684\u73af\u5883\u901a\u5e38\u517c\u5bb9\u6027\u66f4\u597d&#xff0c;\u56e0\u4e3a\u5b83\u4f1a\u5904\u7406\u4e00\u4e9b\u8def\u5f84\u95ee\u9898\u3002<\/p>\n<h3>5. \u9a8c\u8bc1\u8fc1\u79fb\u7ed3\u679c<\/h3>\n<p>\u73af\u5883\u6062\u590d\u540e&#xff0c;\u6700\u91cd\u8981\u7684\u4e00\u6b65\u662f\u9a8c\u8bc1\u5b83\u662f\u5426\u80fd\u6b63\u5e38\u5de5\u4f5c\u3002\u5bf9\u4e8ecv_resnet50_face-reconstruction\u9879\u76ee&#xff0c;\u6211\u4eec\u53ef\u4ee5\u8fd0\u884c\u4e00\u4e2a\u5b8c\u6574\u7684\u6d4b\u8bd5\u6d41\u7a0b\u3002<\/p>\n<h4>5.1 \u57fa\u7840\u73af\u5883\u9a8c\u8bc1<\/h4>\n<p>\u9996\u5148\u68c0\u67e5\u5173\u952e\u4f9d\u8d56\u7684\u7248\u672c\u662f\u5426\u6b63\u786e&#xff1a;<\/p>\n<p># \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u9a8c\u8bc1\u811a\u672c verify_env.py<br \/>\nimport torch<br \/>\nimport torchvision<br \/>\nimport cv2<br \/>\nimport modelscope<\/p>\n<p>print(f&#034;PyTorch\u7248\u672c: {torch.__version__}&#034;)<br \/>\nprint(f&#034;Torchvision\u7248\u672c: {torchvision.__version__}&#034;)<br \/>\nprint(f&#034;OpenCV\u7248\u672c: {cv2.__version__}&#034;)<br \/>\nprint(f&#034;ModelScope\u7248\u672c: {modelscope.__version__}&#034;)<\/p>\n<p># \u68c0\u67e5CUDA\u662f\u5426\u53ef\u7528&#xff08;\u5982\u679c\u539f\u73af\u5883\u7528\u4e86GPU&#xff09;<br \/>\nprint(f&#034;CUDA\u53ef\u7528: {torch.cuda.is_available()}&#034;)<br \/>\nif torch.cuda.is_available():<br \/>\n    print(f&#034;CUDA\u7248\u672c: {torch.version.cuda}&#034;)<br \/>\n    print(f&#034;GPU\u8bbe\u5907: {torch.cuda.get_device_name(0)}&#034;)<\/p>\n<p>\u8fd0\u884c\u8fd9\u4e2a\u811a\u672c&#xff1a;<\/p>\n<p>python verify_env.py<\/p>\n<p>\u4f60\u5e94\u8be5\u770b\u5230\u4e0e\u539f\u73af\u5883\u4e00\u81f4\u7684\u7248\u672c\u4fe1\u606f\u3002<\/p>\n<h4>5.2 \u9879\u76ee\u529f\u80fd\u9a8c\u8bc1<\/h4>\n<p>\u63a5\u4e0b\u6765\u6d4b\u8bd5\u4eba\u8138\u91cd\u5efa\u529f\u80fd\u662f\u5426\u6b63\u5e38&#xff1a;<\/p>\n<p># \u786e\u4fdd\u4f60\u5728\u9879\u76ee\u76ee\u5f55\u4e2d<br \/>\ncd cv_resnet50_face-reconstruction<\/p>\n<p># \u51c6\u5907\u6d4b\u8bd5\u56fe\u7247<br \/>\n# \u5982\u679c\u4f60\u6ca1\u6709\u73b0\u6210\u7684\u4eba\u8138\u56fe\u7247&#xff0c;\u53ef\u4ee5\u7528\u4ee5\u4e0b\u4ee3\u7801\u751f\u6210\u4e00\u4e2a\u6d4b\u8bd5\u7528\u7684\u7a7a\u767d\u56fe\u7247&#xff08;\u4ec5\u7528\u4e8e\u9a8c\u8bc1\u6d41\u7a0b&#xff09;<br \/>\nimport cv2<br \/>\nimport numpy as