{"id":85708,"date":"2026-07-27T21:24:32","date_gmt":"2026-07-27T13:24:32","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/85708.html"},"modified":"2026-07-27T21:24:32","modified_gmt":"2026-07-27T13:24:32","slug":"%e5%9b%be%e5%83%8f%e8%af%84%e4%bc%b0%e6%96%b9%e6%b3%95-fid-%e7%9a%84%e5%85%b7%e4%bd%93%e5%ae%9e%e7%8e%b0%e5%8e%9f%e7%90%86%e4%b8%8e%e4%bb%a3%e7%a0%81%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/85708.html","title":{"rendered":"\u56fe\u50cf\u8bc4\u4f30\u65b9\u6cd5 FID \u7684\u5177\u4f53\u5b9e\u73b0\u539f\u7406\u4e0e\u4ee3\u7801\u5b9e\u73b0"},"content":{"rendered":"

\u4e00\u3001FID \u5b8c\u6574\u539f\u7406<\/h3>\n

1. \u7279\u5f81\u63d0\u53d6\u539f\u7406<\/h4>\n
  • \u4f7f\u7528 ImageNet \u9884\u8bad\u7ec3\u7684 InceptionV3 \u4f5c\u4e3a\u56fa\u5b9a\u7279\u5f81\u63d0\u53d6\u5668<\/li>\n
  • \u79fb\u9664\u7f51\u7edc\u672b\u7aef\u5206\u7c7b\u5c42\u4e0e Softmax，\u622a\u53d6\u5012\u6570\u7b2c\u4e8c\u5c42（Mixed_7c）\u7279\u5f81\u56fe<\/li>\n
  • \u5bf9\u7279\u5f81\u56fe\u6267\u884c\u5168\u5c40\u5e73\u5747\u6c60\u5316，\u5c06\u4efb\u610f\u8f93\u5165\u56fe\u50cf\u6620\u5c04\u4e3a\u56fa\u5b9a\u7684 2048 \u7ef4\u5b9e\u6570\u7279\u5f81\u5411\u91cf<\/li>\n
  • \u5206\u522b\u5bf9\u771f\u5b9e\u56fe\u548c\u751f\u6210\u56fe\u6279\u91cf\u63d0\u53d6，\u5404\u81ea\u6784\u6210\u72ec\u7acb\u7279\u5f81\u6837\u672c\u96c6<\/li>\n

    2. \u5206\u5e03\u5efa\u6a21\u539f\u7406<\/h4>\n
  • \u6d77\u91cf\u56fe\u50cf\u7ecf InceptionV3 \u5f97\u5230\u7684 2048 \u7ef4\u7279\u5f81，\u7edf\u8ba1\u5206\u5e03\u8fd1\u4f3c\u670d\u4ece\u591a\u7ef4\u9ad8\u65af\u5206\u5e03<\/li>\n
  • \u591a\u7ef4\u9ad8\u65af\u5206\u5e03\u7531\u4e24\u4e2a\u53c2\u6570\u552f\u4e00\u786e\u5b9a：<\/li>\n\n\u53c2\u6570\u8ba1\u7b97\u65b9\u5f0f\u8868\u5f81\u542b\u4e49<\/tr>\n\n\n\n
    \u5747\u503c\u5411\u91cf <\/p>\n

    \u03bc<\/p>\n

    \\\\boldsymbol{\\\\mu}<\/p>\n

    <\/span><\/span>\u03bc<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td>\n

    		\u5168\u90e8\u6837\u672c\u9010\u7ef4\u7b97\u672f\u5e73\u5747<\/td>\n\u7279\u5f81\u5206\u5e03\u7684\u4e2d\u5fc3\u4f4d\u7f6e<\/td>\n<\/tr>\n
    \u534f\u65b9\u5dee\u77e9\u9635 <\/p>\n

    \u03a3<\/p>\n

    \\\\Sigma<\/p>\n

    <\/span><\/span>\u03a3<\/span><\/span><\/span><\/span><\/span><\/td>\n

    		\u504f\u5dee\u5411\u91cf\u5916\u79ef\u6c42\u548c，\u5f52\u4e00\u5316<\/td>\n\u7279\u5f81\u6563\u5e03\u8303\u56f4\u3001\u7ef4\u5ea6\u95f4\u7ebf\u6027\u76f8\u5173\u7a0b\u5ea6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
  • \u771f\u5b9e\u56fe\u548c\u751f\u6210\u56fe\u5404\u81ea\u5bf9\u5e94\u4e00\u4e2a\u72ec\u7acb\u7684\u9ad8\u65af\u5206\u5e03：\n
      \n
    • \u771f\u5b9e\u5206\u5e03：\n

