{"id":21198,"date":"2024-07-17T10:43:39","date_gmt":"2024-07-17T02:43:39","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=21198"},"modified":"2024-07-17T10:43:39","modified_gmt":"2024-07-17T02:43:39","slug":"%e9%a6%96%e4%b8%aa%e5%85%a8%e9%87%8f%e5%8c%96vision-transformer%e7%9a%84%e6%96%b9%e6%b3%95fq-vit%ef%bc%8cai%e5%a4%a7%e6%a8%a1%e5%9e%8b%e8%90%bd%e5%9c%b0%e4%b8%8d%e8%bf%9c%e4%ba%86%ef%bc%81","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2024\/07\/17\/21198\/","title":{"rendered":"\u9996\u4e2a\u5168\u91cf\u5316Vision Transformer\u7684\u65b9\u6cd5FQ-ViT\uff0cAI\u5927\u6a21\u578b\u843d\u5730\u4e0d\u8fdc\u4e86\uff01"},"content":{"rendered":"
\"\"<\/div>\n
\u5c06\u7b97\u6cd5\u7f51\u7edc\u8fdb\u884c\u91cf\u5316\u548c\u6a21\u578b\u8f6c\u6362\u53ef\u4ee5\u663e\u7740\u964d\u4f4e\u6a21\u578b\u63a8\u7406\u7684\u590d\u6742\u6027\uff0c\u5e76\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u7136\u800c\uff0c\u5927\u591a\u6570\u73b0\u6709\u7684\u91cf\u5316\u65b9\u6cd5\u4e3b\u8981\u662f\u9488\u5bf9\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5f00\u53d1\u7684\uff0c\u5e76\u4e14\u5728\u5b8c\u5168\u91cf\u5316\u7684vision Transformer\u4e0a\u5e94\u7528\u65f6\u4f1a\u51fa\u73b0\u4e25\u91cd\u7684\u6389\u70b9\u3002\u4eca\u5929\u6211\u4eec\u5c31\u5206\u4eab\u4e00\u4e2a\u65b0\u6280\u672f\uff0c\u5b9e\u73b0\u9ad8\u7cbe\u5ea6\u91cf\u5316\u7684Vit\u90e8\u7f72\u3002AI\u5927\u6a21\u578b\u843d\u5730\u4f7f\u7528\u79bb\u6211\u4eec\u8fd8\u8fdc\u5417\uff1f<\/div>\n
\n
\n

\u672c\u6587\u7ecf\u8ba1\u7b97\u673a\u89c6\u89c9\u7814\u7a76\u9662\u516c\u4f17\u53f7\u6388\u6743\u8f6c\u8f7d\uff0c\u8f6c\u8f7d\u8bf7\u8054\u7cfb\u51fa\u5904\u3002<\/p>\n

\"\u56fe\u7247\"<\/p>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2111.13824.pdf<\/p>\n

\u9879\u76ee\u4ee3\u7801\uff1ahttps:\/\/github.com\/megvii-research\/FQ-ViT<\/p>\n

01 \u603b\u00a0 \u8ff0<\/h3>\n

Transformer \u662f\u73b0\u5728\u706b\u70ed\u7684AIGC\u9884\u8bad\u7ec3\u5927\u6a21\u578b\u7684\u57fa\u7840\uff0c\u800cViT(Vision Transformer)\u662f\u771f\u6b63\u610f\u4e49\u4e0a\u5c06\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u7684Transformer\u5e26\u5230\u4e86\u89c6\u89c9\u9886\u57df\u3002\u4eceTransformer\u7684\u53d1\u5c55\u5386\u7a0b\u5c31\u53ef\u4ee5\u770b\u51fa\uff0c\u4eceTransformer\u7684\u63d0\u51fa\u5230\u5c06Transformer\u5e94\u7528\u5230\u89c6\u89c9\uff0c\u5176\u5b9e\u4e2d\u95f4\u86f0\u4f0f\u4e86\u4e09\u5e74\u7684\u65f6\u95f4\u3002\u800c\u4ece\u5c06Transformer\u5e94\u7528\u5230\u89c6\u89c9\u9886\u57df(ViT)\u5230AIGC\u7684\u706b\u7206\u4e5f\u5dee\u4e0d\u591a\u7528\u4e86\u4e24\u4e09\u5e74\u3002\u5176\u5b9eAIGC\u7684\u706b\u7206\uff0c\u4ece2022\u5e74\u4e0b\u65ec\u5c31\u5f00\u59cb\u6709\u4e00\u4e9b\u82d7\u6761\uff0c\u90a3\u65f6\u5c31\u9010\u6e10\u6709\u4e00\u4e9bAIGC\u597d\u73a9\u7684\u7b97\u6cd5\u653e\u51fa\u6765\uff0c\u800c\u5230\u73b0\u5728\uff0cAIGC\u597d\u73a9\u7684\u9879\u76ee\u771f\u662f\u5c42\u51fa\u4e0d\u7a77\u3002<\/p>\n