np<\/p>\n<p># \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684&#034;\u4eba\u8138&#034;\u56fe\u7247&#xff08;\u5b9e\u9645\u4f7f\u7528\u65f6\u8bf7\u7528\u771f\u5b9e\u4eba\u8138\u56fe\u7247&#xff09;<br \/>\ntest_img &#061; np.ones((256, 256, 3), dtype&#061;np.uint8) * 255<br \/>\ncv2.rectangle(test_img, (80, 80), (176, 176), (0, 0, 0), 2)  # \u753b\u4e00\u4e2a\u6846\u4ee3\u8868\u8138<br \/>\ncv2.circle(test_img, (112, 112), 10, (0, 0, 0), -1)  # \u5de6\u773c<br \/>\ncv2.circle(test_img, (144, 112), 10, (0, 0, 0), -1)  # \u53f3\u773c<br \/>\ncv2.ellipse(test_img, (128, 160), (30, 15), 0, 0, 180, (0, 0, 0), 2)  # \u5634\u5df4<\/p>\n<p>cv2.imwrite(&#039;test_face.jpg&#039;, test_img)<br \/>\nprint(&#034;\u2705 \u6d4b\u8bd5\u56fe\u7247\u5df2\u751f\u6210&#034;)<\/p>\n<p># \u8fd0\u884c\u91cd\u5efa\u811a\u672c<br \/>\npython test.py<\/p>\n<p>\u5982\u679c\u4e00\u5207\u6b63\u5e38&#xff0c;\u4f60\u4f1a\u770b\u5230\u7c7b\u4f3c\u8fd9\u6837\u7684\u8f93\u51fa&#xff1a;<\/p>\n<p>\u2705 \u5df2\u68c0\u6d4b\u5e76\u88c1\u526a\u4eba\u8138\u533a\u57df \u2192 \u5c3a\u5bf8&#xff1a;256&#215;256<br \/>\n\u2705 \u91cd\u5efa\u6210\u529f&#xff01;\u7ed3\u679c\u5df2\u4fdd\u5b58\u5230&#xff1a;.\/reconstructed_face.jpg<\/p>\n<p>\u867d\u7136\u7528\u7b80\u5355\u56fe\u5f62\u751f\u6210\u7684\u4eba\u8138\u91cd\u5efa\u6548\u679c\u4e0d\u4f1a\u597d&#xff0c;\u4f46\u8fd9\u4e2a\u6d41\u7a0b\u80fd\u9a8c\u8bc1\u6574\u4e2a\u73af\u5883\u662f\u5426\u5de5\u4f5c\u6b63\u5e38\u3002<\/p>\n<h3>6. \u9ad8\u7ea7\u6280\u5de7\u4e0e\u95ee\u9898\u6392\u67e5<\/h3>\n<p>\u5728\u5b9e\u9645\u8fc1\u79fb\u8fc7\u7a0b\u4e2d&#xff0c;\u4f60\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u7279\u6b8a\u60c5\u51b5\u3002\u8fd9\u91cc\u5206\u4eab\u51e0\u4e2a\u5b9e\u7528\u6280\u5de7\u3002<\/p>\n<h4>6.1 \u5904\u7406\u5e73\u53f0\u5dee\u5f02<\/h4>\n<p>\u5982\u679c\u4f60\u5728Linux\u4e0a\u6253\u5305\u73af\u5883&#xff0c;\u4f46\u8981\u5728Windows\u4e0a\u6062\u590d&#xff0c;\u76f4\u63a5\u4f7f\u7528YAML\u6587\u4ef6\u53ef\u80fd\u6709\u95ee\u9898\u3002\u8fd9\u65f6\u53ef\u4ee5&#xff1a;<\/p>\n<li>\n<p>\u5bfc\u51fa\u65f6\u6392\u9664\u5e73\u53f0\u7279\u5b9a\u4fe1\u606f&#xff1a;<\/p>\n<p> conda env export &#8211;name torch27 &#8211;from-history &gt; torch27_environment.yaml<\/p>\n<p>\u52a0\u4e0a&#8211;from-history\u53c2\u6570\u53ea\u4f1a\u5bfc\u51fa\u4f60\u660e\u786e\u5b89\u88c5\u7684\u5305&#xff0c;\u800c\u4e0d\u662f\u6240\u6709\u4f9d\u8d56&#xff0c;\u8fd9\u6837\u6587\u4ef6\u66f4\u7b80\u6d01&#xff0c;\u8de8\u5e73\u53f0\u517c\u5bb9\u6027\u66f4\u597d\u3002<\/p>\n<\/li>\n<li>\n<p>\u624b\u52a8\u8c03\u6574YAML\u6587\u4ef6&#xff1a; \u6253\u5f00YAML\u6587\u4ef6&#xff0c;\u5220\u9664prefix\u884c&#xff08;\u6700\u540e\u4e00\u884c&#xff09;&#xff0c;\u56e0\u4e3a\u8fd9\u662f\u7edd\u5bf9\u8def\u5f84&#xff0c;\u5728\u65b0\u673a\u5668\u4e0a\u65e0\u6548\u3002<\/p>\n<\/li>\n<h4>6.2 \u73af\u5883\u7626\u8eab<\/h4>\n<p>conda\u73af\u5883\u53ef\u80fd\u4f1a\u5305\u542b\u5f88\u591a\u4e0d\u5fc5\u8981\u7684\u5305\u3002\u8fc1\u79fb\u524d\u53ef\u4ee5\u5148\u6e05\u7406&#xff1a;<\/p>\n<p># \u67e5\u770b\u73af\u5883\u4e2d\u7684\u6240\u6709\u5305<br \/>\nconda list -n torch27<\/p>\n<p># \u6e05\u7406\u7f13\u5b58<br \/>\nconda clean &#8211;all<\/p>\n<p># \u5378\u8f7d\u4e0d\u5fc5\u8981\u7684\u5305<br \/>\nconda remove -n torch27 \u5305\u540d &#8211;force<\/p>\n<h4>6.3 \u5e38\u89c1\u95ee\u9898\u4e0e\u89e3\u51b3<\/h4>\n<p>\u95ee\u98981&#xff1a;\u5bfc\u5165\u73af\u5883\u540econda\u627e\u4e0d\u5230\u73af\u5883<\/p>\n<p>Could not find conda environment: torch27<\/p>\n<p>\u89e3\u51b3&#xff1a;\u68c0\u67e5\u73af\u5883\u662f\u5426\u771f\u7684\u521b\u5efa\u6210\u529f&#xff1a;<\/p>\n<p>conda info &#8211;envs<\/p>\n<p>\u5982\u679c\u770b\u4e0d\u5230torch27&#xff0c;\u53ef\u80fd\u662f\u8def\u5f84\u95ee\u9898\u3002\u5c1d\u8bd5\u6307\u5b9a\u5b8c\u6574\u8def\u5f84\u6fc0\u6d3b&#xff1a;<\/p>\n<p>source ~\/miniconda3\/envs\/torch27\/bin\/activate<\/p>\n<p>\u95ee\u98982&#xff1a;\u5bfc\u5165\u540e\u5305\u7248\u672c\u4e0d\u4e00\u81f4<\/p>\n<p>\u89e3\u51b3&#xff1a;\u53ef\u80fd\u662fconda\u9891\u9053\u914d\u7f6e\u4e0d\u540c\u3002\u53ef\u4ee5\u5c1d\u8bd5&#xff1a;<\/p>\n<p># \u66f4\u65b0\u6240\u6709\u5305\u5230\u6700\u65b0\u517c\u5bb9\u7248\u672c<br \/>\nconda update -n torch27 &#8211;all<\/p>\n<p>\u6216\u8005\u91cd\u65b0\u521b\u5efa\u73af\u5883&#xff0c;\u6307\u5b9a\u9891\u9053&#xff1a;<\/p>\n<p>conda env create -f torch27_environment.yaml -c conda-forge -c defaults<\/p>\n<p>\u95ee\u98983&#xff1a;\u7279\u5b9a\u5305\u65e0\u6cd5\u5b89\u88c5<\/p>\n<p>\u89e3\u51b3&#xff1a;\u5148\u8df3\u8fc7\u6709\u95ee\u9898\u7684\u5305&#xff0c;\u540e\u7eed\u624b\u52a8\u5b89\u88c5&#xff1a;<\/p>\n<p># \u7f16\u8f91YAML\u6587\u4ef6&#xff0c;\u5220\u9664\u6709\u95ee\u9898\u7684\u5305<br \/>\n# \u7136\u540e\u521b\u5efa\u73af\u5883<br \/>\nconda env create -f torch27_environment_modified.yaml<\/p>\n<p># \u6fc0\u6d3b\u73af\u5883\u540e\u624b\u52a8\u5b89\u88c5<br \/>\nconda activate torch27<br \/>\npip install \u6709\u95ee\u9898\u7684\u5305<\/p>\n<h3>7. \u81ea\u52a8\u5316\u811a\u672c&#xff1a;\u4e00\u952e\u5bfc\u51fa\u4e0e\u5bfc\u5165<\/h3>\n<p>\u4e3a\u4e86\u8ba9\u4f60\u4ee5\u540e\u8fc1\u79fb\u73af\u5883\u66f4\u8f7b\u677e&#xff0c;\u6211\u51c6\u5907\u4e86\u4e24\u4e2a\u5b9e\u7528\u811a\u672c\u3002<\/p>\n<h4>7.1 \u73af\u5883\u5bfc\u51fa\u811a\u672c<\/h4>\n<p>\u521b\u5efaexport_env.sh&#xff08;Linux\/macOS&#xff09;\u6216export_env.bat&#xff08;Windows&#xff09;&#xff1a;<\/p>\n<p>#!