5002 lines
1.2 MiB
5002 lines
1.2 MiB
/*
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Face-API
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homepage: <https://github.com/vladmandic/face-api>
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author: <https://github.com/vladmandic>'
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*/
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n={},o={};for(let u=0;u<t.length;u++)n[t[u].id]=!0;for(let u=0;u<r.length;u++){let l=r[u],c=l.inputs;for(let p in c){let m=c[p],f=!1;for(let d=0;d<t.length;d++)if(n[m.id]){l.outputs.forEach(h=>n[h.id]=!0),f=!0,o[l.id]=!0;break}if(f)break}}let s={};s[e.id]=!0;let i={};for(let u=r.length-1;u>=0;u--){let l=r[u],c=l.inputs;for(let p=0;p<l.outputs.length;p++)if(s[l.outputs[p].id]){for(let m in c)s[c[m].id]=!0,i[l.id]=!0;break}}let a=[];for(let u=0;u<r.length;u++){let l=r[u];if(o[l.id]&&i[l.id]){let c={};for(let m in l.inputs){let f=l.inputs[m];n[f.id]&&(c[m]=f)}let p=Object.assign({},l);p.inputs=c,p.outputs=l.outputs,a.push(p)}}return a}function O_(r,t,e,n){for(let o=t.length-1;o>=0;o--){let s=t[o],i=[];if(s.outputs.forEach(u=>{let l=r[u.id];l!=null?i.push(l):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let a=s.gradient(i);for(let u in s.inputs){if(!(u in a))throw new Error(`Cannot backprop through input ${u}. 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|
|
`)),u.join(`
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y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel(nh,o)}var yE=k({bitwiseAnd_:oj});function sj(r,t){let e=v(r,"s0","broadcastArgs","int32"),n=v(t,"s1","broadcastArgs","int32");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return T.runKernel(Yl,o)}var bE=k({broadcastArgs_:sj});function ij(r,t){let e=v(r,"broadcastTo","x"),n=e.shape;if(Me(t),t.length<e.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${e.rank}.`);if(t.length>e.rank){let l=e.shape.slice();for(;l.length<t.length;)l.unshift(1);e=R(e,l)}let o=e.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return un(e);let a={x:e},u={reps:s};return T.runKernel(so,a,u)}var oa=k({broadcastTo_:ij});function aj(r){let e={x:v(r,"x","ceil","float32")};return T.runKernel(es,e)}var Tx=k({ceil_:aj});function vo(r,t,e){Me(r),e=e||jl(t);let n={shape:r,value:t,dtype:e};return T.runKernel(ru,{},n)}function lj(r,t,e){let n=v(r,"x","clipByValue");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return vo(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return T.runKernel(go,o,s)}var vr=k({clipByValue_:lj});function uj(r){return se(r,0)}var _x=k({concat1d_:uj});function cj(r,t){return se(r,t)}var Ex=k({concat2d_:cj});function pj(r,t){return se(r,t)}var Ax=k({concat3d_:pj});function mj(r,t){return se(r,t)}var Dx=k({concat4d_:mj});function fj(r,t,e,n,o="NHWC",s=[1,1],i){let a=v(r,"x","conv2d","float32"),u=v(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),Se("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),_($r(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),_(ea(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),_(ea(e),()=>"Error in conv2D: Strides should be larger than 0.");let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=T.runKernel(rs,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var kn=k({conv2d_:fj});function dj(r,t,e,n,o="NWC",s=1,i){let a=v(r,"x","conv1d"),u=v(t,"filter","conv1d"),l=a,c=!1;a.rank===2&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1]])),_(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),_(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),Se("conv1d",n,i),_(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),_($r(e,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`),_(ea(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),_(ea(e),()=>"Error in conv1D: Stride should be larger than 0."),_(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=kn(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var um=k({conv1d_:dj});function hj(r,t,e,n,o,s="NHWC",i){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let a=r,u=t,l=!1;t.rank===3&&(l=!0,u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]),a=[1,r[0],r[1],r[2]]),_(a.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${a.length}.`),_(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),_(e.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`);let c=s==="NHWC"?a[3]:a[1],p=s==="NHWC"?u.shape[3]:u.shape[1];_(c===e.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`),_(p===e.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${e.shape[3]}.`),Se("conv2dDerInput",o,i);let m={dy:u,filter:e},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,inputShape:a},d=T.runKernel(ns,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var cm=k({conv2DBackpropInput_:hj});function gj(r,t,e,n,o,s){let i=v(r,"x","conv2dTranspose"),a=v(t,"filter","conv2dTranspose");return cm(e,i,a,n,o,"NHWC",s)}var pm=k({conv2dTranspose_:gj});function xj(r,t,e,n,o="NDHWC",s=[1,1,1]){let i=v(r,"x","conv3d"),a=v(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),_(a.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${a.rank}.`),_(u.shape[4]===a.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${a.shape[3]}.`),_($r(e,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),_(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`),_(ea(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),_(ea(e),()=>"Error in conv3D: Strides should be larger than 0.");let c={x:u,filter:a},p={strides:e,pad:n,dataFormat:o,dilations:s},m=T.runKernel(os,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var $x=k({conv3d_:xj});function yj(r,t,e,n,o){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let s=r,i=t,a=!1;t.rank===4&&(a=!0,i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let u=s[4],l=i.shape[4];_(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),_(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),_(e.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${e.rank}`),_(u===e.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${e.shape[3]}.`),_(l===e.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[4]}.`);let c={dy:i,filter:e},p={pad:o,strides:n,inputShape:s},m=T.runKernel(Ra,c,p);return a?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Rx=k({conv3DBackpropInput_:yj});function bj(r,t,e,n,o){let s=v(r,"x","conv3dTranspose"),i=v(t,"filter","conv3dTranspose");return Rx(e,s,i,n,o)}var Fx=k({conv3dTranspose_:bj});function wj(r){let e={x:v(r,"x","cos","float32")};return T.runKernel(ss,e)}var Iu=k({cos_:wj});function Ij(r){let e={x:v(r,"x","cosh","float32")};return T.runKernel(is,e)}var mm=k({cosh_:Ij});function Cj(r,t=0,e=!1,n=!1){let s={x:v(r,"x","cumprod")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(Fa,s,i)}var gc=k({cumprod_:Cj});function vj(r,t=0,e=!1,n=!1){let s={x:v(r,"x","cumsum")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(as,s,i)}var fm=k({cumsum_:vj});function Sj(r,t,e,n=!1){let o=v(r,"x","denseBincount"),s=v(t,"weights","denseBincount");_(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(Jl,i,a)}var gh=k({denseBincount_:Sj});function Nj(r,t,e="NHWC"){let n=v(r,"x","depthToSpace","float32"),o=e==="NHWC"?n.shape[1]:n.shape[2],s=e==="NHWC"?n.shape[2]:n.shape[3],i=e==="NHWC"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),_(o*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${o} and ${t} for depthToSpace with input shape
|
|
${n.shape}`),_(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${t} for depthToSpace with input shape
|
|
${n.shape}`),_(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${n.shape}`);let a={x:n},u={blockSize:t,dataFormat:e};return T.runKernel(Pa,a,u)}var Ox=k({depthToSpace_:Nj});function kj(r,t,e,n,o="NHWC",s=[1,1],i){let a=v(r,"x","depthwiseConv2d","float32"),u=v(t,"filter","depthwiseConv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${p}) must match the inChannels dimension in filter ${u.shape[2]}.`),Se("depthwiseConv2d",n,i);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=T.runKernel(ls,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var sa=k({depthwiseConv2d_:kj});function Tj(r){let e={x:v(r,"x","diag")};return T.runKernel(Ql,e)}var wE=k({diag_:Tj});function _j(r,t,e,n,o=[1,1],s="NHWC"){let i=v(r,"x","dilation2d"),a=v(t,"filter","dilation2d");_(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),_(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),_(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0),_(u.shape[3]===a.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${u.shape[3]} vs ${a.shape[2]}`);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=T.runKernel(us,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Px=k({dilation2d_:_j});var Ur={};Kt(Ur,{assertAndGetBroadcastShape:()=>Mt,getBroadcastDims:()=>IE,getReductionAxes:()=>ye});function IE(r,t){let e=r.length,n=[];for(let o=0;o<e;o++){let s=e-1-o,i=r[s]||1;(t[t.length-1-o]||1)>1&&i===1&&n.unshift(s)}return n}function ye(r,t){let e=[];for(let n=0;n<t.length;n++){let o=r[r.length-n-1],s=t.length-n-1,i=t[s];(o==null||o===1&&i>1)&&e.unshift(s)}return e}function Mt(r,t){let e=Math.max(r.length,t.length),n=new Array(e);for(let o=0;o<e;o++){let s=r[r.length-o-1];s==null&&(s=1);let i=t[t.length-o-1];if(i==null&&(i=1),s===1)n[e-o-1]=i;else if(i===1)n[e-o-1]=s;else if(s!==i){let a=`Operands could not be broadcast together with shapes ${r} and ${t}.`;throw Error(a)}else n[e-o-1]=s}return n}function Ej(r,t){let e=v(r,"a","equal","string_or_numeric"),n=v(t,"b","equal","string_or_numeric");[e,n]=jt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(za,o)}var Rr=k({equal_:Ej});function Aj(r,t,e){let n=v(t,"a","where"),o=v(e,"b","where"),s=v(r,"condition","where","bool"),i=Mt(Mt(s.shape,n.shape),o.shape),a=oa(s,i),u=oa(n,i),l=oa(o,i),c={condition:a,t:u,e:l};return T.runKernel(Vi,c)}var be=k({where_:Aj});function Dj(r){let e={x:v(r,"x","zerosLike")};return T.runKernel(qi,e)}var vt=k({zerosLike_:Dj});function $j(r,t){let e=v(r,"a","div"),n=v(t,"b","div");[e,n]=jt(e,n);let o=ct(e,n),s=vt(o),i=Rr(n,s);return be(i,s,o)}var Mx=k({divNoNan_:$j});function Rj(r,t){let e=v(r,"t1","dot"),n=v(t,"t2","dot");_((e.rank===1||e.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(_(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),e.rank===1&&n.rank===1){let i=R(e,[1,-1]),a=R(n,[-1,1]),u=Bt(i,a);return R(u,[])}else if(e.rank===1&&n.rank===2){let i=R(e,[1,-1]),a=R(n,[n.shape[0],n.shape[1]]),u=Bt(i,a);return R(u,[u.size])}else if(e.rank===2&&n.rank===1){let i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var Lx=k({dot_:Rj});function Fj(r,...t){let e=t.map((o,s)=>v(o,`tensors${s}`,"einsum")),n={equation:r};return T.runKernel(zp,e,n)}var CE=k({einsum_:Fj});function Oj(r){let e={x:v(r,"x","elu","float32")};return T.runKernel(ps,e)}var ia=k({elu_:Oj});function Pj(r,t){let e=v(r,"x","ensureShape","string_or_numeric");if(!n0(e.shape,t))throw new Error(`EnsureShape: Shape of tensor ${e.shape} is not compatible with expected shape ${t}`);return r}var vE=k({ensureShape_:Pj});function Mj(r){let t=v(r,"x","erf");_(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=Q(t,"float32"));let e={x:t};return T.runKernel(La,e)}var zx=k({erf_:Mj});function U0(r,t){for(let e=0;e<r.length;++e)if(r[r.length-e-1]!==t-1-e)return!1;return!0}function SE(r,t,e){let n=r.length+t.length,o=[],s=0,i=0;for(let a=0;a<n;a++)e.indexOf(a)===-1?o.push(r[s++]):o.push(t[i++]);return o}function H0(r,t){let e=[],n=r.length;for(let s=0;s<n;s++)t.indexOf(s)===-1&&e.push(r[s]);let o=t.map(s=>r[s]);return[e,o]}function So(r,t){let e=t.map(n=>1);return SE(r,e,t)}function Lj(r,t,e){_(U0(t,e),()=>`${r} supports only inner-most axes for now. 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e=v(r,"a","lessEqual","string_or_numeric"),n=v(t,"b","lessEqual","string_or_numeric");[e,n]=jt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Ua,o)}var Gn=k({lessEqual_:u6});function kE(r,t,e){if(e<=0)throw new Error("The number of values should be positive.");let n={start:r,stop:t,num:e};return T.runKernel(Ha,{},n)}function c6(r,t=5,e=1,n=1,o=.5){let s=v(r,"x","localResponseNormalization");_(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
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rank ${s.rank}.`),_(_a(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,a=!1;s.rank===3&&(a=!0,i=R(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let u={x:i},l={depthRadius:t,bias:e,alpha:n,beta:o},c=T.runKernel(Ss,u,l);return a?R(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Hx=k({localResponseNormalization_:c6});function p6(r){let e={x:v(r,"x","log","float32")};return T.runKernel(Cs,e)}var Nr=k({log_:p6});function m6(r){let e={x:v(r,"x","log1p")};return T.runKernel(vs,e)}var Su=k({log1p_:m6});function f6(r){return _(ki(r),()=>"The f passed in grad(f) must be a function"),(t,e)=>{let n=v(t,"x","tf.grad","string_or_numeric"),o=e!=null?v(e,"dy","tf.grad"):null;return T.tidy(()=>{let{value:s,grads:i}=T.gradients(()=>r(n),[n],o);return o!=null&&Re(s.shape,o.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Kx(i),i[0]})}}function d6(r){return _(ki(r),()=>"The f passed in grads(f) must be a function"),(t,e)=>{_(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let n=hl(t,"args","tf.grads","string_or_numeric"),o=e!=null?v(e,"dy","tf.grads"):null;return T.tidy(()=>{let{value:s,grads:i}=T.gradients(()=>r(...n),n,o);return o!=null&&Re(s.shape,o.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Kx(i),i})}}function h6(r){return _(ki(r),()=>"The f passed in valueAndGrad(f) must be a function"),(t,e)=>{_(t instanceof Pt,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),_(e==null||e instanceof Pt,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:n,value:o}=T.gradients(()=>r(t),[t],e);return Kx(n),{grad:n[0],value:o}}}function g6(r){return _(ki(r),()=>"The f passed in valueAndGrads(f) must be a function"),(t,e)=>{_(Array.isArray(t)&&t.every(o=>o instanceof Pt),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),_(e==null||e instanceof Pt,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let n=T.gradients(()=>r(...t),t,e);return e!=null&&Re(n.value.shape,e.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Kx(n.grads),n}}function qx(r,t){_(ki(r),()=>"The f passed in variableGrads(f) must be a function"),_(t==null||Array.isArray(t)&&t.every(l=>l instanceof dl),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let e=t!=null;if(!e){t=[];for(let l in T.registeredVariables)t.push(T.registeredVariables[l])}let n=e?t.filter(l=>!l.trainable):null,o=t.length;t=t.filter(l=>l.trainable),_(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${o} variables is trainable.`);let s=!0,{value:i,grads:a}=T.gradients(r,t,null,s);_(a.some(l=>l!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),_(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let u={};return t.forEach((l,c)=>{a[c]!=null&&(u[l.name]=a[c])}),n!=null&&n.forEach(l=>u[l.name]=null),{value:i,grads:u}}function mn(r){return T.customGrad(r)}function Kx(r){if(r.filter(e=>e==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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the f you passed encloses all operations that lead from x to y.`)}function x6(r){let e={x:v(r,"x","neg")};return T.runKernel(Mi,e)}var Ut=k({neg_:x6});function y6(r){let e={x:v(r,"x","softplus")};return T.runKernel(Ys,e)}var ai=k({softplus_:y6});function b6(r){let t=v(r,"x","logSigmoid");return mn(n=>({value:Ut(ai(Ut(n))),gradFunc:i=>$(i,tn(Ut(n)))}))(t)}var jx=k({logSigmoid_:b6});function w6(r,t){let e=v(r,"a","sub"),n=v(t,"b","sub");[e,n]=jt(e,n);let o={a:e,b:n};return T.runKernel(ei,o)}var lt=k({sub_:w6});function I6(r,t=-1){let e=v(r,"logits","logSoftmax");if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. 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Actual: ${o}.
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Expected: ${s}.`);for(let i=0;i<s.length;++i){let a=o[i],u=s[i];if(!e(a,u))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${u}.
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Actual: ${o}.