      N<\/p>\n

      (<\/p>\n

      \u03bc<\/p>\n

      r<\/p>\n

      ,<\/p>\n

      \u03a3<\/p>\n

      r<\/p>\n

      )<\/p>\n

      \\\\mathcal{N}(\\\\boldsymbol{\\\\mu}_r, \\\\Sigma_r)<\/p>\n

      <\/span><\/span>N<\/span>(<\/span>\u03bc<\/span><\/span><\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span><\/li>\n

    • \u751f\u6210\u5206\u5e03：\n

      N<\/p>\n

      (<\/p>\n

      \u03bc<\/p>\n

      g<\/p>\n

      ,<\/p>\n

      \u03a3<\/p>\n

      g<\/p>\n

      )<\/p>\n

      \\\\mathcal{N}(\\\\boldsymbol{\\\\mu}_g, \\\\Sigma_g)<\/p>\n

      <\/span><\/span>N<\/span>(<\/span>\u03bc<\/span><\/span><\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>,<\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<\/li>\n

      3. FID \u6570\u5b66\u8ba1\u7b97\u539f\u7406<\/h4>\n

      FID \u662f\u4e24\u4e2a\u591a\u7ef4\u9ad8\u65af\u5206\u5e03\u7684 Wasserstein-2 \u8ddd\u79bb\u5e73\u65b9，\u5b58\u5728\u89e3\u6790\u95ed\u5f0f\u89e3：<\/p>\n

      <\/p>\n

      F<\/p>\n

      I<\/p>\n

      D<\/p>\n

      =<\/p>\n

      \u2225<\/p>\n

      \u03bc<\/p>\n

      r<\/p>\n

      \u2212<\/p>\n

      \u03bc<\/p>\n

      g<\/p>\n

      \u2225<\/p>\n

      2<\/p>\n

      2<\/p>\n

      \u23df<\/p>\n

      \u4e2d\u5fc3\u4f4d\u7f6e\u8bef\u5dee<\/p>\n

      +<\/p>\n

      Tr<\/p>\n

      (<\/p>\n

      \u03a3<\/p>\n

      r<\/p>\n

      +<\/p>\n

      \u03a3<\/p>\n

      g<\/p>\n

      \u2212<\/p>\n

      2<\/p>\n

      \u03a3<\/p>\n

      r<\/p>\n

      \u03a3<\/p>\n

      g<\/p>\n

      )<\/p>\n

      \u23df<\/p>\n

      \u5206\u5e03\u5f62\u72b6\u8bef\u5dee<\/p>\n

      FID = \\\\underbrace{\\\\|\\\\boldsymbol{\\\\mu}_r – \\\\boldsymbol{\\\\mu}_g\\\\|_2^2}_{\\\\text{\u4e2d\u5fc3\u4f4d\u7f6e\u8bef\u5dee}} + \\\\underbrace{\\\\text{Tr}\\\\left(\\\\Sigma_r + \\\\Sigma_g – 2\\\\sqrt{\\\\Sigma_r \\\\Sigma_g}\\\\right)}_{\\\\text{\u5206\u5e03\u5f62\u72b6\u8bef\u5dee}}<\/p>\n

      <\/span><\/span>F<\/span>I<\/span>D<\/span><\/span>=<\/span><\/span><\/span><\/span><\/span>\u4e2d\u5fc3\u4f4d\u7f6e\u8bef\u5dee<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      <\/span><\/p>\n

      <\/span><\/p>\n

      <\/span><\/span><\/span><\/span>\u2225<\/span>\u03bc<\/span><\/span><\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2212<\/span><\/span>\u03bc<\/span><\/span><\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2225<\/span><\/span>2<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span><\/span>\u5206\u5e03\u5f62\u72b6\u8bef\u5dee<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      <\/span><\/p>\n