\/bin\/bash<br \/>\n# export_env.sh &#8211; \u4e00\u952e\u5bfc\u51faconda\u73af\u5883<\/p>\n<p>ENV_NAME&#061;&#034;torch27&#034;<br \/>\nBACKUP_DIR&#061;&#034;.\/env_backup&#034;<br \/>\nTIMESTAMP&#061;$(date &#043;&#034;%Y%m%d_%H%M%S&#034;)<\/p>\n<p>echo &#034;\u5f00\u59cb\u5bfc\u51fa\u73af\u5883: $ENV_NAME&#034;<\/p>\n<p># \u521b\u5efa\u5907\u4efd\u76ee\u5f55<br \/>\nmkdir -p $BACKUP_DIR<\/p>\n<p># \u65b9\u6cd51&#xff1a;\u5bfc\u51faYAML\u6587\u4ef6<br \/>\necho &#034;\u5bfc\u51fa\u73af\u5883\u914d\u7f6e\u6587\u4ef6&#8230;&#034;<br \/>\nconda env export &#8211;name $ENV_NAME &gt; &#034;$BACKUP_DIR\/${ENV_NAME}_${TIMESTAMP}.yaml&#034;<\/p>\n<p># \u65b9\u6cd52&#xff1a;\u4f7f\u7528conda-pack\u6253\u5305&#xff08;\u5982\u679c\u5df2\u5b89\u88c5&#xff09;<br \/>\nif command -v conda-pack &amp;&gt; \/dev\/null; then<br \/>\n    echo &#034;\u4f7f\u7528conda-pack\u6253\u5305\u73af\u5883&#8230;&#034;<br \/>\n    conda pack -n $ENV_NAME -o &#034;$BACKUP_DIR\/${ENV_NAME}_${TIMESTAMP}.tar.gz&#034;<br \/>\n    echo &#034;\u2705 \u73af\u5883\u5df2\u6253\u5305\u5230: $BACKUP_DIR\/${ENV_NAME}_${TIMESTAMP}.tar.gz&#034;<br \/>\nelse<br \/>\n    echo &#034;\u26a0 conda-pack\u672a\u5b89\u88c5&#xff0c;\u8df3\u8fc7\u6253\u5305&#034;<br \/>\n    echo &#034;\u5b89\u88c5conda-pack: conda install -c conda-forge conda-pack&#034;<br \/>\nfi<\/p>\n<p># \u5bfc\u51fa\u901a\u8fc7pip\u5b89\u88c5\u7684\u5305<br \/>\necho &#034;\u5bfc\u51fapip\u5b89\u88c5\u7684\u5305&#8230;&#034;<br \/>\npip freeze &gt; &#034;$BACKUP_DIR\/${ENV_NAME}_pip_${TIMESTAMP}.txt&#034;<\/p>\n<p>echo &#034;\u2705 \u73af\u5883\u5bfc\u51fa\u5b8c\u6210&#xff01;&#034;<br \/>\necho &#034;\u6587\u4ef6\u4fdd\u5b58\u5728: $BACKUP_DIR\/&#034;<br \/>\necho &#034;1. YAML\u6587\u4ef6: ${ENV_NAME}_${TIMESTAMP}.yaml&#034;<br \/>\necho &#034;2. Pip\u5305\u5217\u8868: ${ENV_NAME}_pip_${TIMESTAMP}.txt&#034;<br \/>\nif [ -f &#034;$BACKUP_DIR\/${ENV_NAME}_${TIMESTAMP}.tar.gz&#034; ]; then<br \/>\n    echo &#034;3. \u6253\u5305\u6587\u4ef6: ${ENV_NAME}_${TIMESTAMP}.tar.gz&#034;<br \/>\nfi<\/p>\n<h4>7.2 \u73af\u5883\u5bfc\u5165\u811a\u672c<\/h4>\n<p>\u521b\u5efaimport_env.sh&#xff1a;<\/p>\n<p>#!