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l={boxes:i,scores:a},c={maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=T.runKernel(el,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var PA=k({nonMaxSuppressionWithScore_:H5});async function q5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=v(r,"boxes","nonMaxSuppressionAsync"),a=v(t,"scores","nonMaxSuppressionAsync"),u=ko(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=vy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(m,"int32"),selectedScores:Ke(f)}}var MA=q5;function K5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=v(r,"boxes","nonMaxSuppression"),a=v(t,"scores","nonMaxSuppression"),u=ko(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(tl,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var LA=k({nonMaxSuppressionPadded_:K5});async function j5(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=v(r,"boxes","nonMaxSuppressionAsync"),a=v(t,"scores","nonMaxSuppressionAsync"),u=ko(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=Cy(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Ke(d,"int32"),validOutputs:ft(h,"int32")}}var zA=j5;function X5(r,t,e=!1,n=!1){let o=v(r,"images","resizeBilinear");_(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeBilinear: new 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o=v(r,"labels","absoluteDifference"),s=v(t,"predictions","absoluteDifference"),i=null;e!=null&&(i=v(e,"weights","absoluteDifference")),Re(o.shape,s.shape,"Error in absoluteDifference: ");let a=Ee(lt(o,s));return Hr(a,i,n)}var qA=k({absoluteDifference_:o8});function s8(r,t,e,n,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),i=v(t,"predictions","cosineDistance"),a=null;n!=null&&(a=v(n,"weights","cosineDistance")),Re(s.shape,i.shape,"Error in cosineDistance: ");let u=ft(1),l=lt(u,pt($(s,i),e,!0));return Hr(l,a,o)}var KA=k({cosineDistance_:s8});function i8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","hingeLoss"),s=v(t,"predictions","hingeLoss"),i=null;e!=null&&(i=v(e,"weights","hingeLoss")),Re(o.shape,s.shape,"Error in hingeLoss: ");let a=ft(1);o=lt($(ft(2),o),a);let u=Pr(lt(a,$(o,s)));return Hr(u,i,n)}var jA=k({hingeLoss_:i8});function a8(r,t,e,n=1,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),i=v(t,"predictions","huberLoss"),a=null;e!=null&&(a=v(e,"weights","huberLoss")),Re(s.shape,i.shape,"Error in huberLoss: ");let u=ft(n),l=Ee(lt(i,s)),c=uo(l,u),p=lt(l,c),m=Y($(ft(.5),Wt(c)),$(u,p));return Hr(m,a,o)}var XA=k({huberLoss_:a8});function l8(r,t,e,n=1e-7,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),i=v(t,"predictions","logLoss"),a=null;e!=null&&(a=v(e,"weights","logLoss")),Re(s.shape,i.shape,"Error in logLoss: ");let u=ft(1),l=ft(n),c=Ut($(s,Nr(Y(i,l)))),p=$(lt(u,s),Nr(Y(lt(u,i),l))),m=lt(c,p);return Hr(m,a,o)}var YA=k({logLoss_:l8});function u8(r,t,e,n=Ze.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","meanSquaredError"),s=v(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=v(e,"weights","meanSquaredError")),Re(o.shape,s.shape,"Error in meanSquaredError: ");let a=Tm(o,s);return Hr(a,i,n)}var ZA=k({meanSquaredError_:u8});function c8(r,t){let e=v(r,"labels","sigmoidCrossEntropyWithLogits"),n=v(t,"logits","sigmoidCrossEntropyWithLogits");Re(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Pr(n),s=$(n,e),i=Su(ir(Ut(Ee(n))));return Y(lt(o,s),i)}function p8(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),i=v(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=v(e,"weights","sigmoidCrossEntropy")),Re(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=ft(n),c=ft(1),p=ft(.5);s=Y($(s,lt(c,l)),$(p,l))}let u=c8(s,i);return Hr(u,a,o)}var JA=k({sigmoidCrossEntropy_:p8});function m8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${o.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(a.rank!==0)throw new Error(`Default value should be a scalar but received shape ${a.shape}`);let u={indices:o,values:s,denseShape:i,defaultValue:a},l=T.runKernel(su,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var t2=k({sparseFillEmptyRows_:d8});function h8(r,t,e){let n=v(r,"inputIndices","sparseReshape","int32"),o=v(t,"inputShape","sparseReshape","int32"),s=v(e,"newShape","sparseReshape","int32");if(n.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${n.shape}`);if(o.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${o.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(ll,i);return{outputIndices:a[0],outputShape:a[1]}}var e2=k({sparseReshape_:h8});function g8(r,t,e){let n=v(r,"data","sparseSegmentMean"),o=v(t,"indices","sparseSegmentMean","int32"),s=v(e,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(iu,i)}var r2=k({sparseSegmentMean_:g8});function x8(r,t,e){let n=v(r,"data","sparseSegmentSum"),o=v(t,"indices","sparseSegmentSum","int32"),s=v(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(au,i)}var n2=k({sparseSegmentSum_:x8});function y8(r,t,e,n,o,s,i,a){let u=v(r,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=v(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:e,nGramWidths:n,leftPad:o,rightPad:s,padWidth:i,preserveShortSequences:a},p={data:u,dataSplits:l},m=T.runKernel(uu,p,c);return{nGrams:m[0],nGramsSplits:m[1]}}var o2=k({stringNGrams_:y8});function b8(r,t,e=!0){let n=v(r,"input","stringSplit","string"),o=v(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(o.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${o.shape}`);let s={skipEmpty:e},i={input:n,delimiter:o},a=T.runKernel(cu,i,s);return{indices:a[0],values:a[1],shape:a[2]}}var s2=k({stringSplit_:b8});function w8(r,t){let e=v(r,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let o={input:e};return T.runKernel(pu,o,n)}var i2=k({stringToHashBucketFast_:w8});function I8(r,t,e,n=!0){let o=v(r,"input","staticRegexReplace","string"),s={pattern:t,rewrite:e,replaceGlobal:n};return T.runKernel(sc,{x:o},s)}var a2=k({staticRegexReplace_:I8});var 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setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var _c=class extends qr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>vo(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let 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g=Y($(ct(o,n),ct(m,Y(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(Y(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Cl=class extends qr{static get 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s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=Y($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=Y($(p,this.decay),$(a,1-this.decay)),f=ct($(a,this.learningRate),Ne(lt(c,Y(Wt(m),this.epsilon)))),d=Y($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=lt(s,d);s.assign(h)}else{let p=Y($(u,this.decay),$(Wt(a),1-this.decay)),m=Y($(l,this.momentum),ct($(a,this.learningRate),Ne(Y(p,this.epsilon))));u.assign(p),l.assign(m);let f=lt(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let 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indices.shape[0] = ${r}`}function MY(r,t){return`indices(${r}, 0) is invalid: ${t} < 0`}function LY(r,t,e){return`indices(${r}, 0) is invalid: ${t} >= ${e}`}function zY(r,t){return`only one output dimension may be -1, not both ${r} and ${t}`}function BY(r,t){return`size ${r} must be non-negative, not ${t}`}function VY(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function GY(r,t){let e=te(r),n=te(t);return`Input to reshape is a SparseTensor with ${e}
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dense values, but the requested shape requires a multiple of ${n}. inputShape=${r} outputShape= ${t}`}function WY(r,t){let e=te(r),n=te(t);return`Input to reshape is a tensor with ${e} dense values, but the requested shape has ${n}. inputShape=${r} outputShape=${t}`}function UY(){return"segment ids must be >= 0"}function HY(){return"segment ids are not increasing"}function qY(r,t){return`Segment id ${r} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function KY(r,t,e){return`Bad: indices[${r}] == ${t} out of range [0, ${e})`}var bN={};Kt(bN,{collectGatherOpShapeInfo:()=>YY,computeOutShape:()=>XY,segOpComputeOptimalWindowSize:()=>jY});function jY(r,t){let e=!1,n;for(r<=$y?(n=r,e=!0):n=$p(r,Math.floor(Math.sqrt(r)));!e;)n>t||n===r?e=!0:n=$p(r,n+1);return n}function XY(r,t,e){let n=[],o=r.length;for(let s=0;s<o;s++)s!==t?n.push(r[s]):n.push(e);return n}function YY(r,t,e,n){let o=t.shape.length,s=r.shape.length;if(n!==0&&(n<-o||n>o))throw new Error(`Expect batchDims in the range of [-${o}, ${o}], but got ${n}`);if(n<0&&(n+=o),n>s)throw new Error(`batchDims (${n}) must be less than rank(x) (
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this.fitLoop(w,b,I,f,n.epochs,n.verbose,A,N,x,n.shuffle,E,n.initialEpoch,null,null)}finally{this.isTraining=!1,Fo(o,t),Fo(s,e),Fo(i,t),Fo(a,e),Fo(c,u),Fo(p,l),m!=null&&Tt(m)}}async fitLoop(t,e,n,o,s,i,a,u,l,c,p,m,f,d){o==null&&(o=32),s==null&&(s=1),c==null&&(c=!0),m==null&&(m=0);let h=!1;if(u!=null&&l!=null&&(h=!0),d!=null&&(h=!0,f==null))throw new z("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=this.checkNumSamples(e,o,f,"steps_per_epoch"),x;g!=null&&(x=gn(0,g)),i==null&&(i=1);let{callbackList:b,history:w}=tb(a,i,s,m,g,f,o,h,p);b.setModel(this),this.history=w,await b.onTrainBegin(),this.stopTraining_=!1;for(let I=m;I<s;++I){await b.onEpochBegin(I);let N={};if(f!=null)throw new Nt("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new Nt("batch shuffling is not implemneted yet");c&&y.shuffle(x);let E=Ke(x),A=pb(g,o);for(let D=0;D<A.length;++D){let F={};if(await b.onBatchBegin(D,F),B(()=>{let 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this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=e}else y.assert(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof xa))throw new Nt(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=In(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};xa.className="Sequential";J.registerClass(xa);function T7(r){return new qn(r)}function _7(r){return new xa(r)}function BN(r){return qy(r)}function E7(r,t){wn.registerCallbackConstructor(r,t)}var nn=class extends J.Serializable{getConfig(){return{}}},fb=class extends nn{apply(t,e=1){return U$(t,e)}};fb.className="elu";J.registerClass(fb);var db=class extends nn{apply(t){return wm(t)}};db.className="selu";J.registerClass(db);var hb=class extends nn{apply(t){return Pr(t)}};hb.className="relu";J.registerClass(hb);var gb=class extends nn{apply(t){return B(()=>uo(6,Pr(t)))}};gb.className="relu6";J.registerClass(gb);var xb=class extends nn{apply(t){return t}};xb.className="linear";J.registerClass(xb);var yb=class extends nn{apply(t){return tn(t)}};yb.className="sigmoid";J.registerClass(yb);var bb=class extends 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t={};return t.className=r,t.config={},VN(t)}else return r instanceof nn?r:VN(r)}function GN(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var kb=class extends J.Serializable{},Lu=class extends kb{constructor(t){super(),GN(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Pc(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";J.registerClass(Lu);function TR(r){return GN(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function _R(r){return GN(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var NR={l1l2:"L1L2"};function me(r){return Rm(r)}function kR(r,t={}){return fa(r,J.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ce(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in NR?NR[r]:r,config:{}};return kR(e)}else return r instanceof kb?r:kR(r)}var nf=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Pr(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};nf.className="ReLU";J.registerClass(nf);var of=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return vu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};of.className="LeakyReLU";J.registerClass(of);var sf=class extends 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t={alphaInitializer:_e(this.alphaInitializer),alphaRegularizer:me(this.alphaRegularizer),alphaConstraint:Be(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}};sf.className="PReLU";J.registerClass(sf);var af=class extends _t{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA=1,t==null&&(t={}),t.alpha!=null&&t.alpha!==this.DEFAULT_ALPHA)throw new Nt(`Non-default alpha value (${t.alpha}) is not supported by the ELU layer yet.`);this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return ia(n)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};af.className="ELU";J.registerClass(af);var lf=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_THETA=1,t==null&&(t={}),this.theta=t.theta==null?this.DEFAULT_THETA:t.theta}call(t,e){let n=St(t);return $(n,Q(Fe(n,this.theta),"float32"))}computeOutputShape(t){return t}getConfig(){let t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}};lf.className="ThresholdedReLU";J.registerClass(lf);var uf=class extends _t{constructor(t){super(t==null?{}:t),this.DEFAULT_AXIS=1,t==null&&(t={}),this.softmax=new rf().apply,this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis}call(t,e){let n=St(t);return this.softmax(n,this.axis)}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};uf.className="Softmax";J.registerClass(uf);function zu(r,t,e){if(typeof r=="number")return _o(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:di(this.activation),useBias:this.useBias,biasInitializer:_e(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},Bu=class extends Kc{constructor(t,e){super(t,e),this.kernel=null,Bu.verifyArgs(e),this.filters=e.filters,Qe(this.filters,"filters"),this.kernelInitializer=he(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ve(e.kernelConstraint),this.kernelRegularizer=Ce(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Py(this.activation.getClassName());if(s!=null&&this.rank===2)n=ER(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=D7(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=ER(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=$7(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Nt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<n.length;++s){let i=En(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(i)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:_e(this.kernelInitializer),kernelRegularizer:me(this.kernelRegularizer),kernelConstraint:Be(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}},El=class extends Bu{constructor(t){super(2,t),El.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};El.className="Conv2D";J.registerClass(El);var Al=class extends Bu{constructor(t){super(3,t),Al.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Al.className="Conv3D";J.registerClass(Al);var cf=class extends El{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=gi(u,m,c,this.padding),h=gi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=pm(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=gi(e[o],u,i,this.padding),e[s]=gi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};cf.className="Conv2DTranspose";J.registerClass(cf);var pf=class extends Al{constructor(t){if(super(t),this.inputSpec=[new Ie({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=gi(l,h,m,this.padding),w=gi(c,g,f,this.padding),I=gi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Fx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=gi(e[o],c,a,this.padding),e[s]=gi(e[s],p,u,this.padding),e[i]=gi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className="Conv3DTranspose";J.registerClass(pf);var Tb=class extends Bu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let n=t[e],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let a=0;a<this.rank;++a)s.push(1);s.push(n*this.depthMultiplier,this.filters);let i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new Ie({ndim:this.rank+2,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{t=St(t);let n;if(this.rank===1)throw new Nt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=Im(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.pointwiseInitializer=_e(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseConstraint),t.pointwiseConstraint=Be(this.pointwiseConstraint),t}};Tb.className="SeparableConv";var mf=class extends Tb{constructor(t){super(2,t)}};mf.className="SeparableConv2D";J.registerClass(mf);var Vu=class extends Bu{constructor(t){super(1,t),Vu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Oy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Vu.className="Conv1D";J.registerClass(Vu);var ff=class extends _t{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=Ah(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ah(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ah(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ah(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};ff.className="Cropping2D";J.registerClass(ff);var df=class extends _t{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,L$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};df.className="UpSampling2D";J.registerClass(df);function R7(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Oe(o);let i=Bh(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=sa(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var hf=class extends Kc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=he(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ve(t.depthwiseConstraint),this.depthwiseRegularizer=Ce(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=R7(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=En(e,this.kernelSize[0],this.padding,this.strides[0]),i=En(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=_e(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=Be(this.depthwiseRegularizer),t}};hf.className="DepthwiseConv2D";J.registerClass(hf);function UN(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function HN(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new Nt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Q(Q(o,"bool"),"float32"),o.rank===u-1&&(o=ar(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;x<f;++x){let b=d[x],w=B(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(w[0],N),$(m[0],E)),D=m.map((F,P)=>Y($(w[1][P],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=qe(c,1)),[p,g,m]})}var An=class extends _t{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Yc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ie({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Hy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;n<t;++n)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){if(this.numConstants!=null)throw new Nt("Constants support is not implemented in RNN yet.");Hy(t)&&(t=t[0]),t=t;let n=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new Ie({shape:[n,null,...o]});let s=[t[0]].concat(t.slice(2));this.cell.build(s);let i;if(Array.isArray(this.cell.stateSize)?i=this.cell.stateSize:i=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(a=>a.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ie({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new _n("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_=[Te([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>Te([n,o])):this.states_[0]=Te([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],i=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,a=[n,i];if(!y.arraysEqual(s.shape,a))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>$e(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=UN(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ie({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof rn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=HN((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=Te(t.shape);return e=pt(e,[1,2]),e=kl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Gy(e,[1,n]):e):this.cell.stateSize>1?[Gy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===An.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=In(o,n);return new t(Object.assign(e,{cell:s}))}};An.className="RNN";J.registerClass(An);var Dl=class extends _t{},jc=class extends Dl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Rl({ones:()=>Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Rl({ones:()=>Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=$o($(t,i),this.kernel.read()):s=$o(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,$o(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:di(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};jc.className="SimpleRNNCell";J.registerClass(jc);var gf=class extends An{constructor(t){t.cell=new jc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};gf.className="SimpleRNN";J.registerClass(gf);var Xc=class extends Dl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=hi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Rl({ones:()=>Ir(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Rl({ones:()=>Ir(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0<this.dropout&&this.dropout<1&&(t=$(t,s[0]));let c=$o(t,this.kernel.read());this.useBias&&(c=yn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,i[0]));let p=this.recurrentKernel.read(),[m,f]=gr(p,[2*this.units,this.units],p.rank-1),d=$o(o,m),[h,g,x]=gr(c,3,c.rank-1),[b,w]=gr(d,2,d.rank-1);a=this.recurrentActivation.apply(Y(h,b)),u=this.recurrentActivation.apply(Y(g,w));let I=$o($(u,o),f);l=this.activation.apply(Y(x,I));let N=Y($(a,o),$(Y(1,Ut(a)),l));return[N,N]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:di(this.activation),recurrentActivation:di(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},t),e)}};Xc.className="GRUCell";J.registerClass(Xc);var xf=class extends An{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Xc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};xf.className="GRU";J.registerClass(xf);var $l=class extends Dl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=hi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=he(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ce(t.kernelRegularizer),this.recurrentRegularizer=Ce(t.recurrentRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.kernelConstraint=Ve(t.kernelConstraint),this.recurrentConstraint=Ve(t.recurrentConstraint),this.biasConstraint=Ve(t.biasConstraint),this.dropout=Oc([1,mi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Oc([1,mi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Pu().apply([i]),m=s.apply([i*2]);return AN(AN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Rl({ones:()=>Ir(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Rl({ones:()=>Ir(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0<this.dropout&&this.dropout<1&&(t=$(t,i[0]));let m=$o(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,a[0])),m=Y(m,$o(o,this.recurrentKernel.read())),this.useBias&&(m=yn(m,this.bias.read()));let[f,d,h,g]=gr(m,4,m.rank-1);u=this.recurrentActivation.apply(f),l=this.recurrentActivation.apply(d),c=Y($(l,s),$(u,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=$(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:di(this.activation),recurrentActivation:di(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),recurrentInitializer:_e(this.recurrentInitializer),biasInitializer:_e(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),recurrentConstraint:Be(this.recurrentConstraint),biasConstraint:Be(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},t),e)}};$l.className="LSTMCell";J.registerClass($l);var yf=class extends An{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new $l(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className="LSTM";J.registerClass(yf);var Yc=class extends Dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a<this.cells.length;++a){let u=this.cells[a];n=o[a],a===0?i=[t[0]].concat(n):i=[i[0]].concat(n),i=u.call(i,e),s.push(i.slice(1))}n=[];for(let a of s.slice().reverse())n.push(...a);return[i[0]].concat(n)})}build(t){Hy(t)&&(t=t[0]),t=t;let e;this.cells.forEach((n,o)=>{pi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(In(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return $h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;i<n.weights.length;++i)e.push([n.weights[i],s[i]])}qm(e)}};Yc.className="StackedRNNCells";J.registerClass(Yc);function Rl(r){let{ones:t,rate:e,training:n=!1,count:o=1,dropoutFunc:s}=r,i=()=>s!=null?s(t(),e):Uy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?$e(a().clone()):Array(o).fill(void 0).map(a).map(l=>$e(l.clone()))}var F7=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)t.