      <\/span><\/p>\n

      <\/span><\/span><\/span><\/span>Tr<\/span><\/span><\/span>(<\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2212<\/span><\/span>2<\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      <\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      \u7b2c\u4e00\u9879：\u4e2d\u5fc3\u4f4d\u7f6e\u8bef\u5dee<\/h5>\n
        \n
      • \u8fd0\u7b97：\u4e24\u4e2a\u5747\u503c\u5411\u91cf\u9010\u7ef4\u505a\u5dee，\u5dee\u503c\u5e73\u65b9\u6c42\u548c（L2 \u8303\u6570\u5e73\u65b9）<\/li>\n
      • \u6570\u5b66\u542b\u4e49：\u91cf\u5316\u4e24\u4e2a\u9ad8\u65af\u5206\u5e03\u4e2d\u5fc3\u70b9\u7684\u6b27\u5f0f\u8ddd\u79bb，\u8868\u5f81\u5168\u5c40\u4f4d\u7f6e\u504f\u79fb<\/li>\n
      • \u8bed\u4e49\u89e3\u91ca：InceptionV3 \u7279\u5f81\u7f16\u7801\u4e86\u7269\u4f53\u7c7b\u522b\u3001\u5149\u7167\u3001\u8272\u5f69\u3001\u6784\u56fe\u7b49\u9ad8\u5c42\u8bed\u4e49，\u4e2d\u5fc3\u504f\u79fb = \u4e24\u6279\u56fe\u6574\u4f53\u8bed\u4e49\u5747\u503c\u5b58\u5728\u504f\u5dee<\/li>\n<\/ul>\n
        \u7b2c\u4e8c\u9879：\u5206\u5e03\u5f62\u72b6\u8bef\u5dee<\/h5>\n

        \u7531\u5185\u5411\u5916\u8fd0\u7b97：<\/p>\n\n\u6b65\u9aa4\u8fd0\u7b97\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n
        1<\/td>\n<\/p>\n

        \u03a3<\/p>\n

        r<\/p>\n

        \u03a3<\/p>\n

        g<\/p>\n

        \\\\Sigma_r \\\\Sigma_g<\/p>\n

        <\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td>\n

        		\u534f\u65b9\u5dee\u77e9\u9635\u6807\u51c6\u4e58\u6cd5<\/td>\n<\/tr>\n
        2<\/td>\n<\/p>\n

        \u03a3<\/p>\n

        r<\/p>\n

        \u03a3<\/p>\n

        g<\/p>\n

        \\\\sqrt{\\\\Sigma_r \\\\Sigma_g}<\/p>\n

        <\/span><\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

        <\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td>\n

        		\u77e9\u9635\u5e73\u65b9\u6839（\u6ee1\u8db3 <\/p>\n

        M<\/p>\n

        2<\/p>\n

        =<\/p>\n

        \u03a3<\/p>\n

        r<\/p>\n

        \u03a3<\/p>\n

        g<\/p>\n

        M^2 = \\\\Sigma_r \\\\Sigma_g<\/p>\n

        <\/span><\/span>M<\/span><\/span>2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u7684\u6b63\u5b9a\u77e9\u9635 <\/p>\n

        M<\/p>\n

        M<\/p>\n

        <\/span><\/span>M<\/span><\/span><\/span><\/span><\/span>）<\/td>\n<\/tr>\n

        3<\/td>\n<\/p>\n

        2<\/p>\n

        \u03a3<\/p>\n

        r<\/p>\n

        \u03a3<\/p>\n

        g<\/p>\n

        2\\\\sqrt{\\\\Sigma_r \\\\Sigma_g}<\/p>\n

        <\/span><\/span>2<\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

        <\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td>\n

        		\u5e73\u65b9\u6839\u77e9\u9635\u6574\u4f53\u4e58 2<\/td>\n<\/tr>\n
        4<\/td>\n<\/p>\n

        \u03a3<\/p>\n

        r<\/p>\n

        +<\/p>\n

        \u03a3<\/p>\n

        g<\/p>\n

        \\\\Sigma_r + \\\\Sigma_g<\/p>\n

        <\/span><\/span>\u03a3<\/span><\/span>r<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>+<\/span><\/span><\/span><\/span>\u03a3<\/span><\/span>g<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/td>\n

        		\u5bf9\u4f4d\u76f8\u52a0，\u4e24\u7ec4\u7279\u5f81\u603b\u6563\u5e03\u91cf<\/td>\n<\/tr>\n
        5<\/td>\n\u51cf\u6cd5<\/td>\n\u5f97\u5230\u4ec5\u4fdd\u7559\u5dee\u5f02\u7684\u65b9\u9635<\/td>\n<\/tr>\n
        6<\/td>\n<\/p>\n

        Tr<\/p>\n

        (<\/p>\n

        \u22c5<\/p>\n

        )<\/p>\n

        \\\\text{Tr}(\\\\cdot)<\/p>\n

        <\/span><\/span>Tr<\/span><\/span>(<\/span>\u22c5<\/span>)<\/span><\/span><\/span><\/span><\/span> \u8ff9\u8fd0\u7b97<\/td>\n