\"\u56fe\u7247\"<\/p>\n

\u968f\u7740\u8fd1\u4e24\u5e74\u6765\u5bf9\u89c6\u89c9Transformer\u6a21\u578b\uff08ViT\uff09\u7684\u6df1\u5165\u7814\u7a76\uff0cViT\u7684\u8868\u8fbe\u80fd\u529b\u4e0d\u65ad\u63d0\u5347\uff0c\u5e76\u5df2\u7ecf\u5728\u5927\u90e8\u5206\u89c6\u89c9\u57fa\u7840\u4efb\u52a1 (\u5206\u7c7b\uff0c\u68c0\u6d4b\uff0c\u5206\u5272\u7b49) \u4e0a\u5b9e\u73b0\u4e86\u5927\u5e45\u5ea6\u7684\u6027\u80fd\u7a81\u7834\u3002\u7136\u800c\uff0c\u5f88\u591a\u5b9e\u9645\u5e94\u7528\u573a\u666f\u5bf9\u6a21\u578b\u5b9e\u65f6\u63a8\u7406\u7684\u80fd\u529b\u8981\u6c42\u8f83\u9ad8\uff0c\u4f46\u5927\u90e8\u5206\u8f7b\u91cf\u5316ViT\u4ecd\u65e0\u6cd5\u5728\u591a\u4e2a\u90e8\u7f72\u573a\u666f \uff08GPU\uff0cCPU\uff0cONNX\uff0c\u79fb\u52a8\u7aef\u7b49\uff09\u8fbe\u5230\u4e0e\u8f7b\u91cf\u7ea7CNN\uff08\u5982MobileNet\uff09 \u76f8\u5ab2\u7f8e\u7684\u901f\u5ea6\u3002<\/p>\n

\u56e0\u6b64\uff0c\u91cd\u65b0\u5ba1\u89c6\u4e86ViT\u76842\u4e2a\u4e13\u5c5e\u6a21\u5757\uff0c\u5e76\u53d1\u73b0\u4e86\u9000\u5316\u539f\u56e0\u5982\u4e0b\uff1a<\/p>\n

    \n
  • \u7814\u7a76\u8005\u53d1\u73b0LayerNorm\u8f93\u5165\u7684\u901a\u9053\u95f4\u53d8\u5316\u4e25\u91cd\uff0c\u6709\u4e9b\u901a\u9053\u8303\u56f4\u751a\u81f3\u8d85\u8fc7\u4e2d\u503c\u768440\u500d\u3002\u4f20\u7edf\u65b9\u6cd5\u65e0\u6cd5\u5904\u7406\u5982\u6b64\u5927\u7684\u6fc0\u6d3b\u6ce2\u52a8\uff0c\u8fd9\u5c06\u5bfc\u81f4\u5f88\u5927\u7684\u91cf\u5316\u8bef\u5dee<\/li>\n
  • \u8fd8\u53d1\u73b0\u6ce8\u610f\u529b\u56fe\u7684\u503c\u5177\u6709\u6781\u7aef\u7684\u4e0d\u5747\u5300\u5206\u5e03\uff0c\u5927\u591a\u6570\u503c\u805a\u96c6\u57280\uff5e0.01\u4e4b\u95f4\uff0c\u5c11\u6570\u9ad8\u6ce8\u610f\u529b\u503c\u63a5\u8fd11<\/li>\n<\/ul>\n

    \u57fa\u4e8e\u4ee5\u4e0a\u5206\u6790\uff0c\u7814\u7a76\u8005\u63d0\u51fa\u4e86Power-of-Two Factor\uff08PTF\uff09\u6765\u91cf\u5316LayerNorm\u7684\u8f93\u5165\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u91cf\u5316\u8bef\u5dee\u5927\u5927\u964d\u4f4e\uff0c\u5e76\u4e14\u7531\u4e8eBit-Shift\u7b97\u5b50\uff0c\u6574\u4f53\u8ba1\u7b97\u6548\u7387\u4e0e\u5206\u5c42\u91cf\u5316\u7684\u8ba1\u7b97\u6548\u7387\u76f8\u540c\u3002\u6b64\u5916\u8fd8\u63d0\u51fa\u4e86Log Int Softmax\uff08LIS\uff09\uff0c\u5b83\u4e3a\u5c0f\u503c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7684\u91cf\u5316\u5206\u8fa8\u7387\uff0c\u5e76\u4e3aSoftmax\u63d0\u4f9b\u4e86\u66f4\u6709\u6548\u7684\u6574\u6570\u63a8\u7406\u3002\u7ed3\u5408\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u672c\u6587\u9996\u6b21\u5b9e\u73b0\u4e86\u5168\u91cf\u5316Vision Transformer\u7684\u8bad\u7ec3\u540e\u91cf\u5316\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    02 \u65b0\u6846\u67b6<\/h3>\n