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(e[n[o]]=r[n[o]]);return e};var _b=class extends An{constructor(t){if(t.unroll)throw new Nt("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Nt("It is not possible at the moment to stack convolutional cells.");super(t),this.inputSpec=[new Ie({ndim:5})]}call(t,e){return B(()=>{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=Te(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new _n("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_=[Te(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Te(s)):this.states_[0]=Te(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a<this.states_.length;++a){let u=t[a],l=s;if(!y.arraysEqual(u.shape,l))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${u.shape}`);this.states_[a]=u}}this.states_=this.states_.map(a=>$e(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=En(l,o[0],s,i[0],a[0]),m=En(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};_b.className="ConvRNN2D";var Zc=class extends $l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,Qe(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>Qe(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>Qe(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Oe(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>Qe(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=dr([c]),g=l.apply([c*2]);return Om([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Rl({ones:()=>Ir(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,at)=>!st||!st[at]?nt:$(st[at],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Rl({ones:()=>Ir(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=gr(this.kernel.read(),a,w),[D,F,P,V]=this.useBias?gr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=gr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let K=this.recurrentActivation.apply(Y(c,h)),X=this.recurrentActivation.apply(Y(p,g)),Z=Y($(X,i),$(K,this.activation.apply(Y(m,x)))),et=$(this.recurrentActivation.apply(Y(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=F7(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=kn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return kn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Zc.className="ConvLSTM2DCell";J.registerClass(Zc);var bf=class extends _b{constructor(t){let e=new Zc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};bf.className="ConvLSTM2D";J.registerClass(bf);var Jc=class extends _t{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return n}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(n);return Ou(()=>Uy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Jc.className="Dropout";J.registerClass(Jc);var wf=class extends Jc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};wf.className="SpatialDropout1D";J.registerClass(wf);var If=class extends _t{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,Qe(this.units,"units"),this.activation=hi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=he(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=he(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ve(t.kernelConstraint),this.biasConstraint=Ve(t.biasConstraint),this.kernelRegularizer=Ce(t.kernelRegularizer),this.biasRegularizer=Ce(t.biasRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Py(this.activation.getClassName()),s;return o!=null?s=$o(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=$o(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:di(this.activation),useBias:this.useBias,kernelInitializer:_e(this.kernelInitializer),biasInitializer:_e(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:Be(this.kernelConstraint),biasConstraint:Be(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};If.className="Dense";J.registerClass(If);var Cf=class extends _t{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Do(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=Vt(n,o)}return W$(n)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Flatten";J.registerClass(Cf);var vf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.activation=hi(t.activation)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:di(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};vf.className="Activation";J.registerClass(vf);var Sf=class extends _t{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),V$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="RepeatVector";J.registerClass(Sf);var Nf=class extends _t{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let n="Total size of new array must be unchanged.",o=e.slice(),s=1,i=null;for(let u=0;u<o.length;++u){let l=o[u];if(this.isUnknown(l))if(i===null)i=u;else throw new z("Can only specifiy one unknown dimension.");else s*=l}let a=Do(t);if(i!==null){if(s===0||a%s!==0)throw new z(n);o[i]=a/s}else if(a!==s)throw new z(n);return o}computeOutputShape(t){let e=!1;for(let n=0;n<t.length;++n)if(this.isUnknown(t[n])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};Nf.className="Reshape";J.registerClass(Nf);var kf=class extends _t{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ie({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};kf.className="Permute";J.registerClass(kf);var Tf=class extends _t{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t),o=-1;return dc(li(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=-1,s=!0,i=dc(li(n,this.maskValue),o,s);return $(n,Q(i,n.dtype))})}};Tf.className="Masking";J.registerClass(Tf);var _f=class extends _t{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(we(t.inputLength))}this.inputDim=t.inputDim,Qe(this.inputDim,"inputDim"),this.outputDim=t.outputDim,Qe(this.outputDim,"outputDim"),this.embeddingsInitializer=he(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ce(t.embeddingsRegularizer),this.activityRegularizer=Ce(t.activityRegularizer),this.embeddingsConstraint=Ve(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),li(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=we(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o<e.length;++o){let s=e[o],i=t[o+1];if(s!=null&&i!=null&&s!==i)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[n]=i),n++}}return[t[0],...e,this.outputDim]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=en(n,"int32"));let o=Wy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_e(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:Be(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};_f.className="Embedding";J.registerClass(_f);var Fl=class extends _t{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new Nt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let n=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],i=e[o];if(s==null||i==null||s<0||i<0)n.push(null);else if(s===1)n.push(i);else if(i===1)n.push(s);else{if(s!==i)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(s)}}return n}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Gt(t)]),t=t,t.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. 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t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};Ff.className="Concatenate";J.registerClass(Ff);function Vh(r,t){for(;r<0;)r+=t;return r}function O7(r,t,e){if(r.shape.length>3||t.shape.length>3)throw new Nt("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new Nt("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;l<i;++l)u.push(1);t=R(t,t.shape.concat(u))}else if(o>n){i=o-n;let u=[];for(let l=0;l<i;++l)u.push(1);r=R(r,r.shape.concat(u))}else i=0;let a;if(r.shape.length===2&&t.shape.length===2)s[0]===s[1]?a=pt($(r,t),s[0]):a=pt($(Vt(r,[1,0]),t),s[1]);else{let u=s[0]!==r.shape.length-1,l=s[1]===t.shape.length-1;a=Bt(r,t,u,l)}if(i>0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);a=Un(a,l)}return a.shape.length===1&&(a=ar(a,1)),a})}var Of=class extends Fl{constructor(t){super(t),this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} 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_t{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>Y(Pm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Pf.className="GaussianNoise";J.registerClass(Pf);var Mf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Pm(n.shape,1,s))},()=>n,e.training||!1):n})}};Mf.className="GaussianDropout";J.registerClass(Mf);var Lf=class extends _t{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return 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e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ie({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=_o(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return Gh(o,b,w,I,N,this.epsilon)}else return Gh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=L7(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(lt(E,w),N);b.write(lt(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),movingMeanInitializer:_e(this.movingMeanInitializer),movingVarianceInitializer:_e(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:Be(this.betaConstraint),gammaConstraint:Be(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};zf.className="BatchNormalization";J.registerClass(zf);var Bf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=he(t.betaInitializer||"zeros"),this.gammaInitializer=he(t.gammaInitializer||"ones"),this.betaRegularizer=Ce(t.betaRegularizer),this.gammaRegularizer=Ce(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Ao(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=yc(n,this.axis,!0),l=_o(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return a=Fr(a,f),u=Fr(u,f),p!=null&&(p=Fr(p,d)),m!=null&&(m=Fr(m,d)),Gh(n,a,u,m,p,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_e(this.betaInitializer),gammaInitializer:_e(this.gammaInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}};Bf.className="LayerNormalization";J.registerClass(Bf);function z7(r,t,e){return B(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=xn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],fn(r,n)})}var Vf=class extends _t{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?xn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>z7(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Vf.className="ZeroPadding2D";J.registerClass(Vf);function Fb(r,t,e,n,o,s){return B(()=>{Oe(o),kN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=Bh(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=ku(r,t,e,a):i=bu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function AR(r,t,e,n,o,s){return B(()=>{Oe(o),kN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=WN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Zx(r,t,e,a):i=Cx(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Eb=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(Qe(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,hn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=En(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=kl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Un(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"max")}};Gf.className="MaxPooling1D";J.registerClass(Gf);var Wf=class extends Eb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"avg")}};Wf.className="AveragePooling1D";J.registerClass(Wf);var Ab=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=En(e,this.poolSize[0],this.padding,this.strides[0]),n=En(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Uf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"max")}};Uf.className="MaxPooling2D";J.registerClass(Uf);var Hf=class extends Ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),Fb(t,e,n,o,s,"avg")}};Hf.className="AveragePooling2D";J.registerClass(Hf);var Db=class extends _t{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];Qe(this.poolSize,"poolSize"),Qe(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),hn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=En(e,this.poolSize[0],this.padding,this.strides[0]),n=En(n,this.poolSize[1],this.padding,this.strides[1]),o=En(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),AR(t,e,n,o,s,"max")}};qf.className="MaxPooling3D";J.registerClass(qf);var Kf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),hn(o),AR(t,e,n,o,s,"avg")}};Kf.className="AveragePooling3D";J.registerClass(Kf);var $b=class extends _t{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Nt}},jf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return ke(n,1)})}};jf.className="GlobalAveragePooling1D";J.registerClass(jf);var Xf=class extends $b{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Xf.className="GlobalMaxPooling1D";J.registerClass(Xf);var Rb=class extends _t{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Nt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Yf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?ke(n,[1,2]):ke(n,[2,3])})}};Yf.className="GlobalAveragePooling2D";J.registerClass(Yf);var Zf=class extends Rb{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Sr(n,[1,2]):Sr(n,[2,3])})}};Zf.className="GlobalMaxPooling2D";J.registerClass(Zf);var Ob=class extends _t{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=In(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Jf=class extends Ob{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),HN((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Jf.className="TimeDistributed";J.registerClass(Jf);function B7(r){da(P$,"BidirectionalMergeMode",r)}var V7="concat",Qf=class extends Ob{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=In(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=In(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?V7:t.mergeMode,B7(this.mergeMode),t.weights)throw new Nt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=UN(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new Ie({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new Nt("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof rn;for(let l of i)if(l instanceof rn!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=hr(s,1));let a;return this.mergeMode==="concat"?a=Om([o,s]):this.mergeMode==="sum"?a=Y(o,s):this.mergeMode==="ave"?a=$(.5,Y(o,s)):this.mergeMode==="mul"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){pi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),pi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=In(e.layer);if(delete e.layer,e.numConstants!=null)throw new Nt("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};Qf.className="Bidirectional";J.registerClass(Qf);var td=class extends _t{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!=="float32"&&(t=en(t,"float32")),Y($(t,this.scale),this.offset)))}};td.className="Rescaling";J.registerClass(td);var{resizeBilinear:G7,cropAndResize:W7}=dn,ed=class extends _t{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=qe([t])):l=t;for(let N=0;N<l.shape[0];N++)g.push(h);let x=sr(g,[g.length,4]),b=ca(0,g.length,1,"int32"),I=W7(l,x,b,[o,s],"nearest");return c?en(St(xr(I)),u):en(I,u)})}upsize(t,e,n,o){return B(()=>{let s=G7(t,[e,n]);return en(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};ed.className="CenterCrop";J.registerClass(ed);function DR(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=en(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=ar(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=ar(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=gh(a,n,e,i):u=gh(a,[],e,i),t!=="tfIdf")return u;if(n)return $(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var rd=class extends _t{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=en(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count.
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|
Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=xl(t),i=Fe(this.numTokens,o).bufferSync().get(0),a=pn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return DR(t,this.outputMode,this.numTokens,n)})}};rd.className="CategoryEncoding";J.registerClass(rd);var H7=["bilinear","nearest"],$R=new Set(H7),nd=class extends _t{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if($R.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return dn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return dn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...$R]} are supported`)})}};nd.className="Resizing";J.registerClass(nd);var Wh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Wh.className="RandomSeed";var Uh=class extends _t{constructor(t){super(t),this.randomGenerator=new Wh(t.seed)}getConfig(){let t={seed:this.randomGenerator.seed},e=super.getConfig();return Object.assign(t,e),t}};Uh.className="BaseRandomLayer";var q7=["bilinear","nearest"],RR=new Set(q7),od=class extends Uh{constructor(t){super(t);let{factor:e,interpolation:n="bilinear"}=t;if(this.factor=e,Array.isArray(this.factor)&&this.factor.length===2)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else if(!Array.isArray(this.factor)&&this.factor>0)this.widthLower=-this.factor,this.widthUpper=this.factor;else throw new z(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);if(this.widthLower<-1||this.widthUpper<-1)throw new z(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper<this.widthLower)throw new z(`factor cannot have upper bound less than lower bound.
|
|
Got upper bound: ${this.widthUpper}.
|
|
Got lower bound: ${this.widthLower}
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|
`);if(n)if(RR.has(n))this.interpolation=n;else throw new z(`Invalid interpolation parameter: ${n} is not implemented`)}getConfig(){let t={factor:this.factor,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.imgHeight,-1,e]}call(t,e){return B(()=>{let n=St(t);this.imgHeight=n.shape[n.shape.length-3];let o=n.shape[n.shape.length-2];this.widthFactor=Wn([1],1+this.widthLower,1+this.widthUpper,"float32",this.randomGenerator.next());let s=this.widthFactor.dataSync()[0]*o;s=Math.round(s);let i=[this.imgHeight,s];switch(this.interpolation){case"bilinear":return dn.resizeBilinear(t,i);case"nearest":return dn.resizeNearestNeighbor(t,i);default:throw new Error(`Interpolation is ${this.interpolation}
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but only ${[...RR]} are supported`)}})}};od.className="RandomWidth";J.registerClass(od);function K7(r){return new fi(r)}function j7(r){return new af(r)}function X7(r){return new nf(r)}function Y7(r){return new of(r)}function Z7(r){return new sf(r)}function J7(r){return new uf(r)}function Q7(r){return new lf(r)}function tJ(r){return new Vu(r)}function eJ(r){return new El(r)}function rJ(r){return new cf(r)}function nJ(r){return new Al(r)}function oJ(r){return new pf(r)}function sJ(r){return new mf(r)}function iJ(r){return new ff(r)}function aJ(r){return new df(r)}function lJ(r){return new hf(r)}function uJ(r){return new vf(r)}function cJ(r){return new If(r)}function pJ(r){return new Jc(r)}function mJ(r){return new wf(r)}function fJ(r){return new Cf(r)}function dJ(r){return new Sf(r)}function hJ(r){return new Nf(r)}function gJ(r){return new kf(r)}function xJ(r){return new _f(r)}function yJ(r){return new Ef(r)}function bJ(r){return new Df(r)}function wJ(r){return new Ff(r)}function IJ(r){return new $f(r)}function CJ(r){return new Rf(r)}function vJ(r){return new Af(r)}function SJ(r){return new Of(r)}function NJ(r){return new zf(r)}function kJ(r){return new Bf(r)}function TJ(r){return new Vf(r)}function qN(r){return new Wf(r)}function _J(r){return qN(r)}function EJ(r){return qN(r)}function KN(r){return new Hf(r)}function AJ(r){return KN(r)}function DJ(r){return KN(r)}function jN(r){return new Kf(r)}function $J(r){return jN(r)}function RJ(r){return jN(r)}function FJ(r){return new jf(r)}function OJ(r){return new Yf(r)}function FR(r){return new Xf(r)}function OR(r){return new Zf(r)}function PR(r){return new Gf(r)}function MR(r){return new Uf(r)}function PJ(r){return new qf(r)}function MJ(r){return new xf(r)}function LJ(r){return new Xc(r)}function zJ(r){return new yf(r)}function BJ(r){return new $l(r)}function VJ(r){return new gf(r)}function GJ(r){return new jc(r)}function WJ(r){return new bf(r)}function UJ(r){return new Zc(r)}function HJ(r){return new An(r)}function qJ(r){return new Yc(r)}function KJ(r){return new Qf(r)}function jJ(r){return new Jf(r)}var XJ=FR,YJ=OR,ZJ=PR,JJ=MR;function QJ(r){return new Pf(r)}function t9(r){return new Mf(r)}function e9(r){return new Lf(r)}function r9(r){return new Tf(r)}function n9(r){return new td(r)}function o9(r){return new ed(r)}function s9(r){return new nd(r)}function i9(r){return new rd(r)}function a9(r){return new od(r)}var zR={};Kt(zR,{MAPE:()=>y9,MSE:()=>I9,binaryAccuracy:()=>l9,binaryCrossentropy:()=>u9,categoricalAccuracy:()=>p9,categoricalCrossentropy:()=>m9,cosineProximity:()=>h9,mape:()=>b9,meanAbsoluteError:()=>g9,meanAbsolutePercentageError:()=>x9,meanSquaredError:()=>w9,mse:()=>C9,precision:()=>f9,recall:()=>d9,sparseCategoricalAccuracy:()=>c9});function l9(r,t){return Ph(r,t)}function u9(r,t){return nb(r,t)}function c9(r,t){return ob(r,t)}function p9(r,t){return Mh(r,t)}function m9(r,t){return Lh(r,t)}function f9(r,t){return FN(r,t)}function d9(r,t){return lR(r,t)}function h9(r,t){return Oh(r,t)}function g9(r,t){return Zm(r,t)}function x9(r,t){return Mu(r,t)}function y9(r,t){return Mu(r,t)}function b9(r,t){return Mu(r,t)}function w9(r,t){return ga(r,t)}function I9(r,t){return ga(r,t)}function C9(r,t){return ga(r,t)}var BR={};Kt(BR,{modelFromJSON:()=>vR});var VR={};Kt(VR,{l1:()=>S9,l1l2:()=>v9,l2:()=>N9});function v9(r){return new Lu(r)}function S9(r){return TR(r)}function N9(r){return _R(r)}var Mb=class extends _l{constructor(){super(...arguments),this.model=null}setModel(t){if(!(t instanceof qn))throw new Error("model must be a LayersModel, not some other Container");this.model=t}};function Pb(r,t){return r<t}function GR(r,t){return r>t}var Lb=class extends Mb{constructor(t){if(super(),t==null&&(t={}),t.restoreBestWeights)throw new Nt("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=t.monitor||"val_loss",this.minDelta=Math.abs(t.minDelta||0),this.patience=t.patience||0,this.verbose=t.verbose||0,this.mode=t.mode||"auto",this.baseline=t.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. 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TypeError(`Node type ${r.op} is not implemented`)}};function Kn(r,t,e=""){if(!(typeof r=="number"||typeof t=="number")){y.assert(r.length===t.length,()=>e+` Shapes ${r} and ${t} must match`);for(let n=0;n<r.length;n++){let o=r[n],s=t[n];y.assert(o<0||s<0||o===s,()=>e+` Shapes ${r} and ${t} must match`)}}}function XR(r){return!(typeof r=="number"||r.some(t=>t<0))}function sd(r,t,e){let n=Jb(r,e),o=!XR(n);if(o&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${n}`);if(o&&t.forEach(s=>{n=Jb(s.shape,n)}),!XR(n))throw new Error(`Non-fully-defined elementShape: ${n}`);return n}function Jb(r,t){if(typeof r=="number")return t;if(typeof t=="number")return r;if(r.length!==t.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${t}`);let e=[];for(let n=0;n<r.length;++n){let o=r[n],s=t[n];if(o>=0&&s>=0&&o!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${t}`);e[n]=o>=0?o:s}return e}var Qb=class{constructor(t,e,n,o,s,i,a){this.name=t,this.dtype=e,this.maxSize=n,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=i,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=ft(0),$e(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let e=this.tensors[t];if(e.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${t} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor}readMany(t){return t.map(e=>this.read(e))}write(t,e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||!this.dynamicSize&&t>=this.maxSize)throw new Error(`Tried to write to index ${t}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[t]||{};if(e.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t},
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because the value dtype is ${e.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=e.shape),Kn(this.elementShape,e.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${t}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been written.`);n.tensor=e,$e(e),n.written=!0,this.tensors[t]=n}writeMany(t,e){if(t.length!==e.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${t.length} is not the same as tensors size: ${e.length}.`);t.forEach((n,o)=>this.write(n,e[o]))}gather(t,e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${e}`);if(t)t=t.slice(0,this.size());else{t=[];for(let o=0;o<this.size();o++)t.push(o)}if(t.length===0)return sr([],[0].concat(this.elementShape));let n=this.readMany(t);return Kn(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),qe(n,0)}concat(t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${t}`);if(this.size()===0)return sr([],[0].concat(this.elementShape));let e=[];for(let o=0;o<this.size();o++)e.push(o);let n=this.readMany(e);return Kn(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),se(n,0)}scatter(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let n=Math.max(...t);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(t,xr(e,0))}split(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);let n=0,o=t.map(u=>(n+=u,n));if(n!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
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${n}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${t.length}), and the TensorArray is not marked as dynamically resizeable`);let s=n===0?0:e.size/n,i=[];B(()=>{e=R(e,[1,n,s]);for(let u=0;u<t.length;++u){let c=[0,u===0?0:o[u-1],0],p=[1,t[u],s];i[u]=R(Ot(e,c,p),this.elementShape)}return i});let a=[];for(let u=0;u<t.length;u++)a[u]=u;this.writeMany(a,i)}};var Ol=class{get id(){return this.idTensor.id}constructor(t,e,n,o=-1){this.tensors=t,this.elementShape=e,this.elementDtype=n,t!=null&&t.forEach(s=>{if(n!==s.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${s.dtype}`);Kn(e,s.shape,"TensorList shape mismatch: "),$e(s)}),this.idTensor=ft(0),this.maxNumElements=o,$e(this.idTensor)}copy(){return new Ol([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.id))&&e.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(t,e,n=-1){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Kn(t,this.elementShape,"TensorList shape mismatch: ");let o=sd(this.elementShape,this.tensors,t);return B(()=>{let s=this.tensors.map(i=>R(i,o));return qe(s,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=sd(this.elementShape,this.tensors,t),o=this.tensors.pop();return o.kept=!1,Kn(o.shape,t,"TensorList shape mismatch: "),R(o,n)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(Kn(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");$e(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. 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|
tensor.shape[0], but sum of lengths is
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|