        		\u4e3b\u5bf9\u89d2\u7ebf\u6c42\u548c，2048\u00d72048 \u2192 \u5355\u4e2a\u6807\u91cf<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
          \n
        • \u6570\u5b66\u542b\u4e49：\u91cf\u5316\u4e24\u4e2a\u534f\u65b9\u5dee\u77e9\u9635\u7684\u6574\u4f53\u5dee\u5f02，\u8868\u5f81\u6563\u5e03\u5f62\u6001\u3001\u79bb\u6563\u7a0b\u5ea6\u5dee\u5f02<\/li>\n
        • \u8bed\u4e49\u89e3\u91ca：\u534f\u65b9\u5dee\u63cf\u8ff0\u6837\u672c\u6ce2\u52a8\u8303\u56f4\u4e0e\u7ef4\u5ea6\u5173\u8054，\u8be5\u9879\u5bf9\u5e94\u4e24\u6279\u56fe\u591a\u6837\u6027\u548c\u7eb9\u7406\u5206\u5e03\u7684\u7edf\u8ba1\u504f\u5dee<\/li>\n<\/ul>\n

          4. \u6570\u503c\u5224\u5b9a\u539f\u7406<\/h4>\n
        • FID \u4e3a\u975e\u8d1f\u5b9e\u6570，\u4e24\u7ec4\u5206\u5e03\u5b8c\u5168\u4e00\u81f4\u65f6\u4e24\u9879\u5747\u4e3a 0，FID = 0<\/li>\n
        • FID \u5355\u8c03\u9012\u589e\u5bf9\u5e94\u4e24\u7ec4\u56fe\u50cf\u7279\u5f81\u5206\u5e03\u5dee\u5f02\u6269\u5927<\/li>\n\nFID\u542b\u4e49<\/tr>\n\n\n\n\n\n
          0<\/td>\n\u5b8c\u7f8e\u4e00\u81f4<\/td>\n<\/tr>\n
          < 10<\/td>\n\u4f18\u79c0，\u63a5\u8fd1\u771f\u5b9e<\/td>\n<\/tr>\n
          10~50<\/td>\n\u53ef\u7528<\/td>\n<\/tr>\n
          > 50<\/td>\n\u8d28\u91cf\u8f83\u5dee<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          5. \u5de5\u7a0b\u8ba1\u7b97\u539f\u7406<\/h4>\n
        • \u6279\u91cf\u8bfb\u53d6\u56fe\u50cf，\u7edf\u4e00\u7f29\u653e\u81f3 299\u00d7299（InceptionV3 \u6807\u51c6\u8f93\u5165）<\/li>\n
        • \u6279\u91cf\u524d\u5411\u63a8\u7406\u63d0\u53d6 2048 \u7ef4\u7279\u5f81，\u7f13\u5b58\u5168\u90e8\u7279\u5f81\u6837\u672c<\/li>\n
        • \u57fa\u4e8e\u7f13\u5b58\u7279\u5f81\u5206\u522b\u6c42\u89e3\u771f\u5b9e\u96c6\u3001\u751f\u6210\u96c6\u7684\u5747\u503c\u5411\u91cf\u4e0e\u534f\u65b9\u5dee\u77e9\u9635<\/li>\n
        • \u4ee3\u5165 Wasserstein-2 \u8ddd\u79bb\u95ed\u5f0f\u516c\u5f0f，\u8f93\u51fa\u6807\u91cf FID<\/li>\n
          \n

          \u4e8c\u3001\u5b8c\u6574\u53ef\u8fd0\u884c\u4ee3\u7801<\/h3>\n

          \u4f9d\u8d56\u5b89\u88c5<\/h4>\n

          pip install<\/span> torch torchvision pillow numpy scipy<\/p>\n

          \u5b8c\u6574\u5b9e\u73b0<\/h4>\n

          import<\/span> os
          \nimport<\/span> argparse
          \nimport<\/span> numpy as<\/span> np
          \nfrom<\/span> PIL import<\/span> Image
          \nimport<\/span> torch
          \nimport<\/span> torchvision.<\/span>models as<\/span> models
          \nimport<\/span> torchvision.<\/span>transforms as<\/span> transforms
          \nfrom<\/span> scipy.<\/span>linalg import<\/span> sqrtm<\/p>\n

          device =<\/span> torch.<\/span>device(<\/span>"cuda"<\/span> if<\/span> torch.<\/span>cuda.<\/span>is_available(<\/span>)<\/span> else<\/span> "cpu"<\/span>)<\/span><\/p>\n