\/bin\/bash<br \/>\n# import_env.sh &#8211; \u4e00\u952e\u5bfc\u5165conda\u73af\u5883<\/p>\n<p>ENV_NAME&#061;&#034;torch27&#034;<br \/>\nENV_FILE&#061;&#034;.\/env_backup\/torch27_environment.yaml&#034;<\/p>\n<p>echo &#034;\u5f00\u59cb\u5bfc\u5165\u73af\u5883: $ENV_NAME&#034;<\/p>\n<p># \u68c0\u67e5YAML\u6587\u4ef6\u662f\u5426\u5b58\u5728<br \/>\nif [ ! -f &#034;$ENV_FILE&#034; ]; then<br \/>\n    echo &#034;\u274c \u627e\u4e0d\u5230\u73af\u5883\u6587\u4ef6: $ENV_FILE&#034;<br \/>\n    echo &#034;\u8bf7\u5c06YAML\u6587\u4ef6\u653e\u5728: $(pwd)\/env_backup\/&#034;<br \/>\n    exit 1<br \/>\nfi<\/p>\n<p># \u68c0\u67e5\u73af\u5883\u662f\u5426\u5df2\u5b58\u5728<br \/>\nif conda env list | grep -q &#034;^$ENV_NAME &#034;; then<br \/>\n    echo &#034;\u26a0 \u73af\u5883 $ENV_NAME \u5df2\u5b58\u5728&#034;<br \/>\n    read -p &#034;\u662f\u5426\u5220\u9664\u5e76\u91cd\u65b0\u521b\u5efa&#xff1f;(y\/n): &#034; -n 1 -r<br \/>\n    echo<br \/>\n    if [[ $REPLY &#061;~ ^[Yy]$ ]]; then<br \/>\n        conda env remove -n $ENV_NAME<br \/>\n    else<br \/>\n        echo &#034;\u64cd\u4f5c\u53d6\u6d88&#034;<br \/>\n        exit 0<br \/>\n    fi<br \/>\nfi<\/p>\n<p># \u521b\u5efa\u73af\u5883<br \/>\necho &#034;\u521b\u5efa\u65b0\u73af\u5883&#8230;&#034;<br \/>\nconda env create -f &#034;$ENV_FILE&#034;<\/p>\n<p>if [ $? -eq 0 ]; then<br \/>\n    echo &#034;\u2705 \u73af\u5883\u521b\u5efa\u6210\u529f&#xff01;&#034;<br \/>\n    echo &#034;&#034;<br \/>\n    echo &#034;\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6fc0\u6d3b\u73af\u5883:&#034;<br \/>\n    echo &#034;conda activate $ENV_NAME&#034;<br \/>\n    echo &#034;&#034;<br \/>\n    echo &#034;\u9a8c\u8bc1\u73af\u5883:&#034;<br \/>\n    echo &#034;conda list -n $ENV_NAME | head -10&#034;<br \/>\nelse<br \/>\n    echo &#034;\u274c \u73af\u5883\u521b\u5efa\u5931\u8d25&#034;<br \/>\n    echo &#034;\u5c1d\u8bd5\u624b\u52a8\u521b\u5efa: conda env create -f \\\\&#034;$ENV_FILE\\\\&#034;&#034;<br \/>\nfi<\/p>\n<h3>8. \u603b\u7ed3<\/h3>\n<p>\u901a\u8fc7\u672c\u6587\u7684\u5b8c\u6574\u6d41\u7a0b&#xff0c;\u4f60\u73b0\u5728\u5e94\u8be5\u80fd\u591f\u8f7b\u677e\u5730\u5c06cv_resnet50_face-reconstruction\u9879\u76ee\u73af\u5883&#xff08;\u6216\u4efb\u4f55\u5176\u4ed6conda\u73af\u5883&#xff09;\u4ece\u4e00\u4e2a\u670d\u52a1\u5668\u8fc1\u79fb\u5230\u53e6\u4e00\u4e2a\u670d\u52a1\u5668\u4e86\u3002\u8ba9\u6211\u4eec\u56de\u987e\u4e00\u4e0b\u5173\u952e\u8981\u