${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=Jb(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let p=0;p<t.length;++p){let f=[0,p===0?0:o[p-1],0],d=[1,t[p],a];c[p]=R(Ot(r,f,d),i)}return r.dispose(),c}),l=new Ol([],e,r.dtype,t.length);for(let c=0;c<u.length;c++)l.setItem(c,u[c]);return l}var tF=async(r,t,e)=>{switch(r.op){case"If":case"StatelessIf":{let n=C("thenBranch",r,t,e),o=C("elseBranch",r,t,e),s=C("cond",r,t,e),i=C("args",r,t,e);return(await s.data())[0]?e.functionMap[n].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap):e.functionMap[o].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let n=C("body",r,t,e),o=C("cond",r,t,e),s=C("args",r,t,e),i=await e.functionMap[o].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),a=s.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&a.indexOf(c.id)===-1&&c.dispose()});let l=s;for(;u[0];){let c=l;l=await 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n=C("elementShape",r,t,e),o=C("elementDType",r,t,e),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=C(s,r,t,e),a=r.op==="TensorListReserve"?-1:i,u=ZR(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case"TensorListGather":{let n=C("tensorListId",r,t,e),o=C("indices",r,t,e),s=C("elementShape",r,t,e),i=C("elementDType",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case"TensorListStack":{let n=C("tensorListId",r,t,e),o=C("elementShape",r,t,e),s=C("elementDType",r,t,e),i=C("numElements",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case"TensorListFromTensor":{let n=C("tensor",r,t,e),o=C("elementShape",r,t,e),s=C("elementDType",r,t,e),i=YR(n,o,s);return e.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=C("tensorListId",r,t,e),o=e.getTensorList(n.id),s=C("dtype",r,t,e),i=C("elementShape",r,t,e);return[o.concat(s,i)]}case"TensorListPushBack":{let n=C("tensorListId",r,t,e),o=C("tensor",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=C("tensorListId",r,t,e),o=C("elementShape",r,t,e),s=C("elementDType",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=C("tensor",r,t,e),o=C("elementShape",r,t,e),s=C("lengths",r,t,e),i=QR(n,s,o);return e.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=C("tensorListId",r,t,e),o=e.getTensorList(n.id);return[ft(o.size(),"int32")]}case"TensorListResize":{let n=C("tensorListId",r,t,e),o=C("size",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function eF(r,t,e){let[n,o]=C("fusedOps",r,t,e),s=n==="biasadd",i=!s,a=o==="prelu",u=n==="fusedbatchnorm",l=C("numArgs",r,t,e);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&s&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one 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o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("kernelSize",r,t,e),a=C("includeBatchInIndex",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(C("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case"AvgPool3D":{let o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("kernelSize",r,t,e);return[n.avgPool3d(C("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"MaxPool3D":{let o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("kernelSize",r,t,e);return[n.maxPool3d(C("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"Dilation2D":{let o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("dilations",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(C("x",r,t,e),C("filter",r,t,e),[a,u],s,[l,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nF=(r,t,e,n=ie)=>{switch(r.op){case"Fill":{let o=C("shape",r,t,e),s=C("dtype",r,t,e),i=C("value",r,t,e);return[n.fill(o,i,s)]}case"LinSpace":{let o=C("start",r,t,e),s=C("stop",r,t,e),i=C("num",r,t,e);return[n.linspace(o,s,i)]}case"Multinomial":{let o=C("logits",r,t,e),s=C("numSamples",r,t,e),i=C("seed",r,t,e);return[n.multinomial(o,s,i)]}case"OneHot":{let o=C("indices",r,t,e),s=C("depth",r,t,e),i=C("onValue",r,t,e),a=C("offValue",r,t,e),u=C("dtype",r,t,e);return[n.oneHot(o,s,i,a,u)]}case"Ones":return[n.ones(C("shape",r,t,e),C("dtype",r,t,e))];case"OnesLike":return[n.onesLike(C("x",r,t,e))];case"RandomStandardNormal":return[n.randomStandardNormal(C("shape",r,t,e),C("dtype",r,t,e),C("seed",r,t,e))];case"RandomUniform":return[n.randomUniform(C("shape",r,t,e),C("minval",r,t,e),C("maxval",r,t,e),C("dtype",r,t,e))];case"RandomUniformInt":return[n.randomUniformInt(C("shape",r,t,e),C("minval",r,t,e),C("maxval",r,t,e),C("seed",r,t,e))];case"Range":{let o=C("start",r,t,e),s=C("stop",r,t,e),i=C("step",r,t,e);return[n.range(o,s,i,C("dtype",r,t,e))]}case"TruncatedNormal":{let 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B(()=>{let o=[];for(let s=0;s<n.length;s++){let i=n[s],a=this.findWithDefault(i,e);o.push(a)}return qe(o)})}findWithDefault(t,e){let n=this.tensorMap.get(t);return n!=null?n:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}};var aF=async(r,t,e,n)=>{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=C("keyDType",r,t,e),i=C("valueDType",r,t,e),a=new tw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=C("tableHandle",r,t,e,n),s=C("keys",r,t,e),i=C("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let o=C("tableHandle",r,t,e,n),s=C("keys",r,t,e),i=C("defaultValue",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let o=C("tableHandle",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var lF=(r,t,e,n=ie)=>{switch(r.op){case"ResizeBilinear":{let o=C("images",r,t,e),s=C("size",r,t,e),i=C("alignCorners",r,t,e),a=C("halfPixelCenters",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case"ResizeNearestNeighbor":{let o=C("images",r,t,e),s=C("size",r,t,e),i=C("alignCorners",r,t,e),a=C("halfPixelCenters",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case"CropAndResize":{let o=C("image",r,t,e),s=C("boxes",r,t,e),i=C("boxInd",r,t,e),a=C("cropSize",r,t,e),u=C("method",r,t,e),l=C("extrapolationValue",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case"ImageProjectiveTransformV3":{let o=C("images",r,t,e),s=C("transforms",r,t,e),i=C("outputShape",r,t,e),a=C("fillValue",r,t,e),u=C("interpolation",r,t,e),l=C("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var uF=(r,t,e,n=ie)=>{switch(r.op){case"Equal":return[n.equal(C("a",r,t,e),C("b",r,t,e))];case"NotEqual":return[n.notEqual(C("a",r,t,e),C("b",r,t,e))];case"Greater":return[n.greater(C("a",r,t,e),C("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(C("a",r,t,e),C("b",r,t,e))];case"Less":return[n.less(C("a",r,t,e),C("b",r,t,e))];case"LessEqual":return[n.lessEqual(C("a",r,t,e),C("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(C("a",r,t,e),C("b",r,t,e))];case"LogicalNot":return[n.logicalNot(C("a",r,t,e))];case"LogicalOr":return[n.logicalOr(C("a",r,t,e),C("b",r,t,e))];case"Select":case"SelectV2":return[n.where(C("condition",r,t,e),C("a",r,t,e),C("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(C("a",r,t,e),C("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var cF=(r,t,e,n=ie)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(C("a",r,t,e),C("b",r,t,e),C("transposeA",r,t,e),C("transposeB",r,t,e))];case"Einsum":return[n.einsum(C("equation",r,t,e),...C("tensors",r,t,e))];case"Transpose":return[n.transpose(C("x",r,t,e),C("perm",r,t,e))];case"_FusedMatMul":let[o,s]=C("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=C("numArgs",r,t,e),l=C("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=C("args",r,t,e);return[n.fused.matMul({a:C("a",r,t,e),b:C("b",r,t,e),transposeA:C("transposeA",r,t,e),transposeB:C("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(C("a",r,t,e),C("numLower",r,t,e),C("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var pF=(r,t,e,n=ie)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(C("x",r,t,e),C("axis",r,t,e),C("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(C("x",r,t,e),C("mean",r,t,e),C("variance",r,t,e),C("offset",r,t,e),C("scale",r,t,e),C("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(C("x",r,t,e),C("mean",r,t,e),C("variance",r,t,e),C("offset",r,t,e),C("scale",r,t,e),C("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(C("x",r,t,e),C("radius",r,t,e),C("bias",r,t,e),C("alpha",r,t,e),C("beta",r,t,e))];case"Softmax":return[n.softmax(C("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(C("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var mF=(r,t,e,n=ie)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(C("paramsNestedSplits",r,t,e),C("paramsDenseValues",r,t,e),C("indices",r,t,e),C("outputRaggedRank",r,t,e));return 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a=C("axis",r,t,e);return[n.argMax(C("x",r,t,e),a)]}case"ArgMin":{let a=C("axis",r,t,e);return[n.argMin(C("x",r,t,e),a)]}case"Prod":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.prod(C("x",r,t,e),a,u)]}case"Cumprod":{let a=C("axis",r,t,e),u=C("exclusive",r,t,e),l=C("reverse",r,t,e);return[n.cumprod(C("x",r,t,e),a,u,l)]}case"Cumsum":{let a=C("axis",r,t,e),u=C("exclusive",r,t,e),l=C("reverse",r,t,e);return[n.cumsum(C("x",r,t,e),a,u,l)]}case"Bincount":let o=C("x",r,t,e),s=C("weights",r,t,e),i=C("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=C("x",r,t,e),u=C("weights",r,t,e),l=C("size",r,t,e),c=C("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var dF=(r,t,e,n=ie)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=C("n",r,t,e),s=C("axis",r,t,e),i=C("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=C("x",r,t,e),s=C("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=C("axis",r,t,e),s=C("batchDims",r,t,e),i=C("x",r,t,e),a=C("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=C("dims",r,t,e),s=[];for(let a=0;a<o.length;a++)o[a]&&s.push(a);let i=C("x",r,t,e);return[n.reverse(i,s)]}case"ReverseV2":{let o=C("axis",r,t,e),s=C("x",r,t,e);return[n.reverse(s,o)]}case"Slice":{let o=C("begin",r,t,e),s=C("size",r,t,e);return[n.slice(C("x",r,t,e),o,s)]}case"StridedSlice":{let o=C("begin",r,t,e),s=C("end",r,t,e),i=C("strides",r,t,e),a=C("beginMask",r,t,e),u=C("endMask",r,t,e),l=C("ellipsisMask",r,t,e),c=C("newAxisMask",r,t,e),p=C("shrinkAxisMask",r,t,e),m=C("x",r,t,e);return[n.stridedSlice(m,o,s,i,a,u,l,c,p)]}case"Pack":return B(()=>{let o=C("axis",r,t,e),s=C("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape 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hF=(r,t,e,n=ie)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(C("indices",r,t,e),C("values",r,t,e),C("denseShape",r,t,e),C("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(C("inputIndices",r,t,e),C("inputShape",r,t,e),C("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(C("data",r,t,e),C("indices",r,t,e),C("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(C("data",r,t,e),C("indices",r,t,e),C("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gF=(r,t,e,n=ie)=>{switch(r.op){case"FFT":return[n.fft(C("x",r,t,e))];case"IFFT":return[n.ifft(C("x",r,t,e))];case"RFFT":return[n.rfft(C("x",r,t,e))];case"IRFFT":return[n.irfft(C("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var xF=(r,t,e,n=ie)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(C("input",r,t,e),C("pattern",r,t,e),C("rewrite",r,t,e),C("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(C("data",r,t,e),C("dataSplits",r,t,e),C("separator",r,t,e),C("nGramWidths",r,t,e),C("leftPad",r,t,e),C("rightPad",r,t,e),C("padWidth",r,t,e),C("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(C("input",r,t,e),C("delimiter",r,t,e),C("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(C("input",r,t,e),C("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var yF=(r,t,e,n=ie)=>{switch(r.op){case"Cast":return[n.cast(C("x",r,t,e),C("dtype",r,t,e))];case"ExpandDims":{let o=C("axis",r,t,e);return[n.expandDims(C("x",r,t,e),o)]}case"Squeeze":{let o=C("axis",r,t,e);return[n.squeeze(C("x",r,t,e),o)]}case"Reshape":return[n.reshape(C("x",r,t,e),C("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(C("x",r,t,e),C("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(C("x",r,t,e),C("padding",r,t,e),C("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(C("x",r,t,e),C("padding",r,t,e),C("constantValue",r,t,e))];case"SpaceToBatchND":{let o=C("blockShape",r,t,e),s=C("paddings",r,t,e);return[n.spaceToBatchND(C("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=C("blockShape",r,t,e),s=C("crops",r,t,e);return[n.batchToSpaceND(C("x",r,t,e),o,s)]}case"DepthToSpace":{let o=C("blockSize",r,t,e),s=C("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(C("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(C("x",r,t,e),C("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(C("s0",r,t,e),C("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function yk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>KR(i,a,u));case"basic_math":return o(()=>jR(i,a,u));case"control":return tF(i,a,u);case"convolution":return o(()=>rF(i,a,u));case"creation":return o(()=>nF(i,a,u));case"dynamic":return oF(i,a,u);case"evaluation":return o(()=>sF(i,a,u));case"image":return o(()=>lF(i,a,u));case"graph":return o(()=>iF(i,a,u));case"logical":return o(()=>uF(i,a,u));case"matrices":return o(()=>cF(i,a,u));case"normalization":return o(()=>pF(i,a,u));case"ragged":return o(()=>mF(i,a,u));case"reduction":return o(()=>fF(i,a,u));case"slice_join":return o(()=>dF(i,a,u));case"sparse":return o(()=>hF(i,a,u));case"spectral":return o(()=>gF(i,a,u));case"string":return o(()=>xF(i,a,u));case"transformation":return o(()=>yF(i,a,u));case"hash_table":return aF(i,a,u,n);case"custom":let l=zb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Zb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Kh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let n=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(n))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function bk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>Cn(m)[0]));n=n||[];let c=new Set(n.map(m=>Cn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Gu(m)||vQ(m)||SQ(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function bF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>Cn(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=yQ(d,u);return bQ(h,u),h}function yQ(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var id=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function bQ(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new id(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new id(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new id(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new id(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function wF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Gu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;a<r.length;++a){let u=s[a];if(u===e)continue;let l=r[a],c=r[u];i.has(c.name)||i.set(c.name,[]),i.get(c.name).push(l)}return i}var wQ=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),IQ=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),CQ=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Gu(r){return wQ.has(r.op)}function vQ(r){return IQ.has(r.op)}function SQ(r){return CQ.has(r.op)}var Qc=class{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Qc(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=bk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=bF(this.graph,n),u=wF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[Cn(m)[0]]),s=e.map(m=>Cn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Kh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=Cn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=yk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Gu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Gu(u))continue;let l=YN(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Gu(u)||s.has(u.name)}if(!(Gu(t)||i==null))for(let u of i){if(a(u))continue;let l=YN(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Kh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[Cn(I)[0]]),a=n.map(I=>Cn(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=bk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=Cn(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Gu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,o,n)&&([m]=xi(p.node.name,n)),o[p.node.name]==null){let f=yk(p.node,o,n,this._resourceManager);m||([m]=xi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=xi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=Cn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=Cn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=Cn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var ew=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var NQ="?tfjs-format=file",kQ="model.json",jh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new ew}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>this.loadSync(e)):this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Qc(qh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=qh.Instance.transformGraph(t.modelInitializer);this.initializer=new Qc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Pt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Pt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let 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s=n[o],i=e[s];this.resourceIdToCapturedInput[i.resourceId]=t[o]}}}execute(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=this.executor.execute(t,e);return n.length>1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function TQ(r,t={},e=Mr){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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r=="object"&&r instanceof Pt||y.isTypedArray(r)}function AQ(r){return r===null||typeof r!="object"&&typeof r!="function"}function kF(r){return CF(r,DQ)}function DQ(r){return r instanceof Pt?{value:r.clone(),recurse:!1}:Wu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var ad=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return 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this.trav++,{value:kF(t),done:!1}}},vk=class extends tr{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Sk=class extends tr{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Nk=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Tt(t.value)}return this.upstream.next()}},kk=class extends tr{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Tk=class extends tr{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},_k=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Ek=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Co.getTensorsInContainer(t.value),n=this.transform(t.value),o=Co.getTensorsInContainer(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Ak=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},ow=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Co.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Co.getTensorsInContainer(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ep=class extends tr{constructor(){super(),this.outputQueue=new tp,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Dk=class extends ep{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Co.getTensorsInContainer(t.value),n=this.transform(t.value),o=Co.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Co.isTensorInList(s,o)||s.dispose();return!0}},sw=class extends tr{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Pl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Pl||(Pl={}));var $k=class extends tr{constructor(t,e=Pl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof tr?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await nw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Pl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Pl.SHORTEST:return{value:null,done:!0};case Pl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},iw=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new ad(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},Rk=class extends iw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=TF.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var bi=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is
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${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),Dn(async()=>(await n.iterator()).columnMajorBatch(t,e,$Q),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,Dn(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,Dn(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return Dn(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return Dn(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return Dn(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,Dn(async()=>{let o=Xh(async()=>({value:await e.iterator(),done:!1}));return _F(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?n=0:n=null,Dn(async()=>(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=AF.alea(e||y.now().toString());return Dn(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,Dn(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};bi.MAX_BUFFER_SIZE=1e4;function Dn(r,t=null){return new class extends bi{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function DF(r){return Dn(async()=>Fk(r),r.length)}function $F(r){if(!Wu(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e<r.length;e++)t=t==null?r[e].size:Math.min(t,r[e].size);else if(r instanceof Object)for(let e in r)t=t==null?r[e].size:Math.min(t,r[e].size);return Dn(async()=>{let e=await nw(r,n=>{if(n instanceof bi)return{value:n.iterator(),recurse:!1};if(Wu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return EF(e,Pl.SHORTEST)},t)}function $Q(r){if(r===null)return null;let t=r[0];return NF(t)?{value:RQ(r),recurse:!1}:{value:null,recurse:!0}}function RQ(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Pt?qe(r):sr(r)}var ld=class extends bi{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var aw='"',Yh=Symbol("out"),RF=Symbol("field"),lw=Symbol("quote"),Ok=Symbol("quoteafterquote"),FF=Symbol("quoteinquote"),ud=class extends bi{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new ld(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let i=this.fullColumnNames[s],a=this.columnConfigs?this.columnConfigs[i]:null;if(!(this.configuredColumnsOnly&&!a)){let u=e[s],l=null;if(u==="")if(a&&a.default!==void 0)l=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${i} is empty in this line: ${t}`);l=void 0}else{let c=Number(u);if(isNaN(c))a&&a.dtype==="bool"?l=this.getBoolean(u):l=u;else if(!a||!a.dtype)l=c;else switch(a.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(u);break;default:l=c}}a&&a.isLabel?o[i]=l:n[i]=l}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let n=[],o=0,s=t.length,i=Yh;for(let a=0;a<s;a++)switch(i){case Yh:switch(t.charAt(a)){case aw:o=a+1,i=lw;break;case this.delimiter:if(o=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),i=Yh;break;default:i=RF,o=a;break}break;case RF:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a)),i=Yh,o=a+1;break;default:}break;case lw:switch(t.charAt(a)){case aw:i=Ok;break;default:}break;case Ok:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a-1)),i=Yh,o=a+1;break;case aw:i=lw;break;default:i=FF;break}break;case FF:switch(t.charAt(a)){case aw:i=lw;break;default:}break;default:}if(i===Ok?n.push(t.substring(o,s-1)):n.push(t.substring(o)),e&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var cd=class extends tr{constructor(t){super(),this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new cd(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),sr(n,e)}};var pd=class extends tr{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=ui([i,s,u,a],[1,4])}else this.cropBox=ui([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new pd(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Ay.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ar(Q(t,"float32"),0),n;n=dn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var md=class{};var Zh=class extends tr{split(t){return new Pk(this,t)}},Pk=class extends Zh{constructor(t,e){super(),this.upstream=t,this.impl=new Mk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Mk=class extends ep{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var uw=class extends tr{decodeUTF8(){return new Lk(this)}},Lk=class extends Zh{constructor(t){super(),this.upstream=t,this.impl=new zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zk=class extends ep{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=wk();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var fd=class extends uw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function OF(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=FQ(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new fd(i,t)}else throw new Error(s.statusText)}var FQ=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function cw(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var dd=class extends md{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(cw(this.input)&&L().get("IS_NODE")){let t=pw();this.input=t.readFileSync(this.input.slice(7))}return new fd(this.input,this.options)}};var hd=class extends md{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return cw(this.url)?new dd(this.url,this.fileOptions).iterator():OF(this.url,this.fileOptions)}};function PF(r,t={}){return new ud(new hd(r),t)}function MF(r){let t=Xh(r);return Dn(async()=>t)}function LF(r){return Dn(async()=>{let t=await r();return Xh(()=>t.next())})}async function zF(r,t){return pd.create(r,t)}async function BF(r){return cd.create(r)}var Bk="4.5.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var OQ=Kr.whereImpl,Uu=class extends Wo{nextDataId(){return Uu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ta(this,Vn())}write(t,e,n){this.firstUse&&(this.firstUse=!1,L().get("IS_NODE")&&S.warn(`
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============================
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Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details.
