"use strict";(self.webpackChunkrspress_doc_template=self.webpackChunkrspress_doc_template||[]).push([["9429"],{36889:function(e,n,s){s.r(n);var r=s(85893),t=s(50065);function o(e){let n=Object.assign({h1:"h1",a:"a",pre:"pre",code:"code",span:"span",p:"p"},(0,t.ah)(),e.components);return(0,r.jsxs)(r.Fragment,{children:[(0,r.jsxs)(n.h1,{id:"nnpointpillarsscatter",children:[(0,r.jsx)(n.a,{className:"header-anchor","aria-hidden":"true",href:"#nnpointpillarsscatter",children:"#"}),"nn.PointPillarsScatter"]}),"\n",(0,r.jsx)(n.pre,{className:"code",children:(0,r.jsx)(n.pre,{className:"shiki css-variables has-line-number",style:{backgroundColor:"var(--shiki-color-background)"},tabIndex:"0",children:(0,r.jsxs)(n.code,{className:"language-python",meta:"",children:[(0,r.jsxs)(n.span,{className:"line line-number",children:[(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-keyword)"},children:"class"}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-color-text)"},children:" horizon_plugin_pytorch"}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-punctuation)"},children:"."}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-color-text)"},children:"nn"}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-punctuation)"},children:"."}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-function)"},children:"PointPillarsScatter "}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-punctuation)"},children:"(output_shape"}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-keyword)"},children:"="}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-constant)"},children:"None"}),(0,r.jsx)(n.span,{style:{color:"var(--shiki-token-punctuation)"},children:")"})]}),"\n"]})})}),"\n",(0,r.jsx)(n.p,{children:"Convert learned features from dense tensor to sparse pseudo image."}),"\n",(0,r.jsx)(n.p,{children:"This replaces SECOND\u2019s second.pytorch.voxelnet.SparseMiddleExtractor."}),"\n",(0,r.jsx)(n.p,{children:"Parameters: output_shape \u2013 (List[int], optional). RExpected output shape. Defaults to None."}),"\n",(0,r.jsx)(n.p,{children:"forward (voxel_features: Tensor, coords: Tensor, output_shape: Tensor | list | tuple | None = None)"}),"\n",(0,r.jsx)(n.p,{children:"Forward of Horizon PointPillarsScatter."}),"\n",(0,r.jsx)(n.p,{children:"Parameters:"}),"\n",(0,r.jsx)(n.p,{children:"voxel_features (Tensor) \u2013 [M, \u2026], dimention after M will be flattened."}),"\n",(0,r.jsx)(n.p,{children:"coords (Tensor) \u2013 [M, (n, \u2026, y, x)], only indices on N, H and W are used."}),"\n",(0,r.jsx)(n.p,{children:"output_shape (Union[Tensor, list, tuple, None]) \u2013 Expected output shape. Defaults to None."}),"\n",(0,r.jsx)(n.p,{children:"Returns: The NCHW pseudo image."}),"\n",(0,r.jsx)(n.p,{children:"Return type: Tensor"})]})}function a(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},{wrapper:n}=Object.assign({},(0,t.ah)(),e.components);return n?(0,r.jsx)(n,Object.assign({},e,{children:(0,r.jsx)(o,e)})):o(e)}n.default=a,a.__RSPRESS_PAGE_META={},a.__RSPRESS_PAGE_META["latest%2Fen%2Fguide%2Fplugin%2Fplugin_api_reference%2Fhorizon_operator%2Fhorizon_plugin_pytorch_nn_PointPillarsScatter.md"]={toc:[],title:"nn.PointPillarsScatter",frontmatter:{}}}}]);