70b9&#xff1a;<\/p>\n<p>\u4e09\u79cd\u8fc1\u79fb\u65b9\u6cd5\u5404\u6709\u9002\u7528\u573a\u666f&#xff1a;<\/p>\n<ul>\n<li>YAML\u5bfc\u51fa\u6700\u9002\u5408\u6709\u7f51\u7edc\u73af\u5883\u3001\u9700\u8981\u8de8\u5e73\u53f0\u7684\u60c5\u51b5<\/li>\n<li>\u76ee\u5f55\u6253\u5305\u6700\u9002\u5408\u76f8\u540c\u7cfb\u7edf\u3001\u9700\u8981\u79bb\u7ebf\u8fc1\u79fb\u7684\u60c5\u51b5<\/li>\n<li>conda-pack\u63d0\u4f9b\u4e86\u5e73\u8861\u7684\u89e3\u51b3\u65b9\u6848<\/li>\n<\/ul>\n<p>\u8fc1\u79fb\u7684\u6838\u5fc3\u6b65\u9aa4\u5f88\u7b80\u5355&#xff1a;<\/p>\n<li>\u5728\u539f\u73af\u5883\u5bfc\u51fa\u914d\u7f6e\u6216\u6253\u5305\u6587\u4ef6<\/li>\n<li>\u5c06\u6587\u4ef6\u4f20\u8f93\u5230\u65b0\u673a\u5668<\/li>\n<li>\u5728\u65b0\u673a\u5668\u4e0a\u6062\u590d\u73af\u5883<\/li>\n<li>\u9a8c\u8bc1\u73af\u5883\u529f\u80fd\u6b63\u5e38<\/li>\n<p>\u6700\u91cd\u8981\u7684\u5efa\u8bae&#xff1a;\u5b9a\u671f\u5907\u4efd\u4f60\u7684\u73af\u5883\u914d\u7f6e\u3002\u7279\u522b\u662f\u5f53\u4f60\u5b8c\u6210\u4e00\u4e2a\u91cd\u8981\u7684\u9879\u76ee\u914d\u7f6e\u65f6&#xff0c;\u7acb\u5373\u5bfc\u51fa\u73af\u5883YAML\u6587\u4ef6&#xff0c;\u8fd9\u80fd\u4e3a\u4f60\u8282\u7701\u5927\u91cf\u672a\u6765\u53ef\u80fd\u82b1\u8d39\u5728\u91cd\u65b0\u914d\u7f6e\u4e0a\u7684\u65f6\u95f4\u3002<\/p>\n<p>\u5bf9\u4e8ecv_resnet50_face-reconstruction\u8fd9\u6837\u7684\u9879\u76ee&#xff0c;\u7531\u4e8e\u5b83\u5df2\u7ecf\u4f18\u5316\u4e86\u56fd\u5185\u7f51\u7edc\u73af\u5883&#xff0c;\u8fc1\u79fb\u8fc7\u7a0b\u4f1a\u6bd4\u5176\u4ed6\u5305\u542b\u6d77\u5916\u4f9d\u8d56\u7684\u9879\u76ee\u66f4\u52a0\u987a\u7545\u3002\u4f46\u65e0\u8bba\u9879\u76ee\u590d\u6742\u4e0e\u5426&#xff0c;\u638c\u63e1\u73af\u5883\u8fc1\u79fb\u6280\u80fd\u90fd\u662f\u6df1\u5ea6\u5b66\u4e60\u5de5\u7a0b\u5e08\u7684\u5fc5\u5907\u80fd\u529b\u3002<\/p>\n<p>\u73b0\u5728&#xff0c;\u4f60\u53ef\u4ee5\u81ea\u4fe1\u5730\u5728\u4efb\u4f55\u673a\u5668\u4e0a\u5feb\u901f\u590d\u73b0\u4f60\u7684\u5de5\u4f5c\u73af\u5883&#xff0c;\u4e13\u6ce8\u4e8e\u7b97\u6cd5\u548c\u6a21\u578b\u672c\u8eab&#xff0c;\u800c\u4e0d\u662f\u7e41\u7410\u7684\u73af\u5883\u914d\u7f6e\u3002\u8fd9\u5c31\u662f\u5de5\u7a0b\u6548\u7387\u7684\u771f\u6b63\u63d0\u5347\u3002<\/p>\n<hr \/>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>cv_resnet50_face-reconstruction\u955c\u50cf\u514d\u914d\u7f6e\u5b9e\u64cd&#xff1a;conda env\u5bfc\u51fa\/\u5bfc\u5165\u4e0e\u8de8\u670d\u52a1\u5668\u8fc1\u79fb\u65b9\u6848<br 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