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============================`));let o={id:this.nextDataId()};return this.data.set(o,{values:t,dtype:n,refCount:1}),o}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return{dataId:o,shape:t,dtype:e}}refCount(t){return this.data.has(t)?this.data.get(t).refCount:0}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,n,o,s){this.data.set(t,{values:e,dtype:o,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:n}=this.data.get(t);if(e==="complex64"){let o=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return S.mergeRealAndImagArrays(o,s)}return y.convertBackendValuesAndArrayBuffer(this.data.get(t).values,e)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return bt(t.shape,t.dtype,e)}makeOutput(t,e,n){return Vn().makeTensorFromTensorInfo(this.makeTensorInfo(e,n,t),this)}disposeData(t,e=!1){if(this.data.has(t)){if(this.data.get(t).refCount--,!e&&this.data.get(t).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(t);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(t)}return!0}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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Nw={};Kt(Nw,{addImpl:()=>Wk,bincountImpl:()=>yd,bincountReduceImpl:()=>mw,bitwiseAndImpl:()=>Uk,castImpl:()=>Gk,ceilImpl:()=>Hk,concatImpl:()=>rp,equalImpl:()=>qk,expImpl:()=>jk,expm1Impl:()=>Yk,floorDivImpl:()=>Jk,floorImpl:()=>Zk,gatherNdImpl:()=>fw,gatherV2Impl:()=>dw,greaterEqualImpl:()=>tT,greaterImpl:()=>Qk,lessEqualImpl:()=>rT,lessImpl:()=>eT,linSpaceImpl:()=>hw,logImpl:()=>nT,maxImpl:()=>gw,maximumImpl:()=>oT,minimumImpl:()=>sT,multiplyImpl:()=>Jh,negImpl:()=>iT,notEqualImpl:()=>aT,prodImpl:()=>lT,raggedGatherImpl:()=>xw,raggedRangeImpl:()=>yw,raggedTensorToTensorImpl:()=>bw,rangeImpl:()=>op,rsqrtImpl:()=>uT,scatterImpl:()=>wi,sigmoidImpl:()=>yO,simpleAbsImpl:()=>Vk,sliceImpl:()=>sp,sparseFillEmptyRowsImpl:()=>ww,sparseReshapeImpl:()=>Iw,sparseSegmentReductionImpl:()=>Id,sqrtImpl:()=>IO,squaredDifferenceImpl:()=>pT,staticRegexReplaceImpl:()=>mT,stridedSliceImpl:()=>Cw,stringNGramsImpl:()=>ip,stringSplitImpl:()=>ap,stringToHashBucketFastImpl:()=>lp,subImpl:()=>dT,tileImpl:()=>vw,topKImpl:()=>Sw,transposeImpl:()=>bd,uniqueImpl:()=>up});function 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Cr(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=e.makeTensorInfo(n.shape,"complex64"),u=e.data.get(a.dataId);return u.complexTensorInfos={real:e.makeTensorInfo(n.shape,"float32",s),imag:e.makeTensorInfo(o.shape,"float32",i)},a}var WF={kernelName:Op,backendName:"cpu",kernelFunc:Cr};function gd(r,t,e="float32"){if(e==="complex64"){let o=gd(r,t,"float32"),s=gd(r,t,"float32");return Cr({inputs:{real:o,imag:s},backend:r})}let n=y.makeZerosTypedArray(y.sizeFromShape(t),e);return r.makeTensorInfo(t,e,n)}function Yr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var UF={kernelName:xo,backendName:"cpu",kernelFunc:Yr};function Oo(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.real,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var HF={kernelName:jp,backendName:"cpu",kernelFunc:Oo};function 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qF={kernelName:ho,backendName:"cpu",kernelFunc:Po};function ne(r,t,e,n){return e==null?({inputs:o,backend:s})=>{let{a:i,b:a}=o,u=s;tt([i,a],r);let l=u.data.get(i.dataId).values,c=u.data.get(a.dataId).values,p=i.dtype==="string"?S.fromUint8ToStringArray(l):l,m=i.dtype==="string"?S.fromUint8ToStringArray(c):c,f=n||i.dtype,[d,h]=t(i.shape,a.shape,p,m,f);return u.makeTensorInfo(h,f,d)}:({inputs:o,backend:s})=>{let{a:i,b:a}=o,u=s;if(i.dtype==="complex64"||a.dtype==="complex64"){let 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i=fO(t,2)[1],a=fO(s,2)[1],u=0;for(let l of e)for(let c=l[0];c<l[1];++c){for(let p=0;p<n;++p)o[u*a+p]=r[c*i+p];++u}}function stt(r,t,e,n,o){let s=t.slice();s[0]=o;let i=y.getArrayFromDType(e,y.sizeFromShape(s)),a=r.length,u=a===0?0:a/t[0];return ott(r,t,n,u,i,s),[i,s]}function xw(r,t,e,n,o,s,i,a){if(r.length===0)throw new Error("paramsNestedSplits must be non empty");if(t[0].length===0)throw new Error("Split tensors must not be scalars");let u=t[0][0]-1;if(ttt(s,i,u),n.length===0)throw new Error("params.rank must be nonzero");let l=n[0],{outSplits:c,valueSlices:p,numValues:m}=rtt(s,i,r,l),f=ntt(c),d=stt(e,n,o,p,m);return[f,d[0],d[1]]}var dO=2147483647;function yw(r,t,e,n,o,s,i){if(t.length>1)throw new Error("starts must be a scalar or vector");if(o.length>1)throw new Error("limits must be a scalar or vector");if(i.length>1)throw new Error("deltas must be a scalar or vector");let a=t.length===0,u=o.length===0,l=i.length===0,c=[];a||c.push(t[0]),u||c.push(o[0]),l||c.push(i[0]);for(let g=1;g<c.length;++g)if(c[g]!==c[g-1])throw new Error("starts, limits, and deltas must have the same shape");let p=c.length===0?1:c[0],m=y.getArrayFromDType("int32",p+1);m[0]=0;for(let g=0;g<p;++g){let x=a?r[0]:r[g],b=u?n[0]:n[g],w=l?s[0]:s[g];if(w===0)throw new Error("Requires delta != 0");let I;if(w>0&&b<x||w<0&&b>x)I=0;else if(I=Math.ceil(Math.abs((b-x)/w)),I>dO)throw new Error(`Requires ((limit - start) / delta) <= ${dO}`);m[g+1]=m[g]+I}let f=m[p],d=y.getArrayFromDType(e,f),h=0;for(let g=0;g<p;++g){let x=m[g+1]-m[g],b=a?r[0]:r[g],w=l?s[0]:s[g];for(let I=0;I<x;++I)d[h++]=b,b+=w}return[m,d]}var Mo=S.RowPartitionType,wd=class{constructor(t,e,n,o,s,i,a,u,l,c){this.shape=t,this.shapeShape=e,this.values=n,this.valuesShape=o,this.valuesDType=s,this.defaultValue=i,this.defaultValueShape=a,this.rowPartitionValues=u,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=S.getRowPartitionTypesHelper(c),this.raggedRank=S.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(t){return this.rowPartitionTypes[0]===Mo.FIRST_DIM_SIZE?this.rowPartitionTypes[t+1]:this.rowPartitionTypes[t]}getRowPartitionTensor(t){return this.rowPartitionTypes[0]===Mo.FIRST_DIM_SIZE?this.rowPartitionValues[t+1]:this.rowPartitionValues[t]}getMaxWidth(t){let e=this.getRowPartitionTensor(t-1);switch(this.getRowPartitionTypeByDimension(t-1)){case Mo.VALUE_ROWIDS:return wd.getMaxWidthValueRowID(e);case Mo.ROW_SPLITS:return wd.getMaxWidthRowSplit(e);default:throw new Error(`Cannot handle partition type ${Mo[this.getRowPartitionTypeByDimension(t-1)]}`)}}static getMaxWidthRowSplit(t){let e=t.length;if(e===0||e===1)return 0;let n=0;for(let o=0;o<e-1;++o){let s=t[o+1]-t[o];s>n&&(n=s)}return n}static getMaxWidthValueRowID(t){let e=t.length;if(e===0)return 0;let n=0,o=t[0],s=0;for(let i=1;i<e;++i){let a=t[i];a!==o&&(o=a,s=Math.max(i-n,s),n=i)}return Math.max(e-n,s)}tensorShapeFromTensor(t,e,n=!0){if(e.length===0){if(t[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return gO(t,n)}calculateOutputSize(t){let e=this.valuesShape,n=this.defaultValueShape;S.validateDefaultValueShape(n,e);let o=this.tensorShapeFromTensor(this.shape,this.shapeShape),i=S.combineRaggedTensorToTensorShapes(this.raggedRank,o,e);i[0]<0&&(i[0]=t);for(let a=1;a<=this.raggedRank;++a)i[a]<0&&(i[a]=this.getMaxWidth(a));return i}calculateFirstParentOutputIndex(t,e,n){let o=Math.min(t,n),s=[],i=0;for(let a=0;a<o;++a,i+=e)s.push(i);for(let a=o;a<t;++a)s.push(-1);return y.assert(s.length===t,()=>"Final length of result must be equal to firstDimension."),s}calculateOutputIndexRowSplit(t,e,n,o){let s=t.length,i=[];for(let a=0;a<s-1;++a){let u=t[a+1]-t[a],l=Math.min(o,u),c=e[a];c===-1&&(l=0);for(let p=0;p<l;++p)i.push(c),c+=n;for(let p=0;p<u-l;++p)i.push(-1)}if(s>0&&i.length!==t[s-1])throw new Error("Invalid row split size.");return i}calculateOutputIndexValueRowID(t,e,n,o){let s=t.length,i=[];if(s===0)return[];let a=0,u=t[0];if(u>=e.length)throw new Error(`Got currentValueRowId=${u}, which is not less than ${e.length}`);let l=e[u];i.push(l);for(let c=1;c<s;++c){let p=t[c];if(p===u)l>=0&&(++a,a<o?l+=n:l=-1);else{if(a=0,u=p,p>=e.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${e.length}`);l=e[p]}i.push(l)}if(i.length!==t.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(t,e,n,o){let s=this.getRowPartitionTensor(t),i=this.getRowPartitionTypeByDimension(t);switch(i){case Mo.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,e,n,o);case Mo.ROW_SPLITS:if(s.length-1>e.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${e.length}`);return this.calculateOutputIndexRowSplit(s,e,n,o);default:throw new Error(`Unsupported partition type: ${Mo[i]}`)}}getFirstDimensionSize(){let t=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let e=this.rowPartitionTypes[0];switch(e){case Mo.FIRST_DIM_SIZE:return t[0];case Mo.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Mo.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${Mo[e]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),n=this.calculateOutputSize(e),o=new Array(this.raggedRank+1);o[o.length-1]=1;for(let u=o.length-2;u>=0;--u)o[u]=o[u+1]*n[u+1];let s=gO(n,!1),i=y.getArrayFromDType(this.valuesDType,y.sizeFromShape(s));if(o[0]*n[0]>0){let u=this.calculateFirstParentOutputIndex(e,o[0],n[0]);for(let l=1;l<=this.raggedRank;++l)u=this.calculateOutputIndex(l-1,u,o[l],n[l]);this.setOutput(this.raggedRank,u,i,s)}return[s,i]}setOutput(t,e,n,o){if(n.length===0)return;let s=this.values,i=n,a=o.slice();a=a.slice(t+1);let u=y.sizeFromShape(a),l=e.length,c=this.defaultValue;if(c.length!==u&&c.length!==1){let d=this.defaultValueShape;B(()=>{let h=R(c,d);c=oa(h,a).dataSync()})}let p=0,m=0,f=0;for(let d=0;d<=l;++d){let h=d<l?e[d]:-1;if(h===f){++f;continue}if(m<f){let g=s.subarray(p*u),x=i.subarray(m*u),b=(f-m)*u;hO(x,g,b)}if(d>=l){let 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I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=Qt({inputs:{x:o},backend:e,attrs:{shape:I}}),A=Qt({inputs:{x:s},backend:e,attrs:{shape:N}}),D=i?E.shape[1]:E.shape[2],F=i?E.shape[2]:E.shape[1],P=a?A.shape[1]:A.shape[2],V=Math.max(g,x),G=e.data.get(E.dataId).values,W=e.data.get(A.dataId).values,q=y.computeStrides(E.shape),H=y.computeStrides(A.shape),[K,X,Z]=i?[q[0],1,q[1]]:[q[0],q[1],1],[et,nt,st]=a?[1,H[1],H[0]]:[H[1],1,H[0]],at=F*P,ot=bt([V,F,P],E.dtype),it=ot.values,mt=e.blockSize;for(let gt=0;gt<V;gt++){let It=gt%g,Rt=gt%x;for(let Dt=0;Dt<F;Dt+=mt){let Ht=Math.min(Dt+mt,F);for(let qt=0;qt<P;qt+=mt){let ce=Math.min(qt+mt,P);for(let ge=0;ge<D;ge+=mt){let ee=Math.min(ge+mt,D);for(let xe=Dt;xe<Ht;xe++)for(let fe=qt;fe<ce;fe++){let Ae=0;for(let De=ge;De<ee;De++){let Pn=G[It*K+xe*X+De*Z],lr=W[De*et+fe*nt+Rt*st];Ae+=Pn*lr}it[gt*at+(xe*P+fe)]+=Ae}}}}}return e.disposeIntermediateTensorInfo(E),e.disposeIntermediateTensorInfo(A),e.makeTensorInfo(w,ot.dtype,ot.values)}var FO={kernelName:ts,backendName:"cpu",kernelFunc:wT};function ftt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m,f,d,h=[];m=wT({inputs:{a:o,b:s},attrs:{transposeA:u,transposeB:l},backend:e}),i&&(f=ya({inputs:{a:m,b:i},backend:e}),h.push(m),m=f),c&&(d=cp(e,m,c,a,p),h.push(m),m=d);for(let x of h)e.disposeIntermediateTensorInfo(x);return m}var OO={kernelName:Ki,backendName:"cpu",kernelFunc:ftt};var dtt=At(Ho,r=>Math.acos(r)),PO={kernelName:Ho,backendName:"cpu",kernelFunc:dtt};var htt=At(qo,r=>Math.acosh(r)),MO={kernelName:qo,backendName:"cpu",kernelFunc:htt};function gtt(r){let{inputs:t,backend:e}=r,n=t;tt(t,"addN");let o=n.map(a=>e.data.get(a.dataId).values),s=bt(n[0].shape,n[0].dtype),i=s.values;for(let a=0;a<n.length;a++){let u=o[a];for(let l=0;l<i.length;l++)i[l]+=u[l]}return e.makeTensorInfo(s.shape,s.dtype,s.values)}var LO={kernelName:Ko,backendName:"cpu",kernelFunc:gtt};function 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,P=bt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W<c.batchSize;++W)for(let q=0;q<c.inChannels;++q)for(let H=0;H<c.inDepth;++H)for(let K=0;K<c.inHeight;++K)for(let X=0;X<c.inWidth;++X){let Z=H-A,et=K-F,nt=X-D,st=0;for(let at=0;at<I;at+=x){let ot=(Z+at)/p;if(!(ot<0||ot>=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it<N;it+=b){let mt=(et+it)/m;if(!(mt<0||mt>=c.outHeight||Math.floor(mt)!==mt))for(let gt=0;gt<E;gt+=w){let It=(nt+gt)/f;if(It<0||It>=c.outWidth||Math.floor(It)!==It)continue;let Rt=G.get(W,ot,mt,It,q);st+=Rt}}}P.set(st*V,W,H,K,X,q)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var ZO={kernelName:Xl,backendName:"cpu",kernelFunc:Ett};function Att(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=bt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=bt(o.shape,"float32",A);for(let F=0;F<c.batchSize;++F)for(let P=0;P<c.inChannels;++P)for(let V=0;V<c.inHeight;++V)for(let G=0;G<c.inWidth;++G){let W=V-I,q=G-w,H=0;for(let K=0;K<x;K+=h){let X=(W+K)/p;if(!(X<0||X>=c.outHeight||Math.floor(X)!==X))for(let Z=0;Z<b;Z+=g){let et=(q+Z)/m;if(et<0||et>=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,X,et,P);H+=nt}}N.set(H*E,F,V,G,P)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var JO={kernelName:Fp,backendName:"cpu",kernelFunc:Att};function Dtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D<c.length;++D)h[D]=d[I++]+(c[D]-p[N++])*f[E++]/Math.sqrt(m[A++]+l),I>=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var QO={kernelName:gs,backendName:"cpu",kernelFunc:Dtt};function $tt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=Qt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Qt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Lo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var tP={kernelName:$i,backendName:"cpu",kernelFunc:$tt};function Rtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=yd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var eP={kernelName:Da,backendName:"cpu",kernelFunc:Rtt};function Ftt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var rP={kernelName:Yl,backendName:"cpu",kernelFunc:Ftt};var Ott=At(go,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r<e.clipValueMin?e.clipValueMin:r}),nP={kernelName:go,backendName:"cpu",kernelFunc:Ott};var Ptt=r=>{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;l<a.length;l++){let c=a[l],p=u[l];n[l]=Math.hypot(c,p)}return e.makeOutput(n,t.shape,"float32")},oP={kernelName:Zl,backendName:"cpu",kernelFunc:Ptt};function ba(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.imag,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var sP={kernelName:Gp,backendName:"cpu",kernelFunc:ba};function Hu(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(h=>h.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Yr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Oo({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Hu({inputs:h,backend:e,attrs:{axis:s}}),b=Hu({inputs:g,backend:e,attrs:{axis:s}}),w=Cr({inputs:{real:x,imag:b},backend:e});return 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Mtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n;tt([o,s],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,x=m.dataFormat==="channelsLast",b=new le(m.filterShape,"float32"),w=m.padInfo.left,I=m.padInfo.top,N=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,A=new le(o.shape,o.dtype,N),D=new le(s.shape,s.dtype,E);for(let F=0;F<h;++F){let P=Math.max(0,Math.ceil((I-F)/f)),V=Math.min(m.outHeight,(m.inHeight+I-F)/f);for(let G=0;G<g;++G){let W=Math.max(0,Math.ceil((w-G)/d)),q=Math.min(m.outWidth,(m.inWidth+w-G)/d);for(let H=0;H<m.inChannels;++H)for(let K=0;K<m.outChannels;++K){let X=0;for(let Z=0;Z<m.batchSize;++Z)for(let et=P;et<V;++et){let nt=F+et*f-I;for(let st=W;st<q;++st){let at=G+st*d-w;x?X+=A.get(Z,nt,at,H)*D.get(Z,et,st,K):X+=A.get(Z,H,nt,at)*D.get(Z,K,et,st)}}b.set(X,F,G,H,K)}}}return 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qt=Ht-X,ce=Math.max(0,Math.ceil(qt/H)),ge=Math.min(W,(A+qt)/H);for(let ee=0;ee<V;++ee){let xe=ee-Z,fe=Math.max(0,Math.ceil(xe/K)),Ae=Math.min(q,(D+xe)/K),De=0;for(let lr=ce;lr<ge;++lr){let Jn=lr*H-qt;for(let Br=fe;Br<Ae;++Br){let je=Br*K-xe,Vr=it*Rt+mt*lr+gt*Br,Gr=w*(A-1-Jn)+I*(D-1-je)+N*Dt;for(let Qn=0;Qn<G;++Qn){let to=x[Vr+It*Qn],Jr=b[Gr+Qn];De+=to*Jr}}}let Pn=nt*Rt+st*Ht+at*ee+ot*Dt;g[Pn]=De}}return e.makeTensorInfo(h.shape,h.dtype,h.values)}var uP={kernelName:ns,backendName:"cpu",kernelFunc:Ltt};function ztt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;tt([o,s],"conv3d");let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=l,x=g.front,b=g.left,w=g.top,I=new le(l.outShape,o.dtype),N=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,A=I.values,D=y.computeStrides(o.shape),F=y.computeStrides(s.shape);for(let P=0;P<l.batchSize;++P){let V=P*D[0],G=P*I.strides[0];for(let W=0;W<l.outDepth;++W){let q=G+W*I.strides[1],H=W*l.strideDepth-x;for(let K=0;K<c;++K){let X=H+K*f;if(X<0||X>=l.inDepth)continue;let Z=K*F[0],et=V+X*D[1];for(let nt=0;nt<l.outHeight;++nt){let st=q+nt*I.strides[2],at=nt*l.strideHeight-w;for(let ot=0;ot<p;++ot){let it=at+ot*d;if(it<0||it>=l.inHeight)continue;let mt=Z+ot*F[1],gt=et+it*D[2];for(let It=0;It<l.outWidth;++It){let Rt=st+It*l.outChannels,Dt=It*l.strideWidth-b;for(let Ht=0;Ht<m;++Ht){let qt=Dt+Ht*h;if(qt<0||qt>=l.inWidth)continue;let ce=mt+Ht*F[2],ge=gt+qt*l.inChannels,ee=ce;for(let xe=0;xe<l.inChannels;++xe){let fe=N[ge+xe];for(let Ae=0;Ae<l.outChannels;++Ae)A[Rt+Ae]+=fe*E[ee+Ae];ee+=l.outChannels}}}}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}var cP={kernelName:os,backendName:"cpu",kernelFunc:ztt};function Btt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;tt([o,s],"conv3dBackpropFilterV2");let l=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(o.shape,u,i,1,a),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,x=p.filterWidth,b=new le(p.filterShape,"float32"),w=b.values,[I,N,E,A]=b.strides,D=e.data.get(s.dataId).values,[F,P,V,G]=c,W=e.data.get(o.dataId).values,[q,H,K,X]=l,Z=p.padInfo.front,et=p.padInfo.left,nt=p.padInfo.top;for(let st=0;st<h;++st){let at=Math.max(0,Math.ceil((Z-st)/m)),ot=Math.min(p.outDepth,(p.inDepth+Z-st)/m),it=st*I;for(let mt=0;mt<g;++mt){let gt=Math.max(0,Math.ceil((nt-mt)/f)),It=Math.min(p.outHeight,(p.inHeight+nt-mt)/f),Rt=mt*N+it;for(let Dt=0;Dt<x;++Dt){let Ht=Math.max(0,Math.ceil((et-Dt)/d)),qt=Math.min(p.outWidth,(p.inWidth+et-Dt)/d),ce=Dt*E+Rt;for(let ge=0;ge<p.inChannels;++ge){let ee=ge*A+ce;for(let xe=0;xe<p.outChannels;++xe){let fe=0;for(let Ae=0;Ae<p.batchSize;++Ae){let De=Ae*q,Pn=Ae*F;for(let lr=at;lr<ot;++lr){let Br=(st+lr*m-Z)*H+De,je=lr*P+Pn;for(let Vr=gt;Vr<It;++Vr){let Qn=(mt+Vr*f-nt)*K+Br,to=Vr*V+je;for(let Jr=Ht;Jr<qt;++Jr){let Go=(Dt+Jr*d-et)*X+Qn,Ni=Jr*G+to;fe+=W[Go+ge]*D[Ni+xe]}}}}w[ee+xe]=fe}}}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var pP={kernelName:$a,backendName:"cpu",kernelFunc:Btt};function Vtt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;tt([o],"conv3dBackpropInputV2");let l=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(u,s.shape,a,1,i),m=new le(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,b=e.data.get(o.dataId).values,[w,I,N,E]=l,A=e.data.get(s.dataId).values,[D,F,P,V]=c,{batchSize:G,filterDepth:W,filterHeight:q,filterWidth:H,inChannels:K,inDepth:X,inHeight:Z,inWidth:et,outChannels:nt,outDepth:st,outHeight:at,outWidth:ot,strideDepth:it,strideHeight:mt,strideWidth:gt}=p,It=W-1-p.padInfo.front,Rt=q-1-p.padInfo.top,Dt=H-1-p.padInfo.left;for(let Ht=0;Ht<G;++Ht)for(let qt=0;qt<K;++qt)for(let ce=0;ce<X;++ce){let ge=ce-It,ee=Math.max(0,Math.ceil(ge/it)),xe=Math.min(st,(W+ge)/it);for(let fe=0;fe<Z;++fe){let Ae=fe-Rt,De=Math.max(0,Math.ceil(Ae/mt)),Pn=Math.min(at,(q+Ae)/mt);for(let lr=0;lr<et;++lr){let Jn=lr-Dt,Br=Math.max(0,Math.ceil(Jn/gt)),je=Math.min(ot,(H+Jn)/gt),Vr=0;for(let Gr=ee;Gr<xe;++Gr){let Qn=Gr*it-ge;for(let to=De;to<Pn;++to){let Jr=to*mt-Ae;for(let Ca=Br;Ca<je;++Ca){let Go=Ca*gt-Jn,Ni=w*Ht+I*Gr+N*to+E*Ca,Er=D*(W-1-Qn)+F*(q-1-Jr)+P*(H-1-Go)+V*qt;for(let va=0;va<nt;++va){let Hd=b[Ni+va],qd=A[Er+va];Vr+=Hd*qd}}}}f[d*Ht+h*ce+g*fe+x*lr+qt]=Vr}}}return e.makeTensorInfo(m.shape,m.dtype,m.values)}var mP={kernelName:Ra,backendName:"cpu",kernelFunc:Vtt};var Gtt=At(ss,r=>Math.cos(r)),fP={kernelName:ss,backendName:"cpu",kernelFunc:Gtt};var Wtt=At(is,r=>Math.cosh(r)),dP={kernelName:is,backendName:"cpu",kernelFunc:Wtt};function Utt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=bt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A<d;A++){let D=A*4,F=b[D],P=b[D+1],V=b[D+2],G=b[D+3],W=w[A];if(W>=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-P)*(m-1)/(g-1):0;for(let K=0;K<h;K++){let X=h>1?F*(p-1)+K*q:.5*(F+V)*(p-1);if(X<0||X>p-1){for(let Z=0;Z<g;Z++)for(let et=0;et<f;et++){let nt=et+Z*E[2]+K*E[1]+A*E[0];x.values[nt]=l}continue}if(u==="bilinear"){let Z=Math.floor(X),et=Math.ceil(X),nt=X-Z;for(let st=0;st<g;st++){let at=g>1?P*(m-1)+st*H:.5*(P+G)*(m-1);if(at<0||at>m-1){for(let gt=0;gt<f;gt++){let It=gt+st*E[2]+K*E[1]+A*E[0];x.values[It]=l}continue}let ot=Math.floor(at),it=Math.ceil(at),mt=at-ot;for(let gt=0;gt<f;gt++){let 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Xtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n;tt([o,s],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),{strideHeight:m,strideWidth:f,filterHeight:d,filterWidth:h}=p,g=new le(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=p.outChannels/p.inChannels,I=e.data.get(o.dataId).values,N=new le(o.shape,o.dtype,I),E=e.data.get(s.dataId).values,A=new le(s.shape,s.dtype,E);for(let D=0;D<d;++D){let F=Math.max(0,Math.ceil((b-D)/m)),P=Math.min(p.outHeight,(p.inHeight+b-D)/m);for(let V=0;V<h;++V){let G=Math.max(0,Math.ceil((x-V)/f)),W=Math.min(p.outWidth,(p.inWidth+x-V)/f);for(let q=0;q<p.outChannels;++q){let H=Math.trunc(q/w),K=q%w,X=0;for(let Z=0;Z<p.batchSize;++Z)for(let et=F;et<P;++et){let nt=D+et*m-b;for(let st=G;st<W;++st){let at=V+st*f-x;X+=N.get(Z,nt,at,H)*A.get(Z,et,st,q)}}g.set(X,D,V,H,K)}}}return e.makeTensorInfo(g.shape,g.dtype,g.values)}var IP={kernelName:Mp,backendName:"cpu",kernelFunc:Xtt};function Ytt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n;tt([o,s],"depthwiseConv2DNativeBackpropInput");let p=y.computeStrides(o.shape),m=y.