    \u4e0b\u9762\u7684\u8fd9\u4e24\u5f20\u56fe\u8868\u660e\uff0c\u4e0eCNN\u76f8\u6bd4\uff0c\u89c6\u89c9\u8f6c\u6362\u5668\u4e2d\u5b58\u5728\u4e25\u91cd\u7684\u901a\u9053\u95f4\u53d8\u5316\uff0c\u8fd9\u5bfc\u81f4\u4e86\u5206\u5c42\u91cf\u5316\u7684\u4e0d\u53ef\u63a5\u53d7\u7684\u91cf\u5316\u8bef\u5dee\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u9996\u5148\u89e3\u91ca\u7f51\u7edc\u91cf\u5316\u7b26\u53f7\u3002\u5047\u8bbe\u91cf\u5316\u4f4d\u5bbd\u4e3ab\uff0c\u91cf\u5316\u5668Q\uff08X|b\uff09\u53ef\u4ee5\u516c\u5f0f\u5316\u4e3a\u5c06\u6d6e\u70b9\u6570X\u2208R\u6620\u5c04\u5230\u6700\u8fd1\u91cf\u5316\u4ed3\u7684\u51fd\u6570\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    Uniform Quantization<\/h3>\n

    Uniform Quantization\u5728\u5927\u591a\u6570\u786c\u4ef6\u5e73\u53f0\u4e0a\u90fd\u5f97\u5230\u4e86\u5f88\u597d\u7684\u652f\u6301\u3002\u5b83\u7684\u91cf\u5316\u5668Q\uff08X|b\uff09\u53ef\u4ee5\u5b9a\u4e49\u4e3a\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u5176\u4e2ds\uff08\u6807\u5ea6\uff09\u548czp\uff08\u96f6\u70b9\uff09\u662f\u7531X\u7684\u4e0b\u754cl\u548c\u4e0a\u754cu\u786e\u5b9a\u7684\u91cf\u5316\u53c2\u6570\uff0c\u5b83\u4eec\u901a\u5e38\u662f\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002<\/p>\n

    Log2 Quantization<\/strong><\/p>\n

    Log2 Quantization\u5c06\u91cf\u5316\u8fc7\u7a0b\u4ece\u7ebf\u6027\u53d8\u5316\u8f6c\u6362\u4e3a\u6307\u6570\u53d8\u5316\u3002\u5176\u91cf\u5316\u5668Q\uff08X|b\uff09\u53ef\u5b9a\u4e49\u4e3a:<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u4e3a\u4e86\u5b9e\u73b0\u5b8c\u5168\u91cf\u5316\u7684\u89c6\u89c9\u53d8\u6362\u5668\uff0c\u7814\u7a76\u8005\u5bf9\u6240\u6709\u6a21\u5757\u8fdb\u884c\u91cf\u5316\uff0c\u5305\u62ecConv\u3001Linear\u3001MatMul\u3001LayerNorm\u3001Softmax\u7b49\u3002\u7279\u522b\u662f\uff0c\u5bf9Conv\u3001\u7ebf\u6027\u548cMatMul\u6a21\u5757\u4f7f\u7528\u5747\u5300\u7684Min-Max\u91cf\u5316\uff0c\u5bf9LayerNor\u548cSoftmax\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u3002<\/p>\n

    Power-of-Two Factor for LayerNorm Quantization<\/strong><\/p>\n

    \u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\uff0cLayerNorm\u8ba1\u7b97\u6bcf\u4e2a\u6b63\u5411\u6b65\u9aa4\u4e2d\u7684\u7edf\u8ba1\u91cf\u00b5X\uff0c\u03c3X\uff0c\u5e76\u5bf9\u8f93\u5165X\u8fdb\u884c\u5f52\u4e00\u5316\u3002\u7136\u540e\uff0c\u4eff\u5c04\u53c2\u6570\u03b3\uff0c\u03b2\u5c06\u5f52\u4e00\u5316\u8f93\u5165\u91cd\u65b0\u7f29\u653e\u4e3a\u53e6\u4e00\u4e2a\u5b66\u4e60\u5206\u5e03\u3002<\/p>\n