          # ============================================================<\/span>
          \n# 1. \u6784\u5efa InceptionV3 \u7279\u5f81\u63d0\u53d6\u5668<\/span>
          \n# ============================================================<\/span>
          \ndef<\/span> build_inception_extractor<\/span>(<\/span>)<\/span>:<\/span>
          \n """\u52a0\u8f7d\u9884\u8bad\u7ec3 InceptionV3，\u622a\u53d6 Mixed_7c \u5c42\u7684 2048 \u7ef4\u7279\u5f81"""<\/span>
          \n # \u517c\u5bb9\u65b0\u7248 torchvision \u6743\u91cd\u52a0\u8f7d（pretrained=True \u5df2\u5e9f\u5f03）<\/span>
          \n weights =<\/span> models.<\/span>Inception_V3_Weights.<\/span>IMAGENET1K_V1
          \n inception =<\/span> models.<\/span>inception_v3(<\/span>weights=<\/span>weights)<\/span>.<\/span>to(<\/span>device)<\/span>
          \n # \u5173\u95ed\u8f85\u52a9\u5206\u7c7b\u5206\u652f，\u6d88\u9664\u5197\u4f59\u8ba1\u7b97<\/span>
          \n inception.<\/span>aux_logits =<\/span> False<\/span>
          \n inception.<\/span>eval<\/span>(<\/span>)<\/span><\/p>\n

          feat_output =<\/span> None<\/span><\/p>\n

          def<\/span> hook_fn<\/span>(<\/span>module,<\/span> input<\/span>,<\/span> output)<\/span>:<\/span>
          \n nonlocal<\/span> feat_output
          \n feat_output =<\/span> output<\/p>\n

          handle =<\/span> inception.<\/span>Mixed_7c.<\/span>register_forward_hook(<\/span>hook_fn)<\/span><\/p>\n

          def<\/span> get_feature<\/span>(<\/span>)<\/span>:<\/span>
          \n return<\/span> feat_output<\/p>\n

          return<\/span> inception,<\/span> handle,<\/span> get_feature<\/p>\n

          # ============================================================<\/span>
          \n# 2. \u56fe\u50cf\u9884\u5904\u7406<\/span>
          \n# ============================================================<\/span>
          \ntransform =<\/span> transforms.<\/span>Compose(<\/span>[<\/span>
          \n transforms.<\/span>Resize(<\/span>(<\/span>299<\/span>,<\/span> 299<\/span>)<\/span>)<\/span>,<\/span>
          \n transforms.<\/span>ToTensor(<\/span>)<\/span>,<\/span>
          \n transforms.<\/span>Normalize(<\/span>mean=<\/span>[<\/span>0.485<\/span>,<\/span> 0.456<\/span>,<\/span> 0.406<\/span>]<\/span>,<\/span>
          \n std=<\/span>[<\/span>0.229<\/span>,<\/span> 0.224<\/span>,<\/span> 0.225<\/span>]<\/span>)<\/span>,<\/span>
          \n]<\/span>)<\/span><\/p>\n

          # ============================================================<\/span>
          \n# 3. \u6279\u91cf\u63d0\u53d6 2048 \u7ef4\u7279\u5f81<\/span>
          \n# ============================================================<\/span>
          \ndef<\/span> extract_all_features<\/span>(<\/span>img_dir,<\/span> extractor,<\/span> get_feat,<\/span> batch_size=<\/span>32<\/span>)<\/span>:<\/span>
          \n """\u6279\u91cf\u63a8\u7406\u63d0\u53d6\u7279\u5f81，\u81ea\u52a8\u8df3\u8fc7\u635f\u574f\u56fe\u7247"""<\/span>
          \n feat_list =<\/span> [<\/span>]<\/span>
          \n valid_suffix =<\/span> (<\/span>'.jpg'<\/span>,<\/span> '.jpeg'<\/span>,<\/span> '.png'<\/span>)<\/span>
          \n img_names =<\/span> [<\/span>f for<\/span> f in<\/span> os.<\/span>listdir(<\/span>img_dir)<\/span>
          \n if<\/span> f.<\/span>lower(<\/span>)<\/span>.<\/span>endswith(<\/span>valid_suffix)<\/span>]<\/span>
          \n print<\/span>(<\/span>f"\u68c0\u6d4b\u5230 <\/span>{<\/span>len<\/span>(<\/span>img_names)<\/span>}<\/span><\/span> \u5f20\u56fe\u7247"<\/span><\/span>)<\/span><\/p>\n

          for<\/span> start_idx in<\/span> range<\/span>(<\/span>0<\/span>,<\/span> len<\/span>(<\/span>img_names)<\/span>,<\/span> batch_size)<\/span>:<\/span>
          \n batch_imgs =<\/span> [<\/span>]<\/span>
          \n end_idx =<\/span> min<\/span>(<\/span>start_idx +<\/span> batch_size,<\/span> len<\/span>(<\/span>img_names)<\/span>)<\/span><\/p>\n