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),d=new le(f.inShape,"float32"),h=d.values,[g,x,b]=d.strides,w=e.data.get(o.dataId).values,[I,N,E]=p,A=e.data.get(s.dataId).values,[D,F,P]=m,{batchSize:V,filterHeight:G,filterWidth:W,inChannels:q,inHeight:H,inWidth:K,outChannels:X,outHeight:Z,outWidth:et,strideHeight:nt,strideWidth:st}=f,at=G-1-f.padInfo.top,ot=W-1-f.padInfo.left,it=X/q;for(let mt=0;mt<V;++mt)for(let gt=0;gt<q;++gt)for(let It=0;It<H;++It){let Rt=It-at,Dt=Math.max(0,Math.ceil(Rt/nt)),Ht=Math.min(Z,(G+Rt)/nt);for(let qt=0;qt<K;++qt){let ce=qt-ot,ge=Math.max(0,Math.ceil(ce/st)),ee=Math.min(et,(W+ce)/st),xe=0;for(let fe=Dt;fe<Ht;++fe){let Ae=fe*nt-Rt;for(let De=ge;De<ee;++De){let 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SP={kernelName:us,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(P),G=P.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H<f;++H)for(let K=0;K<x;++K){let X=K*I-w.top;for(let Z=0;Z<b;++Z){let et=Z*N-w.left;for(let nt=0;nt<g;++nt){let st=Number.MIN_SAFE_INTEGER;for(let ot=0;ot<E;++ot){let it=X+ot*D;if(it>=0&&it<d)for(let mt=0;mt<A;++mt){let gt=et+mt*F;if(gt>=0&><h){let It=y.locToIndex([H,it,gt,nt],c,y.computeStrides(n.shape)),Rt=y.locToIndex([ot,mt,nt],m,y.computeStrides(o.shape)),Dt=l[It]+p[Rt];Dt>st&&(st=Dt)}}}let at=y.locToIndex([H,K,Z,nt],G,y.computeStrides(P));W[at]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),P,n.dtype),shape:P,dtype:n.dtype}}};var NP={kernelName:eu,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${eu}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let P=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W<m;++W)for(let q=0;q<g;++q){let H=q*w-b.top;for(let K=0;K<x;++K){let X=K*I-b.left;for(let Z=0;Z<h;++Z){let 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n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g<o;g++){let x=Lo({inputs:{x:a},backend:e,attrs:{begin:[g,0],size:[1,s]}}),b=Lo({inputs:{x:u},backend:e,attrs:{begin:[g,0],size:[1,s]}}),w=Cr({inputs:{real:x,imag:b},backend:e}),{real:I,imag:N}=uet(w,t,e),E=S.mergeRealAndImagArrays(I,N);for(let A=0;A<s;A++){let D=S.getComplexWithIndex(E,A);p[g*s+A]=D.real,m[g*s+A]=D.imag}e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(w)}let f=e.makeTensorInfo(l,"float32",p),d=e.makeTensorInfo(l,"float32",m),h=Cr({inputs:{real:f,imag:d},backend:e});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),h}function uet(r,t,e){let n=y.sizeFromShape(r.shape),o=e.data.get(r.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,i=e.data.get(o.complexTensorInfos.imag.dataId).values;if(cet(n)){let a=vT(s,i,n,t,e),u=[r.shape[0],r.shape[1]];if(t){let l=e.makeTensorInfo(u,"float32",a.real),c=e.makeTensorInfo(u,"float32",a.imag),p=e.makeTensorInfo([],"float32",y.createScalarValue(n,"float32")),m=Yr({inputs:{x:p},backend:e}),f=rg.kernelFunc({inputs:{a:l,b:p},backend:e}),d=rg.kernelFunc({inputs:{a:c,b:m},backend:e}),h=e.data.get(f.dataId).values,g=e.data.get(d.dataId).values;return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),{real:h,imag:g}}return a}else{let a=S.mergeRealAndImagArrays(s,i),u=pet(a,n,t);return S.splitRealAndImagArrays(u)}}function cet(r){return(r&r-1)===0}function vT(r,t,e,n,o){if(e===1)return{real:r,imag:t};let 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e.makeTensorInfo(F.shape,F.dtype,F.values)}var QP={kernelName:nu,backendName:"cpu",kernelFunc:Fet};function Oet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;tt([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=e.data.get(a.dataId).values,d=bt(m.outShape,a.dtype,kw(f,a.shape,a.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,w=m.effectiveFilterHeight,I=m.effectiveFilterWidth,N=I-1-m.padInfo.left,E=w-1-m.padInfo.top,A=bt(a.shape,"float32"),D=e.data.get(o.dataId).values,F=bt(o.shape,"float32",D);for(let P=0;P<m.batchSize;++P)for(let V=0;V<m.inChannels;++V)for(let G=0;G<m.inHeight;++G)for(let W=0;W<m.inWidth;++W){let q=G-E,H=W-N,K=0;for(let X=0;X<w;X+=x){let Z=(q+X)/h;if(!(Z<0||Z>=m.outHeight||Math.floor(Z)!==Z))for(let et=0;et<I;et+=b){let nt=(H+et)/g;if(nt<0||nt>=m.outWidth||Math.floor(nt)!==nt)continue;let 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wrt=11920928955078125e-23,zM=Math.log(wrt)+2,Irt=At(Ys,r=>{let t=r>-zM,e=r<zM,n=Math.exp(r),o;return e?o=n:t?o=r:o=Math.log(1+n),o}),BM={kernelName:Ys,backendName:"cpu",kernelFunc:Irt};function Crt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;tt([o],"spaceToBatchND");let a=y.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let E=1+s.length;E<o.shape.length;++E)u.push([0,0]);let l=Ew.kernelFunc({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,s,a,!1),p=S.getPermuted(c.length,s.length,!1),m=S.getReshapedPermuted(l.shape,s,a,!1),h=Qt({inputs:{x:l},backend:e,attrs:{shape:c}}),b=Ge({inputs:{x:h},backend:e,attrs:{perm:p}}),N=Qt({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(b),N}var VM={kernelName:Wi,backendName:"cpu",kernelFunc:Crt};function vrt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
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${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
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|
${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${i.shape}`);let a=e.data.get(n.dataId).values,u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=e.data.get(i.dataId).values[0],[p,m,f,d,h]=ww(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var GM={kernelName:su,backendName:"cpu",kernelFunc:vrt};function Srt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.data.get(o.dataId).values),a=e.data.get(n.dataId).values,u=Array.from(e.data.get(s.dataId).values),[l,c,p]=Iw(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var WM={kernelName:ll,backendName:"cpu",kernelFunc:Srt};function Nrt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${s.shape}`);if(o.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=Id(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var UM={kernelName:iu,backendName:"cpu",kernelFunc:Nrt};function krt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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${s.shape}`);if(o.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=Id(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var HM={kernelName:au,backendName:"cpu",kernelFunc:krt};function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1,d=e.bufferSync(o),h;switch(s.dtype){case"bool":{let g=e.bufferSync(s),x=!!e.data.get(i.dataId).values[0];h=wi(d,g,a,m,c,l,u,p,x,f);break}case"float32":{let g=e.bufferSync(s),x=e.data.get(i.dataId).values[0];h=wi(d,g,a,m,c,l,u,p,x,f);break}case"int32":{let g=e.bufferSync(s),x=e.data.get(i.dataId).values[0];h=wi(d,g,a,m,c,l,u,p,x,f);break}case"string":{let g=e.bufferSync(s),x=y.decodeString(e.data.get(i.dataId).values[0]);h=wi(d,g,a,m,c,l,u,p,x,f);break}default:throw new Error(`Unsupported type ${s.dtype}`)}return e.makeTensorInfo(a,h.dtype,h.values)}var qM={kernelName:ul,backendName:"cpu",kernelFunc:Trt};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=new Array(o.shape.length).fill(0),c=o.shape.slice();return u.map(p=>{let m=[...c];m[a]=p;let f=Lo({inputs:{x:o},backend:e,attrs:{begin:l,size:m}});return l[a]+=p,f})}var KM={kernelName:Ui,backendName:"cpu",kernelFunc:_rt};var jM={kernelName:lu,backendName:"cpu",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,n=t;tt(e,"square");let o=n.data.get(e.dataId).values,s=new Float32Array(o.length);for(let a=0;a<o.length;++a){let u=o[a];s[a]=u*u}return{dataId:n.write(s,e.shape,e.dtype),shape:e.shape,dtype:e.dtype}}};var Ert=At(yo,(r,t)=>{let e=t;return 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Drt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.data.get(c.dataId).values,f=e.data.get(p.dataId).values,[d,h]=ip(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var ZM={kernelName:uu,backendName:"cpu",kernelFunc:Drt};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values[0],[l,c,p]=ap(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var JM={kernelName:cu,backendName:"cpu",kernelFunc:$rt};function Rrt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.data.get(s.dataId).values,a=lp(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var QM={kernelName:pu,backendName:"cpu",kernelFunc:Rrt};var Frt=At(ri,r=>Math.tan(r)),tL={kernelName:ri,backendName:"cpu",kernelFunc:Frt};var Ort=At(ni,r=>Math.tanh(r)),eL={kernelName:ni,backendName:"cpu",kernelFunc:Ort};function Prt(r){let{inputs:t,backend:e}=r,{tensor:n,indices:o,updates:s}=t,{sliceRank:i,numUpdates:a,sliceSize:u,strides:l,outputSize:c}=S.calculateShapes(s,o,n.shape),p=!1,m=e.bufferSync(o),f=e.bufferSync(s),d=e.bufferSync(n),h=wi(m,f,n.shape,c,u,a,i,l,d,p);return e.makeTensorInfo(n.shape,h.dtype,h.values)}var rL={kernelName:sl,backendName:"cpu",kernelFunc:Prt};function 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pp={},Aw={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function TT(r,t){pp[r]=t}function jn(r,t){if(!(r in pp)||t!=null){let n=Zrt(r,t);if(n!==null)pp[r]=n;else return console.log("Could not get context for WebGL version",r),null}let e=pp[r];return e==null||e.isContextLost()?(delete pp[r],jn(r)):(e.disable(e.DEPTH_TEST),e.disable(e.STENCIL_TEST),e.disable(e.BLEND),e.disable(e.DITHER),e.disable(e.POLYGON_OFFSET_FILL),e.disable(e.SAMPLE_COVERAGE),e.enable(e.SCISSOR_TEST),e.enable(e.CULL_FACE),e.cullFace(e.BACK),pp[r])}function Yrt(r){if(!L().getBool("IS_SAFARI")&&typeof OffscreenCanvas!="undefined"&&r===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function Zrt(r,t){if(r!==1&&r!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let e=t==null?Yrt(r):t;return e.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete pp[r]},!1),L().getBool("SOFTWARE_WEBGL_ENABLED")&&(Aw.failIfMajorPerformanceCaveat=!1),r===1?e.getContext("webgl",Aw)||e.getContext("experimental-webgl",Aw):e.getContext("webgl2",Aw)}var qu;(function(r){r[r.DENSE=0]="DENSE",r[r.SHARED_BATCH=1]="SHARED_BATCH"})(qu||(qu={}));var Zr;(function(r){r[r.RENDER=0]="RENDER",r[r.UPLOAD=1]="UPLOAD",r[r.PIXELS=2]="PIXELS",r[r.DOWNLOAD=3]="DOWNLOAD"})(Zr||(Zr={}));var Lr;(function(r){r[r.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",r[r.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",r[r.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",r[r.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",r[r.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(Lr||(Lr={}));function mp(r,t){return[t,r]}function cL(r,t){return r*t}function ig(r){let t=y.sizeFromShape(r),e=Math.ceil(t/4);return y.sizeToSquarishShape(e)}function wa(r,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(r/2))]}function pL(r,t){let[e,n]=wa(r,t);return e*n*4}function ag(r,t){let e=r,n,o,s,i,a,u,l,c,p,m;return L().getNumber("WEBGL_VERSION")===2?(n=e.R32F,o=e.R16F,s=e.RGBA16F,i=e.RGBA32F,a=e.RED,l=4,c=1,p=e.HALF_FLOAT,m=e.FLOAT,u=e.RGBA8):(n=r.RGBA,o=r.RGBA,s=r.RGBA,i=e.RGBA,a=r.RGBA,l=4,c=4,p=t!=null?t.HALF_FLOAT_OES:null,m=r.FLOAT,u=r.RGBA),{internalFormatFloat:n,internalFormatHalfFloat:o,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:a,downloadTextureFormat:u,downloadUnpackNumChannels:l,defaultNumChannels:c,textureTypeHalfFloat:p,textureTypeFloat:m}}function ht(r,t){let e=t();return L().getBool("DEBUG")&&Jrt(r),e}function Jrt(r){let t=r.getError();if(t!==r.NO_ERROR)throw new Error("WebGL Error: "+mL(r,t))}var Qrt=596e-10,tnt=65504;function AT(r){return!!(L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||r===0||Qrt<Math.abs(r)&&Math.abs(r)<tnt)}function mL(r,t){switch(t){case r.NO_ERROR:return"NO_ERROR";case r.INVALID_ENUM:return"INVALID_ENUM";case r.INVALID_VALUE:return"INVALID_VALUE";case r.INVALID_OPERATION:return"INVALID_OPERATION";case r.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case r.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case r.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function Sd(r,t){return Ll(r,()=>r.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function DT(r,t){let e=Ll(r,()=>r.createShader(r.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(ht(r,()=>r.shaderSource(e,t)),ht(r,()=>r.compileShader(e)),r.getShaderParameter(e,r.COMPILE_STATUS)===!1)throw console.log(r.getShaderInfoLog(e)),new Error("Failed to compile vertex shader.");return e}function $T(r,t){let e=Ll(r,()=>r.createShader(r.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ht(r,()=>r.shaderSource(e,t)),ht(r,()=>r.compileShader(e)),L().get("ENGINE_COMPILE_ONLY"))return e;if(r.getShaderParameter(e,r.COMPILE_STATUS)===!1)throw Fw(t,r.getShaderInfoLog(e)),new Error("Failed to compile fragment shader.");return e}var ent=/ERROR: [0-9]+:([0-9]+):/g;function Fw(r,t){let e=ent.exec(t);if(e==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(r);return}let n=+e[1],o=r.split(`
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`),s=o.length.toString().length+2,i=o.map((p,m)=>y.rightPad((m+1).toString(),s)+p),a=0;for(let p=0;p<i.length;p++)a=Math.max(i[p].length,a);let u=i.slice(0,n-1),l=i.slice(n-1,n),c=i.slice(n);console.log(u.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${y.rightPad(l[0],a)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
|
|
`))}function RT(r){return Ll(r,()=>r.createProgram(),"Unable to create WebGLProgram.")}function FT(r,t){if(ht(r,()=>r.linkProgram(t)),!L().get("ENGINE_COMPILE_ONLY")&&r.getProgramParameter(t,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function lg(r,t){if(ht(r,()=>r.validateProgram(t)),r.getProgramParameter(t,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function OT(r,t){let e=Ll(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),ht(r,()=>r.bufferData(r.ARRAY_BUFFER,t,r.STATIC_DRAW)),e}function PT(r,t){let e=Ll(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ht(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,e)),ht(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,t,r.STATIC_DRAW)),e}function rnt(){return L().getNumber("WEBGL_VERSION")===2?1:4}function MT(r){return Ll(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function LT(r,t){let e=L().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||t<=0){let n=`[${r}x${t}]`;throw new Error("Requested texture size "+n+" is invalid.")}if(r>e||t>e){let n=`[${r}x${t}]`,o=`[${e}x${e}]`;throw new Error("Requested texture size "+n+" greater than WebGL maximum on this browser / GPU "+o+".")}}function zT(r){return Ll(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Ow(r,t,e,n,o,s,i){let a=r.getAttribLocation(t,e);return a===-1?!1:(ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,n)),ht(r,()=>r.vertexAttribPointer(a,o,r.FLOAT,!1,s,i)),ht(r,()=>r.enableVertexAttribArray(a)),!0)}function fL(r,t,e){hL(r,e),ht(r,()=>r.activeTexture(r.TEXTURE0+e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,t))}function nnt(r,t){hL(r,t),ht(r,()=>r.activeTexture(r.TEXTURE0+t)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function BT(r,t,e){return Ll(r,()=>r.getUniformLocation(t,e),'uniform "'+e+'" not present in 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Ku(r,t){if(r=r.slice(-2),t=t.slice(-2),y.arraysEqual(r,t)||!r.length||!t.length||r[0]===0||r[1]===0||t[0]===0||t[1]===0)return!0;if(r.length!==t.length){let e=r[r.length-1],n=t[t.length-1];if(e===n||Dw(e)&&Dw(n)&&(r[0]===1||t[0]===1))return!0}return r[1]===t[1]&&Dw(r[0])&&Dw(t[0])}var $w,Rw;function UT(r){if($w==null){let t=jn(r);$w=t.getParameter(t.MAX_TEXTURE_SIZE)}return $w}function snt(){$w=null}function int(){Rw=null}function HT(r){if(Rw==null){let t=jn(r);Rw=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Rw)}function qT(r){if(r===0)return 0;let t,e=jn(r);return Xn(e,"EXT_disjoint_timer_query_webgl2")&&r===2?t=2:Xn(e,"EXT_disjoint_timer_query")?t=1:t=0,t}function Xn(r,t){return r.getExtension(t)!=null}function Mw(r){try{if(jn(r)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function KT(r){if(r===0)return!1;let t=jn(r);if(r===1){if(!Xn(t,"OES_texture_float"))return!1}else if(!Xn(t,"EXT_color_buffer_float"))return!1;return ET(t)}function jT(r){if(r===0)return!1;let t=jn(r);if(r===1){if(!Xn(t,"OES_texture_float")||!Xn(t,"WEBGL_color_buffer_float"))return!1}else{if(Xn(t,"EXT_color_buffer_float"))return ET(t);let n="EXT_color_buffer_half_float";if(Xn(t,n)){let o=t.getExtension(n);return ant(t,o)}return!1}return ET(t)}function ET(r){let t=ag(r),e=r.createTexture();r.bindTexture(r.TEXTURE_2D,e);let n=1,o=1;r.texImage2D(r.TEXTURE_2D,0,t.internalFormatFloat,n,o,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,s),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0);let i=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(e),r.deleteFramebuffer(s),i}function ant(r,t){let e=ag(r,t),n=r.createTexture();r.bindTexture(r.TEXTURE_2D,n);let o=1,s=1;r.texImage2D(r.TEXTURE_2D,0,e.internalFormatHalfFloat,o,s,0,e.textureFormatFloat,e.textureTypeHalfFloat,null);let i=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,i),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,n,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(n),r.deleteFramebuffer(i),a}function XT(r){return r!==2?!1:jn(r).fenceSync!=null}function Ii(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Et=L();Et.registerFlag("HAS_WEBGL",()=>Et.getNumber("WEBGL_VERSION")>0);Et.registerFlag("WEBGL_VERSION",()=>Mw(2)?2:Mw(1)?1:0);Et.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Et.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Et.get("WEBGL_VERSION")===2);Et.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Et.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Et.registerFlag("WEBGL_PACK",()=>Et.getBool("HAS_WEBGL"));Et.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_CLIP",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_PACK_REDUCE",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_LAZILY_UNPACK",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_CONV_IM2COL",()=>Et.getBool("WEBGL_PACK"));Et.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>UT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>HT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Et.getNumber("WEBGL_VERSION");return r===0?0:qT(r)});Et.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Et.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!xu.isMobile());Et.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>KT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Et.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Et.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Et.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>jT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_FENCE_API_ENABLED",()=>XT(Et.getNumber("WEBGL_VERSION")));Et.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Et.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Et.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Et.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>xu.isMobile()?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});Et.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Et.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Et.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Et.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);Et.registerFlag("WEBGL_EXP_CONV",()=>!1);Et.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>Et.getBool("IS_TEST"));Et.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);Et.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);Et.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);Et.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function We(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",t="in",e="out",n="in",o="texture",s="outputColor",i="out vec4 outputColor;",a=L().getBool("WEBGL2_ISNAN_CUSTOM")?`