    \u5982\u521a\u5f00\u59cb\u89e3\u91ca\u5206\u6790\u4e00\u6837\uff0c\u4e0e\u795e\u7ecf\u7f51\u7edc\u4e2d\u5e38\u7528\u7684BatchNorm\u4e0d\u540c\uff0cLayerNorm\u7531\u4e8e\u5176\u52a8\u6001\u8ba1\u7b97\u7279\u6027\uff0c\u65e0\u6cd5\u6298\u53e0\u5230\u524d\u4e00\u5c42\uff0c\u56e0\u6b64\u5fc5\u987b\u5355\u72ec\u91cf\u5316\u5b83\u3002\u7136\u800c\uff0c\u5728\u5bf9\u5176\u5e94\u7528\u8bad\u7ec3\u540e\u91cf\u5316\u65f6\u89c2\u5bdf\u5230\u663e\u8457\u7684\u6027\u80fd\u4e0b\u964d\u3002\u67e5\u770bLayerNorm\u5c42\u7684\u8f93\u5165\uff0c\u53d1\u73b0\u5b58\u5728\u4e25\u91cd\u7684\u901a\u9053\u95f4\u53d8\u5316\u3002<\/p>\n

    \u7814\u7a76\u8005\u63d0\u51fa\u4e86\u4e00\u79cd\u7b80\u5355\u800c\u6709\u6548\u7684\u5c42\u8303\u6570\u91cf\u5316\u65b9\u6cd5\uff0c\u5373Power-of-Two Factor<\/strong>\uff08PTF\uff09\u3002PTF\u7684\u6838\u5fc3\u601d\u60f3\u662f\u4e3a\u4e0d\u540c\u7684\u4fe1\u9053\u914d\u5907\u4e0d\u540c\u7684\u56e0\u5b50\uff0c\u800c\u4e0d\u662f\u4e0d\u540c\u7684\u91cf\u5316\u53c2\u6570\u3002\u7ed9\u5b9a\u91cf\u5316\u4f4d\u5bbdb\uff0c\u8f93\u5165\u6d3b\u52a8X\u2208RB\u00d7L\u00d7C\uff0c\u9010\u5c42\u91cf\u5316\u53c2\u6570s\uff0czp\u2208R1\uff0c\u4ee5\u53caPTF\u03b1\u2208NC\uff0c\u5219\u91cf\u5316\u6d3b\u52a8XQ\u53ef\u4ee5\u516c\u5f0f\u5316\u4e3a\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u5176\u4e2d\u90e8\u5206\u53c2\u6570\u5982\u4e0b\uff1a<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    Softmax quantized with Log-Int-Softmax (LIS)<\/h3>\n

    \u6ce8\u610f\u56fe\u7684\u5b58\u50a8\u548c\u8ba1\u7b97\u662f\u53d8\u538b\u5668\u7ed3\u6784\u7684\u74f6\u9888\uff0c\u56e0\u6b64\u7814\u7a76\u8005\u5e0c\u671b\u5c06\u5176\u91cf\u5316\u5230\u6781\u4f4e\u7684\u4f4d\u5bbd\uff08\u4f8b\u59824\u4f4d\uff09\u3002\u7136\u800c\uff0c\u5982\u679c\u76f4\u63a5\u5b9e\u73b04\u4f4d\u5747\u5300\u91cf\u5316\uff0c\u5219\u4f1a\u51fa\u73b0\u4e25\u91cd\u7684\u7cbe\u5ea6\u9000\u5316\u3002\u7814\u7a76\u8005\u89c2\u5bdf\u5230\u5206\u5e03\u96c6\u4e2d\u5728Softmax\u8f93\u51fa\u7684\u4e00\u4e2a\u76f8\u5f53\u5c0f\u7684\u503c\u4e0a\uff0c\u800c\u53ea\u6709\u5c11\u6570\u5f02\u5e38\u503c\u5177\u6709\u63a5\u8fd11\u7684\u8f83\u5927\u503c\u3002\u57fa\u4e8e\u4ee5\u4e0b\u53ef\u89c6\u5316\uff0c\u5bf9\u4e8e\u5177\u6709\u5bc6\u96c6\u5206\u5e03\u7684\u5c0f\u503c\u533a\u95f4\uff0cLog2\u4fdd\u7559\u4e86\u6bd4\u5747\u5300\u66f4\u591a\u7684\u91cf\u5316\u533a\u95f4\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u5c06Log2\u91cf\u5316\u4e0ei-exp\uff08i-BERT\u63d0\u51fa\u7684\u6307\u6570\u51fd\u6570\u7684\u591a\u9879\u5f0f\u8fd1\u4f3c\uff09\u76f8\u7ed3\u5408\uff0c\u63d0\u51fa\u4e86LIS\uff0c\u8fd9\u662f\u4e00\u4e2a\u4ec5\u6574\u6570\u3001\u66f4\u5feb\u3001\u4f4e\u529f\u8017\u7684Softmax\u3002<\/p>\n