          for<\/span> name in<\/span> img_names[<\/span>start_idx:<\/span>end_idx]<\/span>:<\/span>
          \n img_path =<\/span> os.<\/span>path.<\/span>join(<\/span>img_dir,<\/span> name)<\/span>
          \n try<\/span>:<\/span>
          \n img =<\/span> Image.<\/span>open<\/span>(<\/span>img_path)<\/span>.<\/span>convert(<\/span>'RGB'<\/span>)<\/span>
          \n batch_imgs.<\/span>append(<\/span>transform(<\/span>img)<\/span>)<\/span>
          \n except<\/span> Exception as<\/span> e:<\/span>
          \n print<\/span>(<\/span>f"\u8df3\u8fc7\u635f\u574f\u56fe\u7247 <\/span>{<\/span>name}<\/span><\/span>: <\/span>{<\/span>e}<\/span><\/span>"<\/span><\/span>)<\/span>
          \n continue<\/span><\/p>\n

          if<\/span> not<\/span> batch_imgs:<\/span>
          \n continue<\/span><\/p>\n

          img_batch =<\/span> torch.<\/span>stack(<\/span>batch_imgs)<\/span>.<\/span>to(<\/span>device)<\/span>
          \n with<\/span> torch.<\/span>no_grad(<\/span>)<\/span>:<\/span>
          \n extractor(<\/span>img_batch)<\/span><\/p>\n

          feat_map =<\/span> get_feat(<\/span>)<\/span> # [B, 2048, 8, 8]<\/span>
          \n feat_vec =<\/span> torch.<\/span>mean(<\/span>feat_map,<\/span> dim=<\/span>[<\/span>2<\/span>,<\/span> 3<\/span>]<\/span>)<\/span>.<\/span>cpu(<\/span>)<\/span>.<\/span>numpy(<\/span>)<\/span> # [B, 2048]<\/span>
          \n feat_list.<\/span>extend(<\/span>feat_vec)<\/span>
          \n print<\/span>(<\/span>f"\u5df2\u5904\u7406 <\/span>{<\/span>end_idx}<\/span><\/span>\/<\/span>{<\/span>len<\/span>(<\/span>img_names)<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

          return<\/span> np.<\/span>array(<\/span>feat_list)<\/span> # [N, 2048]<\/span><\/p>\n

          # ============================================================<\/span>
          \n# 4. \u8ba1\u7b97\u5747\u503c\u548c\u534f\u65b9\u5dee（\u6709\u504f\u4f30\u8ba1，\u4e0e pytorch-fid \u5bf9\u9f50）<\/span>
          \n# ============================================================<\/span>
          \ndef<\/span> calc_mu_sigma<\/span>(<\/span>features)<\/span>:<\/span>
          \n """\u4ece\u7279\u5f81\u77e9\u9635 [N, 2048] \u8ba1\u7b97\u9ad8\u65af\u5206\u5e03\u7684\u5747\u503c\u548c\u534f\u65b9\u5dee"""<\/span>
          \n mu =<\/span> np.<\/span>mean(<\/span>features,<\/span> axis=<\/span>0<\/span>)<\/span> # [2048]<\/span>
          \n sigma =<\/span> np.<\/span>cov(<\/span>features,<\/span> rowvar=<\/span>False<\/span>,<\/span> bias=<\/span>True<\/span>)<\/span> # [2048, 2048]<\/span>
          \n return<\/span> mu,<\/span> sigma<\/p>\n

          # ============================================================<\/span>
          \n# 5. FID \u6838\u5fc3\u516c\u5f0f<\/span>
          \n# ============================================================<\/span>
          \ndef<\/span> calculate_fid<\/span>(<\/span>mu1,<\/span> sigma1,<\/span> mu2,<\/span> sigma2)<\/span>:<\/span>
          \n """Wasserstein-2 \u8ddd\u79bb\u5e73\u65b9 = \u4e2d\u5fc3\u504f\u79fb + \u5f62\u72b6\u5dee\u5f02"""<\/span>
          \n # \u7b2c\u4e00\u9879：\u5747\u503c L2 \u8303\u6570\u5e73\u65b9<\/span>
          \n diff_mu =<\/span> mu1 –<\/span> mu2
          \n term1 =<\/span> np.<\/span>sum<\/span>(<\/span>diff_mu **<\/span> 2<\/span>)<\/span><\/p>\n