|
|
bool isnan_custom(float val) {
|
|
uint floatToUint = floatBitsToUint(val);
|
|
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`:"",u="",l=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(r="",t="attribute",e="varying",n="varying",o="texture2D",s="gl_FragColor",i="",a=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,u=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,l=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Ci(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function fp(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function lnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function gL(r,t,e="index"){let n=r.map((s,i)=>i),o=lnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function _d(r){let t=y.computeStrides(r).map(e=>e.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function Ed(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var Lw=`
|
|
const float FLOAT_MAX = 1.70141184e38;
|
|
const float FLOAT_MIN = 1.17549435e-38;
|
|
|
|
lowp vec4 encode_float(highp float v) {
|
|
if (isnan(v)) {
|
|
return vec4(255, 255, 255, 255);
|
|
}
|
|
|
|
highp float av = abs(v);
|
|
|
|
if(av < FLOAT_MIN) {
|
|
return vec4(0.0, 0.0, 0.0, 0.0);
|
|
} else if(v > FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
|
|
} else if(v < -FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
|
|
}
|
|
|
|
highp vec4 c = vec4(0,0,0,0);
|
|
|
|
highp float e = floor(log2(av));
|
|
highp float m = exp2(fract(log2(av))) - 1.0;
|
|
|
|
c[2] = floor(128.0 * m);
|
|
m -= c[2] / 128.0;
|
|
c[1] = floor(32768.0 * m);
|
|
m -= c[1] / 32768.0;
|
|
c[0] = floor(8388608.0 * m);
|
|
|
|
highp float ebias = e + 127.0;
|
|
c[3] = floor(ebias / 2.0);
|
|
ebias -= c[3] * 2.0;
|
|
c[2] += floor(ebias) * 128.0;
|
|
|
|
c[3] += 128.0 * step(0.0, -v);
|
|
|
|
return c / 255.0;
|
|
}
|
|
`;var{getBroadcastDims:xL}=S;function yL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=zw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
|
|
`),s=r.map(f=>unt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`
|
|
`),i=t.texShape,a=We(),u=mnt(a),l,c,p=hnt(a);return t.isPacked?(l=cnt(t.logicalShape,i,e.enableShapeUniforms),c=dnt(a)):(l=pnt(t.logicalShape,i,e.enableShapeUniforms),c=fnt(a)),e.packedInputs&&(p+=bnt),[p,u,c,o,l,s,e.userCode].join(`
|
|
`)}function Dd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Dnt(r,t);case 1:return Rnt(r,t);case 2:return Ont(r,t);case 3:return Mnt(r,t);case 4:return znt(r,t);case 5:return Bnt(r);case 6:return Vnt(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function bL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Ant(r);case 1:return $nt(r,t);case 2:return Fnt(r,t);case 3:return Pnt(r,t);default:return Lnt(r,t)}}function unt(r,t,e=!1,n){let o="";e?o+=bL(r,n):o+=Dd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Gnt(r,t):o+=Wnt(r,t)),o}function cnt(r,t,e){switch(r.length){case 0:return wL();case 1:return wnt(r,t,e);case 2:return _nt(r,t,e);case 3:return Cnt(r,t,e);default:return Snt(r,t,e)}}function pnt(r,t,e){switch(r.length){case 0:return wL();case 1:return Int(r,t,e);case 2:return Ent(r,t,e);case 3:return vnt(r,t,e);case 4:return Nnt(r,t,e);case 5:return knt(r,t);case 6:return Tnt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function mnt(r){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${r.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function fnt(r){return`
|
|
void setOutput(float val) {
|
|
${r.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function dnt(r){return`
|
|
void setOutput(vec4 val) {
|
|
${r.output} = val;
|
|
}
|
|
`}function hnt(r){return`${r.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${r.varyingFs} vec2 resultUV;
|
|
${r.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${r.defineSpecialNaN}
|
|
${r.defineSpecialInf}
|
|
${r.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${gnt}
|
|
${xnt}
|
|
${ynt}
|
|
`}var gnt=`
|
|
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
|
|
int texelIndex = index / 2;
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,xnt=`
|
|
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
|
|
int texNumC, int row, int col) {
|
|
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,ynt=`
|
|
vec2 packedUVfrom3D(int texNumR, int texNumC,
|
|
int texelsInBatch, int texelsInLogicalRow, int b,
|
|
int row, int col) {
|
|
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,bnt=`
|
|
float getChannel(vec4 frag, vec2 innerDims) {
|
|
vec2 modCoord = mod(innerDims, 2.);
|
|
return modCoord.x == 0. ?
|
|
(modCoord.y == 0. ? frag.r : frag.g) :
|
|
(modCoord.y == 0. ? frag.b : frag.a);
|
|
}
|
|
float getChannel(vec4 frag, int dim) {
|
|
float modCoord = mod(float(dim), 2.);
|
|
return modCoord == 0. ? frag.r : frag.g;
|
|
}
|
|
`;function wL(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function wnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${n[1]}.0);
|
|
}
|
|
`:n[1]===1?e?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${n[0]}.0);
|
|
}
|
|
`:e?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
|
|
}
|
|
`}function Int(r,t,e){return t[0]===1?e?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?e?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:e?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function Cnt(r,t,e){if(e)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
int b = index / ${s};
|
|
index -= b * ${s};
|
|
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function vnt(r,t,e){if(e)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${fp(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let n=Ci(["r","c","d"],r);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function Snt(r,t,e){if(e)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l<r.length-1;l++)i*=r[r.length-l-1],a=`
|
|
int b${l} = index / ${i};
|
|
index -= b${l} * ${i};
|
|
`+a,u=`b${l}, `+u;return`
|
|
ivec${r.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
${a}
|
|
|
|
int b = index / ${s};
|
|
index -= b * ${s};
|
|
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec${r.length}(${u});
|
|
}
|
|
`}function Nnt(r,t,e){if(e)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${fp(["r","c","d","d2"],r)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let n=Ci(["r","c","d","d2"],r);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function knt(r,t){let e=Ci(["r","c","d","d2","d3"],r);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${e}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function Tnt(r,t){let e=Ci(["r","c","d","d2","d3","d4"],r);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${e}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function _nt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let o=Math.ceil(r[1]/2);return e?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Ent(r,t,e){return y.arraysEqual(r,t)?e?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:r[1]===1?e?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:r[0]===1?e?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:e?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${r[1]};
|
|
int c = index - r * ${r[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function dp(r){return`offset${r}`}function Ant(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=We();return`
|
|
vec4 ${e}() {
|
|
return ${n.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function Dnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
|
|
float ${n}() {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let i=dp(e);if(t)return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], ${i});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`;let[a,u]=r.shapeInfo.texShape;return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${a}, ${u}, ${i});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function $nt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=We();if(t)return`
|
|
vec4 ${n}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${s.texture2D}(${e}, uv);
|
|
}
|
|
`;let i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${i[0]}, ${i[1]}, index);
|
|
return ${s.texture2D}(${e}, uv);
|
|
}
|
|
`}function Rnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${$d(r)}
|
|
}
|
|
`;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return`
|
|
float ${n}(int index) {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let a=dp(e);return i===1?t?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / float(${e}TexShape[0]));
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${s}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:s===1?t?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${a}) + 0.5) / float(${e}TexShape[1]), 0.5);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${i}.0, 0.5);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:t?`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], index + ${a});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, index + ${a});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function Fnt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=We();if(s!=null&&y.arraysEqual(e,s))return t?`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${i}.0);
|
|
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${o}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`;let l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(e[1]/2);return`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`}function Ont(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return`
|
|
float ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=s[0],f=s[1];return`
|
|
float ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:i,keptDims:a}=y.squeezeShape(e),u=i;if(u.length<e.length){let m=Rd(r,u),f=["row","col"];return`
|
|
${Dd(m,t)}
|
|
float ${o}(int row, int col) {
|
|
return ${o}(${Fd(f,a)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
|
|
${$d(r)}
|
|
}
|
|
`;let l=s[0],c=s[1],p=dp(n);return c===1?t?`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:l===1?t?`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${o}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n}Shape[1] + col + ${p};
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${e[1]} + col + ${p};
|
|
vec2 uv = uvFromFlat(${l}, ${c}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Pnt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(e[0]===1){let m=e.slice(1),f=[1,2],d=Rd(r,m),h=["b","row","col"];return`
|
|
${bL(d,t)}
|
|
vec4 ${o}(int b, int row, int col) {
|
|
return ${o}(${Fd(h,f)});
|
|
}
|
|
`}let a=We();if(t)return`
|
|
vec4 ${o}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`;let u=i[0],l=i[1],c=Math.ceil(e[2]/2),p=c*Math.ceil(e[1]/2);return`
|
|
vec4 ${o}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${u}, ${l}, ${p}, ${c}, b, row, col);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`}function Mnt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[1]*e[2],i=e[2],{newShape:a,keptDims:u}=y.squeezeShape(e),l=a;if(l.length<e.length){let h=Rd(r,l),g=["row","col","depth"];return`
|
|
${Dd(h,t)}
|
|
float ${o}(int row, int col, int depth) {
|
|
return ${o}(${Fd(g,u)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${s}, ${i}, 1)));
|
|
${$d(r)}
|
|
}
|
|
`;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return t?`
|
|
float ${o}(int row, int col, int depth) {
|
|
int stride1 = ${n}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${i}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(m===i&&f==null)return t?`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let d=dp(n);return t?`
|
|
float ${o}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${n}Shape[1] * ${n}Shape[2];
|
|
int stride1 = ${n}Shape[2];
|
|
int index = row * stride0 + col * stride1 + depth + ${d};
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s} + col * ${i} + depth + ${d};
|
|
vec2 uv = uvFromFlat(${p}, ${m}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Lnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=We();if(t)return`
|
|
vec4 ${n}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${e}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${e}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${e}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${o.texture2D}(${e}, uv);
|
|
}
|
|
`;let s=r.shapeInfo.logicalShape,i=s.length,a=r.shapeInfo.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=Math.ceil(s[i-1]/2),m=p*Math.ceil(s[i-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<i-1;h++)f=`int b${h}, `+f,m*=s[i-h-1],d=`b${h} * ${m} + `+d;return`
|
|
vec4 ${n}(${f}) {
|
|
int index = ${d};
|
|
int texR = index / ${c};
|
|
int texC = index - texR * ${c};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
|
|
return ${o.texture2D}(${e}, uv);
|
|
}
|
|
`}function znt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[3],i=e[2]*s,a=e[1]*i,{newShape:u,keptDims:l}=y.squeezeShape(e);if(u.length<e.length){let b=Rd(r,u),w=["row","col","depth","depth2"];return`
|
|
${Dd(b,t)}
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
return ${o}(${Fd(w,l)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${a}, ${i}, ${s}, 1)));
|
|
${$d(r)}
|
|
}
|
|
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1],d=`int stride2 = ${n}Shape[3];`,h=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(f===a&&c==null)return t?`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
${d}
|
|
${h}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${i}, ${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===s&&c==null)return t?`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}TexShape[1], ${n}TexShape[0]);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${e[1]*e[2]}, ${e[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let x=dp(n);return t?`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${d}
|
|
${h}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${x});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${i} +
|
|
depth * ${s} + depth2;
|
|
vec2 uv = uvFromFlat(${m}, ${f}, index + ${x});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Bnt(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,i=t[2]*s,a=t[1]*i,{newShape:u,keptDims:l}=y.squeezeShape(t);if(u.length<t.length){let h=Rd(r,u),g=["row","col","depth","depth2","depth3"];return`
|
|
${Dd(h)}
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${n}(${Fd(g,l)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${a}, ${i}, ${s}, ${o})) +
|
|
depth3;
|
|
${$d(r)}
|
|
}
|
|
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===a&&c==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`;if(f===o&&c==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`;let d=dp(e);return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${i} + depth * ${s} +
|
|
depth2 * ${o} + depth3 + ${d};
|
|
vec2 uv = uvFromFlat(${m}, ${f}, index);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function Vnt(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=y.squeezeShape(t);if(o.length<t.length){let g=Rd(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Dd(g)}
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${n}(${Fd(x,s)});
|
|
}
|
|
`}let i=t[5],a=t[4]*i,u=t[3]*a,l=t[2]*u,c=t[1]*l;if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${l}, ${u}, ${a})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${$d(r)}
|
|
}
|
|
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${l}, ${u}, ${a}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${f}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`;if(d===i&&p==null)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${f}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`;let h=dp(e);return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${c} + col * ${l} + depth * ${u} +
|
|
depth2 * ${a} + depth3 * ${i} + depth4 + ${h};
|
|
vec2 uv = uvFromFlat(${f}, ${d}, index);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function $d(r){let t=r.name,e=y.sizeFromShape(r.shapeInfo.logicalShape);return e<2?`return ${t};`:`
|
|
for (int i = 0; i < ${e}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function Gnt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=xL(r.shapeInfo.logicalShape,t.logicalShape),u=zt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`
|
|
`);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(h&&!x)i===1?f=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:f=`
|
|
return vec4(outputValue.x);
|
|
`;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f="return vec4(outputValue.x);":a.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${o}() {
|
|
${u} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${n}(${m});
|
|
${f}
|
|
}
|
|
`}function Wnt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return`
|
|
float ${o}() {
|
|
return sampleTexture(${e}, resultUV);
|
|
}
|
|
`;let l=zt(u),c=xL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
|
|
`);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
|
|
float ${o}() {
|
|
${l} coords = getOutputCoords();
|
|
${m}
|
|
return get${n}(${d});
|
|
}
|
|
`}function zt(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function zw(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.length<s||i;return{useSqueezeShape:u,uniformShape:u?a:t,keptDims:o}}function Rd(r,t){let e=JSON.parse(JSON.stringify(r));return e.shapeInfo.logicalShape=t,e}function Fd(r,t){return t.map(e=>r[e]).join(", ")}function CL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=yL(o,i,t),u=$T(r.gl,a),l=r.createProgram(u);return L().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},YT(r,t,l)))}function YT(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,"NAN",!1),L().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,"outShape",c),a=r.getUniformLocation(e,"outShapeStrides",c),i=r.getUniformLocation(e,"outTexShape",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function IL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function vL(r,t,e,n,o){t.program.enableShapeUniforms||(IL(t.inShapeInfos,e),IL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u<e.length;++u){let l=e[u],{uniform:c,offset:p,shape:m,texShape:f}=t.variablesLocations[u];if(m){let{uniformShape:d}=zw(t.program.packedInputs,l.shape,l.texData.texShape);switch(d.length){case 1:r.gl.uniform1iv(m,new Int32Array(d));break;case 2:r.gl.uniform2iv(m,new Int32Array(d));break;case 3:r.gl.uniform3iv(m,new Int32Array(d));break;case 4:r.gl.uniform4iv(m,new Int32Array(d));break;default:break}}if(f&&r.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(y.sizeFromShape(l.shape)<2)r.gl.uniform1f(c,l.uniformValues[0]);else{let d=l.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),r.gl.uniform1fv(c,d)}continue}l.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,l.texData.slice.flatOffset),r.setInputMatrixTexture(l.texData.texture.texture,c,u)}}let a=t.outShapeLocation;if(a)switch(n.shape.length){case 1:r.gl.uniform1iv(a,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(a,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(a,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(a,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let u=y.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:r.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:r.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}if(t.outTexShapeLocation&&r.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&o)for(let u=0;u<t.program.customUniforms.length;++u){let l=t.program.customUniforms[u],c=t.customUniformLocations[u],p=o[u];if(l.type==="float")r.gl.uniform1fv(c,p);else if(l.type==="vec2")r.gl.uniform2fv(c,p);else if(l.type==="vec3")r.gl.uniform3fv(c,p);else if(l.type==="vec4")r.gl.uniform4fv(c,p);else if(l.type==="int")r.gl.uniform1iv(c,p);else if(l.type==="ivec2")r.gl.uniform2iv(c,p);else if(l.type==="ivec3")r.gl.uniform3iv(c,p);else if(l.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}r.executeProgram()}function SL(r,t,e){let n="";t.concat(e).forEach(i=>{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=zw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${L().getNumber("WEBGL_VERSION")}`,s}function de(r){return L().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Bw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=qu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?fp(["r","c","d"],t):Ci(["r","c","d"],t)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${e.output} = result;