    \u6574\u4e2a\u8fc7\u7a0b\u5982\u4e0b\u6240\u793a\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    03 \u5b9e\u9a8c&\u53ef\u89c6\u5316<\/h3>\n

    Comparison of the top-1 accuracy with state-of-the-art methods on ImageNet dataset<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    \u5c06\u6ce8\u610f\u529b\u56fe\u53ef\u89c6\u5316\uff0c\u4ee5\u67e5\u770b\u5747\u5300\u91cf\u5316\u548cLIS\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u5982\u4e0a\u56fe\u6240\u793a\u3002\u5f53\u4e24\u8005\u90fd\u4f7f\u75288\u4f4d\u65f6\uff0c\u5747\u5300\u91cf\u5316\u96c6\u4e2d\u5728\u9ad8\u6fc0\u6d3b\u533a\u57df\uff0c\u800cLIS\u5728\u4f4e\u6fc0\u6d3b\u533a\u57df\u4fdd\u7559\u66f4\u591a\u7eb9\u7406\uff0c\u8fd9\u4fdd\u7559\u4e86\u6ce8\u610f\u529b\u56fe\u7684\u66f4\u591a\u76f8\u5bf9\u79e9\u3002\u57288\u4f4d\u7684\u60c5\u51b5\u4e0b\uff0c\u8fd9\u79cd\u5dee\u5f02\u4e0d\u4f1a\u4ea7\u751f\u592a\u5927\u7684\u5dee\u5f02\u3002\u7136\u800c\uff0c\u5f53\u91cf\u5316\u5230\u8f83\u4f4e\u7684\u4f4d\u5bbd\u65f6\uff0c\u59826\u4f4d\u548c4\u4f4d\u7684\u60c5\u51b5\u6240\u793a\uff0c\u5747\u5300\u91cf\u5316\u4f1a\u6025\u5267\u9000\u5316\uff0c\u751a\u81f3\u4f7f\u6240\u6709\u5173\u6ce8\u533a\u57df\u5931\u6548\u3002\u76f8\u53cd\uff0cLIS\u4ecd\u7136\u8868\u73b0\u51fa\u7c7b\u4f3c\u4e8e8\u4f4d\u7684\u53ef\u63a5\u53d7\u6027\u80fd\u3002<\/p>\n

    \"\u56fe\u7247\"<\/p>\n

    Channel-wise minimum and maximum values of Vision Transformers and ResNets<\/p>\n<\/div>\n<\/div>\n

    <\/div>\n

    <\/g><\/svg><\/i> \"Loading\"<\/p>\n

    <\/div>\n","protected":false},"excerpt":{"rendered":"

    \u5c06\u7b97\u6cd5\u7f51\u7edc\u8fdb\u884c\u91cf\u5316\u548c\u6a21\u578b\u8f6c\u6362\u53ef\u4ee5\u663e\u7740\u964d\u4f4e\u6a21\u578b\u63a8\u7406\u7684\u590d\u6742\u6027\uff0c\u5e76\u5728\u5b9e\u9645\u90e8\u7f72\u4e2d\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u7136\u800c\uff0c\u5927\u591a\u6570\u73b0\u6709\u7684\u91cf […]<\/p>\n

    <\/div>\n

    <\/g><\/svg><\/i> \"Loading\"<\/p>\n

    <\/div>\n","protected":false},"author":56,"featured_media":21200,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[23,20,80],"tags":[1208],"class_list":["post-21198","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-23","category-20","category-80","tag-1208"],"_links":{"self":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/21198","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/users\/56"}],"replies":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/comments?post=21198"}],"version-history":[{"count":1,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/21198\/revisions"}],"predecessor-version":[{"id":21201,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/21198\/revisions\/21201"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media\/21200"}],"wp:attachment":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media?parent=21198"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/categories?post=21198"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/tags?post=21198"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}