          # \u7b2c\u4e8c\u9879：\u534f\u65b9\u5dee\u8ff9\u90e8\u5206<\/span>
          \n cov_product =<\/span> sigma1 @ sigma2
          \n cov_sqrt =<\/span> sqrtm(<\/span>cov_product)<\/span>
          \n if<\/span> np.<\/span>iscomplexobj(<\/span>cov_sqrt)<\/span>:<\/span> # \u6d88\u9664\u6d6e\u70b9\u865a\u6570\u8bef\u5dee<\/span>
          \n cov_sqrt =<\/span> cov_sqrt.<\/span>real
          \n term2 =<\/span> np.<\/span>trace(<\/span>sigma1 +<\/span> sigma2 –<\/span> 2<\/span> *<\/span> cov_sqrt)<\/span><\/p>\n

          return<\/span> float<\/span>(<\/span>term1 +<\/span> term2)<\/span><\/p>\n

          # ============================================================<\/span>
          \n# \u4e3b\u7a0b\u5e8f（\u652f\u6301\u547d\u4ee4\u884c\u53c2\u6570）<\/span>
          \n# ============================================================<\/span>
          \nif<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
          \n parser =<\/span> argparse.<\/span>ArgumentParser(<\/span>description=<\/span>"FID \u56fe\u50cf\u8d28\u91cf\u8bc4\u4f30"<\/span>)<\/span>
          \n parser.<\/span>add_argument(<\/span>"–real"<\/span>,<\/span> type<\/span>=<\/span>str<\/span>,<\/span> required=<\/span>True<\/span>,<\/span> help<\/span>=<\/span>"\u771f\u5b9e\u56fe\u7247\u6587\u4ef6\u5939\u8def\u5f84"<\/span>)<\/span>
          \n parser.<\/span>add_argument(<\/span>"–gen"<\/span>,<\/span> type<\/span>=<\/span>str<\/span>,<\/span> required=<\/span>True<\/span>,<\/span> help<\/span>=<\/span>"\u751f\u6210\u56fe\u7247\u6587\u4ef6\u5939\u8def\u5f84"<\/span>)<\/span>
          \n parser.<\/span>add_argument(<\/span>"–batch"<\/span>,<\/span> type<\/span>=<\/span>int<\/span>,<\/span> default=<\/span>32<\/span>,<\/span> help<\/span>=<\/span>"\u63a8\u7406\u6279\u91cf\u5927\u5c0f"<\/span>)<\/span>
          \n args =<\/span> parser.<\/span>parse_args(<\/span>)<\/span><\/p>\n

          # \u521d\u59cb\u5316\u7279\u5f81\u63d0\u53d6\u5668<\/span>
          \n model,<\/span> hook_handle,<\/span> get_feature =<\/span> build_inception_extractor(<\/span>)<\/span><\/p>\n

          # \u63d0\u53d6\u7279\u5f81<\/span>
          \n print<\/span>(<\/span>"===== \u63d0\u53d6\u771f\u5b9e\u56fe\u7247\u7279\u5f81 ====="<\/span>)<\/span>
          \n feats_real =<\/span> extract_all_features(<\/span>args.<\/span>real,<\/span> model,<\/span> get_feature,<\/span> args.<\/span>batch)<\/span>
          \n print<\/span>(<\/span>f"\u6709\u6548\u771f\u5b9e\u6837\u672c: <\/span>{<\/span>feats_real.<\/span>shape[<\/span>0<\/span>]<\/span>}<\/span><\/span> \u5f20"<\/span><\/span>)<\/span><\/p>\n

          print<\/span>(<\/span>"===== \u63d0\u53d6\u751f\u6210\u56fe\u7247\u7279\u5f81 ====="<\/span>)<\/span>
          \n feats_gen =<\/span> extract_all_features(<\/span>args.<\/span>gen,<\/span> model,<\/span> get_feature,<\/span> args.<\/span>batch)<\/span>
          \n print<\/span>(<\/span>f"\u6709\u6548\u751f\u6210\u6837\u672c: <\/span>{<\/span>feats_gen.<\/span>shape[<\/span>0<\/span>]<\/span>}<\/span><\/span> \u5f20"<\/span><\/span>)<\/span><\/p>\n

          # \u8ba1\u7b97\u9ad8\u65af\u53c2\u6570<\/span>
          \n mu_real,<\/span> sigma_real =<\/span> calc_mu_sigma(<\/span>feats_real)<\/span>
          \n mu_gen,<\/span> sigma_gen =<\/span> calc_mu_sigma(<\/span>feats_gen)<\/span><\/p>\n

          # \u8ba1\u7b97 FID<\/span>
          \n fid_score =<\/span> calculate_fid(<\/span>mu_real,<\/span> sigma_real,<\/span> mu_gen,<\/span> sigma_gen)<\/span>
          \n print<\/span>(<\/span>f"\\\\n===== FID = <\/span>{<\/span>fid_score:<\/span>.4f<\/span>}<\/span><\/span> ====="<\/span><\/span>)<\/span><\/p>\n

          hook_handle.<\/span>remove(<\/span>)<\/span>
          \n torch.<\/span>cuda.<\/span>empty_cache(<\/span>)<\/span><\/p>\n

