"use strict";(self.webpackChunkrspress_doc_template=self.webpackChunkrspress_doc_template||[]).push([["8786"],{57223:function(e,t,r){r.r(t);var o=r(85893),s=r(50065);function n(e){let t=Object.assign({p:"p",div:"div",ol:"ol",li:"li",strong:"strong",code:"code"},(0,s.ah)(),e.components);return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(t.p,{children:"In the table below:"}),"\n",(0,o.jsx)(t.p,{children:"lhs: left-hand side, the left-hand side in the operation."}),"\n",(0,o.jsx)(t.p,{children:"rhs: right-hand side, the right-hand side in the operation."}),"\n",(0,o.jsxs)(t.div,{className:"rspress-directive info",children:[(0,o.jsx)(t.div,{className:"rspress-directive-title",children:"Note"}),(0,o.jsx)(t.div,{className:"rspress-directive-content",children:(0,o.jsxs)(t.ol,{children:["\n",(0,o.jsxs)(t.li,{children:["The ",(0,o.jsx)(t.strong,{children:"DeformConv"})," operator itself has certain special characteristics.\nWe have implemented direct support for ",(0,o.jsx)(t.code,{children:"torchvision::deformconv"})," through a specific custom operator method.\nPlease contact Horizon for more information."]}),"\n",(0,o.jsxs)(t.li,{children:["The ",(0,o.jsx)(t.strong,{children:"GroupNormalization"})," operator, when exported from the Pytorch framework to ONNX,\nwill be automatically split by the framework into multiple suboperators into the original ONNX model,\nand we support BPU computation for these suboperators, thus implementing support for the GroupNormalization operator."]}),"\n"]})})]})]})}function i(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},{wrapper:t}=Object.assign({},(0,s.ah)(),e.components);return t?(0,o.jsx)(t,Object.assign({},e,{children:(0,o.jsx)(n,e)})):n(e)}t.default=i,i.__RSPRESS_PAGE_META={},i.__RSPRESS_PAGE_META["latest%2Fen%2Fguide%2Fappendix%2Fsupported_op_list%2Foperator_constraints_bpu%2Fonnx_operator_constraints_list_remark_j6em.mdx"]={toc:[],title:"",frontmatter:{}}}}]);