          \n

          \u4e09\u3001\u4f7f\u7528\u8bf4\u660e<\/h3>\n

          pip install<\/span> torch torchvision pillow numpy scipy<\/p>\n

          # \u547d\u4ee4\u884c\u4f20\u53c2<\/span>
          \npython fid_calc.py –real<\/span> .\/real_images –gen<\/span> .\/gen_images –batch<\/span> 32<\/span><\/p>\n

          \u6ce8\u610f\u4e8b\u9879<\/h4>\n\n\u6ce8\u610f\u70b9\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n
          \u6837\u672c\u91cf<\/td>\n\u5efa\u8bae \u2265 50000 \u5f20（\u8bba\u6587\u6807\u51c6），< 10000 \u504f\u7f6e\u4e25\u91cd，\u4e0d\u5177\u5907\u5bf9\u6bd4\u4ef7\u503c<\/td>\n<\/tr>\n
          \u534f\u65b9\u5dee\u8ba1\u7b97<\/td>\nbias=True \u6709\u504f\u4f30\u8ba1，\u4e0e pytorch-fid \u5b98\u65b9\u5de5\u5177\u7ed3\u679c\u4e00\u81f4<\/td>\n<\/tr>\n
          \u635f\u574f\u56fe\u7247<\/td>\n\u81ea\u52a8\u8df3\u8fc7，\u4e0d\u4f1a\u4e2d\u65ad\u7a0b\u5e8f<\/td>\n<\/tr>\n
          \u6279\u91cf\u63a8\u7406<\/td>\n–batch \u53c2\u6570\u63a7\u5236 batch size，GPU \u73af\u5883\u5efa\u8bae 32~64<\/td>\n<\/tr>\n
          \u8bbe\u5907<\/td>\n\u81ea\u52a8\u9002\u914d CPU \/ GPU<\/td>\n<\/tr>\n
          \u56fe\u7247\u683c\u5f0f<\/td>\n\u652f\u6301 .jpg \/ .jpeg \/ .png<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u4e2a\u4eba\u80fd\u529b\u6709\u9650，\u6709\u95ee\u9898\u968f\u65f6\u8054\u7cfb~<\/p>\n","protected":false},"excerpt":{"rendered":"

          \u4e00\u3001FID \u5b8c\u6574\u539f\u7406
          \n1. \u7279\u5f81\u63d0\u53d6\u539f\u7406
          \n\u4f7f\u7528 ImageNet \u9884\u8bad\u7ec3\u7684 InceptionV3 \u4f5c\u4e3a\u56fa\u5b9a\u7279\u5f81\u63d0\u53d6\u5668\u79fb\u9664\u7f51\u7edc\u672b\u7aef\u5206\u7c7b\u5c42\u4e0e Softmax，\u622a\u53d6\u5012\u6570\u7b2c\u4e8c\u5c42（Mixed_7c）\u7279\u5f81\u56fe\u5bf9\u7279\u5f81\u56fe\u6267\u884c\u5168\u5c40\u5e73\u5747\u6c60\u5316，\u5c06\u4efb\u610f\u8f93\u5165\u56fe\u50cf\u6620\u5c04\u4e3a\u56fa\u5b9a\u7684 2048 \u7ef4\u5b9e\u6570\u7279\u5f81\u5411\u91cf\u5206\u522b\u5bf9\u771f\u5b9e\u56fe\u548c\u751f\u6210\u56fe\u6279\u91cf\u63d0\u53d6，\u5404\u81ea\u6784\u6210\u72ec\u7acb\u7279\u5f81\u6837\u672c\u96c6
          \n2. \u5206\u5e03\u5efa\u6a21\u539f\u7406
          \n\u6d77\u91cf\u56fe\u50cf\u7ecf InceptionV3 \u5f97\u5230\u7684 2048 <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,207],"topic":[],"class_list":["post-85708","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-207"],"yoast_head":"\n\u56fe\u50cf\u8bc4\u4f30\u65b9\u6cd5 FID \u7684\u5177\u4f53\u5b9e\u73b0\u539f\u7406\u4e0e\u4ee3\u7801\u5b9e\u73b0 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/85708.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u56fe\u50cf\u8bc4\u4f30\u65b9\u6cd5 FID \u7684\u5177\u4f53\u5b9e\u73b0\u539f\u7406\u4e0e\u4ee3\u7801\u5b9e\u73b0 - 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