|
|
}
|
|
`}};var Vw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=qu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?fp(["r","c","d"],t):Ci(["r","c","d"],t)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${e.output} = result;
|
|
}
|
|
`}};var Gw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`
|
|
${Lw}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${e.output} = encode_float(x);
|
|
}
|
|
`}};var Ww=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`
|
|
${Lw}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${e.output} = encode_float(x);
|
|
}
|
|
`}};var qnt={R:0,G:1,B:2,A:3},cg=class{constructor(t,e=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let o=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let i="";for(let a=0;a<n.length;a++){let u=n[a];i+=`
|
|
if(offset == ${a}) {
|
|
result = values[${qnt[u]}];
|
|
}`}this.userCode=`
|
|
${this.enableShapeUniforms?Ed():_d(t)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int flatIndex = getFlatIndex(coords);
|
|
float result = 0.;
|
|
int offset = imod(flatIndex, ${n.length});
|
|
|
|
flatIndex = idiv(flatIndex, ${n.length}, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
if (r < texShape[0]) {
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
vec4 values = ${o.texture2D}(A, uv);
|
|
${i}
|
|
}
|
|
${o.output} = vec4(${s}, 0., 0., 0.);
|
|
}
|
|
`}};var Uw=class{constructor(t,e=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=We();this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let o="",s="result";e&&(s="floor(result * 255. + 0.5)");for(let i=0;i<=1;i++)for(let a=0;a<=1;a++){let u=i*2+a;o+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${t[2]}`}) {
|
|
localCoords[2] += ${a};
|
|
if (localCoords[1] + ${i} < ${this.enableShapeUniforms?"outShape[1]":`${t[1]}`}) {
|
|
localCoords[1] += ${i};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${u}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${u}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${u}] = values[2];
|
|
} else {
|
|
result[${u}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?Ed():_d(t)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${o}
|
|
|
|
${n.output} = ${s};
|
|
}
|
|
`}};var f1={};Kt(f1,{bindVertexProgramAttributeStreams:()=>s1,createBufferFromOutputTexture:()=>l1,createFloat16MatrixTexture:()=>e1,createFloat16PackedMatrixTexture:()=>o1,createFloat32MatrixTexture:()=>t1,createIndexBuffer:()=>QT,createPackedMatrixTexture:()=>n1,createUnsignedBytesMatrixTexture:()=>r1,createVertexBuffer:()=>JT,createVertexShader:()=>ZT,downloadByteEncodedFloatMatrixFromOutputTexture:()=>c1,downloadFloat32MatrixFromBuffer:()=>u1,downloadMatrixFromPackedOutputTexture:()=>m1,downloadPackedMatrixFromBuffer:()=>p1,getInternalFormatForFloat16MatrixTexture:()=>qw,getInternalFormatForFloat16PackedMatrixTexture:()=>Xw,getInternalFormatForFloat32MatrixTexture:()=>Hw,getInternalFormatForPackedMatrixTexture:()=>jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Kw,uploadDenseMatrixToTexture:()=>i1,uploadPixelDataToTexture:()=>a1});function ZT(r){let t=We(),e=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return DT(r,e)}function JT(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return OT(r,t)}function QT(r){let t=new Uint16Array([0,1,2,2,1,3]);return PT(r,t)}function pg(r,t,e,n,o,s){LT(t,e);let i=MT(r),a=r.TEXTURE_2D;return ht(r,()=>r.bindTexture(a,i)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),L().getNumber("WEBGL_VERSION")===1?ht(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):ht(r,()=>r.texStorage2D(a,1,n,t,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function Hw(r){return r.internalFormatFloat}function t1(r,t,e,n){let[o,s]=mp(t,e);return pg(r,o,s,Hw(n),n.textureFormatFloat,r.FLOAT)}function qw(r){return r.internalFormatHalfFloat}function e1(r,t,e,n){let[o,s]=mp(t,e);return pg(r,o,s,qw(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Kw(r){return r.downloadTextureFormat}function r1(r,t,e,n){let[o,s]=mp(t,e);return pg(r,o,s,Kw(n),r.RGBA,r.UNSIGNED_BYTE)}function jw(r){return r.internalFormatPackedFloat}function n1(r,t,e,n){let[o,s]=wa(t,e);return pg(r,o,s,jw(n),r.RGBA,r.FLOAT)}function Xw(r){return r.internalFormatPackedHalfFloat}function o1(r,t,e,n){let[o,s]=wa(t,e);return pg(r,o,s,Xw(n),r.RGBA,n.textureTypeHalfFloat)}function s1(r,t,e){return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),Ow(r,t,"clipSpacePos",e,3,20,0)&&Ow(r,t,"uv",e,2,20,12)}function i1(r,t,e,n,o,s){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t));let i,a,u;o instanceof Uint8Array?(i=new Uint8Array(e*n*4),a=r.UNSIGNED_BYTE,u=r.RGBA):(i=new Float32Array(e*n*4),a=r.FLOAT,u=s.internalFormatPackedFloat),i.set(o),L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e,n,r.RGBA,a,i)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,u,e,n,0,r.RGBA,a,i)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function a1(r,t,e){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t)),e.data instanceof Uint8Array?L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e.width,e.height,r.RGBA,r.UNSIGNED_BYTE,e.data)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,e.width,e.height,0,r.RGBA,r.UNSIGNED_BYTE,e.data)):L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,e)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function l1(r,t,e,n){let o=r.createBuffer();ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let a=4*4*t*e;return ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function u1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function c1(r,t,e,n){let[o,s]=mp(t,e),i=4,a=new Uint8Array(cL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function p1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(pL(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function m1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var hp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber("WEBGL_VERSION");if(t!=null?(this.gl=t,TT(e,t)):this.gl=jn(e),t=this.gl,L().getNumber("WEBGL_VERSION")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension("OES_vertex_array_object");if(s==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),L().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",i="OES_texture_half_float";if(this.textureFloatExtension=Sd(this.gl,s),Xn(this.gl,i))this.textureHalfFloatExtension=Sd(this.gl,i);else if(L().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Xn(this.gl,o))this.colorBufferHalfFloatExtension=Sd(this.gl,o);else if(L().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Xn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Xn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=JT(this.gl),this.indexBuffer=QT(this.gl),this.framebuffer=zT(this.gl),this.textureConfig=ag(this.gl,this.textureHalfFloatExtension)}get debug(){return L().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),t1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),e1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),r1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),a1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),i1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),o1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),n1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Pw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>c1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return p1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return u1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=l1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>m1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=ZT(e));let n=RT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),FT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&lg(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),s1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&lg(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?BT(this.gl,t,e):VT(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),GT(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&lg(this.gl,this.program),Nd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Sd(this.gl,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Knt(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),ug(this.gl,t,this.framebuffer),this.debug&&Nd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ug(this.gl,this.outputTexture,this.framebuffer),this.debug&&Nd(this.gl)):Pw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;ug(o,t,this.framebuffer),this.debug&&Nd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Knt(r){let t=0;for(;t<r.length&&r[t]();++t);return t-1}var{addImpl:NL,bincountImpl:Yw,bincountReduceImpl:kL,castImpl:TL,ceilImpl:_L,concatImpl:EL,equalImpl:AL,expImpl:DL,expm1Impl:$L,floorImpl:RL,gatherNdImpl:FL,gatherV2Impl:OL,greaterImpl:PL,greaterEqualImpl:ML,lessImpl:LL,lessEqualImpl:zL,linSpaceImpl:BL,logImpl:VL,maxImpl:GL,maximumImpl:WL,minimumImpl:UL,multiplyImpl:HL,negImpl:qL,notEqualImpl:KL,prodImpl:jL,raggedGatherImpl:XL,raggedRangeImpl:YL,raggedTensorToTensorImpl:ZL,rangeImpl:JL,rsqrtImpl:QL,scatterImpl:tz,sigmoidImpl:ez,simpleAbsImpl:Zw,sliceImpl:rz,sparseFillEmptyRowsImpl:nz,sparseReshapeImpl:oz,sparseSegmentReductionImpl:Jw,sqrtImpl:sz,staticRegexReplaceImpl:iz,stridedSliceImpl:az,stringNGramsImpl:lz,stringSplitImpl:uz,stringToHashBucketFastImpl:cz,subImpl:pz,tileImpl:mz,topKImpl:fz,transposeImpl:gp,uniqueImpl:dz}=Nw;function d1(r,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${r}.${e}`)}function er(r,t){return t===1?[r]:d1(r,t)}function hz(r,t){if(r===1)return"rc";let e="";for(let n=0;n<r;n++)e+=t[n],n<r-1&&(e+=",");return e}var Qw=class{constructor(t){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.enableShapeUniforms=de(this.outputShape.length),this.rank===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let e=er("rc",this.rank),n=zt(this.rank),o=this.getOutOfBoundsCondition(e),s=this.getSetup(e),i=this.getOutput(e);this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
|
|
if(${o}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}getSourceCoordsArr(t){let e=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let i=2;i<this.rank;i++)s=`${t[t.length-1-i]},`+s;e.push(s)}return e}getOutOfBoundsCondition(t){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n<this.rank;n++)e+=`${t[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(e+="||");return e}getSetup(t){if(this.rank===1)return"";let e=t.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],o=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
|
|
int r = ${e[0]};
|
|
int c = ${e[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${n};
|
|
bool rEdge = rp1 >= ${o};
|
|
`}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),
|
|
cEdge ? 0. : getA(${e[1]}),
|
|
rEdge ? 0. : getA(${e[2]}),
|
|
rEdge || cEdge ? 0. : getA(${e[3]})`}};var Od=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
|
|
${s}
|
|
${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${o}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${o>0?"}":""}
|
|
`}this.userCode=`
|
|
${jnt(e,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?Ed():_d(t)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":t[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":t[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function jnt(r,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?gL(["r","c","d"],"inputShape"):Ci(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var tI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=xz(e,n),s=yz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=gz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=xz(n,o),i=yz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=gz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Xnt(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function gz(r,t,e,n,o){let s=Ynt(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=mp(r[0],r[1]);i=u*l}let a=Xnt(e,s);return i*a}function Ynt(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return jw(t);case Lr.PACKED_2X2_FLOAT16:return Xw(t);case Lr.UNPACKED_FLOAT32:return Hw(t);case Lr.UNPACKED_FLOAT16:return qw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Kw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Znt(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function xz(r,t){if(r===Zr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Zr.RENDER||r==null)return Znt(t);if(r===Zr.DOWNLOAD||r===Zr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function yz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},yr="if (isnan(x)) return x;",bz="return x;",h1="return abs(x);";var wz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Iz=yr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Cz=yr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Ia="return x;",vz="return 1.0 / (1.0 + exp(-1.0 * x));";var Nz="return x;",kz=`
|
|
vec4 result;
|
|
|
|
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
|
|
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
|
|
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
|
|
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
|
|
|
|
return result;
|
|
`,Tz=`
|
|
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,_z=`
|
|
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,Ez="return 1.0 / (1.0 + exp(-1.0 * x));",Rn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}};var eI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er("rc",e),o=zt(e),s=hz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${s});
|
|
|
|
setOutput(getChannel(packedInput, ${a}));
|
|
}
|
|
`}};var Qnt=Kr.whereImpl,tot=1e-7,eot=1e-4,rI={};function rot(r){return r in rI||(rI[r]={}),rI[r]}var not=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),oot=600;function sot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*oot/1024/1024}var ju=class extends Wo{nextDataId(){return ju.nextDataId++}constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof hp)e=t;else{let n=jn(L().getNumber("WEBGL_VERSION"),t);e=new hp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=jn(L().getNumber("WEBGL_VERSION"));e=new hp(n),this.binaryCache=rot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new tI(this.gpgpu),this.numMBBeforeWarning=sot(),this.texData=new Ta(this,Vn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=kd(e),c=new cg(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Zr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Zr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Rn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Rn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...ig(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Vn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Rn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=Vn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return bt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return bt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e<t.length;e++){let n=t[e];if(!AT(n))throw L().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:n,isPacked:o}=this.texData.get(t),s=y.sizeFromShape(e);if(L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(t),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture,...ig(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let i=L().getBool("WEBGL_PACK")&&o===!0,a=i?kd(e):e,u=i?new Ww(a):new Gw(a),l=this.runWebGLProgram(u,[{shape:a,dtype:n,dataId:t}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=not){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<e)}getGPGPUContext(){return this.gpgpu}where(t){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let e=t.dataSync();return Qnt(t.shape,e)}packedUnaryOp(t,e,n){let o=new Rn(t.shape,e),s=this.compileAndRun(o,[t],n);return Vn().makeTensorFromTensorInfo(s)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let o=Zw(this.texData.get(t.dataId).values);return this.makeOutput(t.shape,t.dtype,o)}if(L().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,h1,t.dtype);let e=new zr(t.shape,h1),n=this.compileAndRun(e,[t]);return Vn().makeTensorFromTensorInfo(n)}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Vn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new eI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Qw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[zl(t.shape),...Bl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[zl(e),...Bl(e)],i=new Od(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=kd(s),u;o?u=new Vw(a):u=new Bw(a);let l=!0,c=[e!=null?e:ig(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===qu.DENSE){let x=i!=null?i:ig(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(x=>{if(x.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(x.dataId);if(b.texture==null){if(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Ku(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=SL(t,c,p),f=this.getAndSaveBinary(m,()=>CL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||vL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(ft(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?tot:eot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=WT(n,u),e.texShape=p),s!=null){let m=kd(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new Uw(m,g):f=new cg(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Zr.PIXELS:w.usage=Zr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],N=!0,E=this.runWebGLProgram(f,[b],o,I,N),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=iot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await kh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Fw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=YT(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Vn().backend;if(!u.gpgpu.gl.isTexture(o))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=u.writeTexture(o,e,n,s,i,a);return Vn().makeTensorFromDataId(l,e,n,u)}};ju.nextDataId=0;function iot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<e.length;++n)e[n]=Math.round(r[n]);return e}else throw new Error(`Unknown dtype ${t}`)}var Az="4.5.0";function Dz(){L().set("WEBGL_FORCE_F16_TEXTURES",!0)}xu.isBrowser()&&sm("webgl",()=>new ju,2);var aDe={forceHalfFloat:Dz};var Pd=`
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|
if (isnan(a)) return a;
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|
if (isnan(b)) return b;
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|
`;var mo=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=de(this.outputShape.length),this.userCode=`
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float binaryOperation(float a, float b) {
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|
${t}
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|
}
|
|
|
|
void main() {
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float a = getAAtOutCoords();
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float b = getBAtOutCoords();
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setOutput(binaryOperation(a, b));
|
|
}
|
|
`}};var Yn=`
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|
result.r = isNaN.r ? NAN : result.r;
|
|
result.g = isNaN.g ? NAN : result.g;
|
|
result.b = isNaN.b ? NAN : result.b;
|
|
result.a = isNaN.a ? NAN : result.a;
|
|
`;var zo=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=de(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`
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|
result.y = 0.;
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|
result.z = 0.;
|
|
result.w = 0.;
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|
`;else if(i=`
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|
${zt(s)} coords = getOutputCoords();
|
|
`,s===1)this.enableShapeUniforms?i+=`
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result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
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|
result.w = 0.;
|
|
`:i+=`
|
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result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
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|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let u=er("coords",s);this.enableShapeUniforms?i+=`
|
|
bool nextRowOutOfBounds =
|
|
(${u[s-2]} + 1) >= outShape[${s} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${u[s-1]} + 1) >= outShape[${s} - 1];
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|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:i+=`
|
|
bool nextRowOutOfBounds =
|
|
(${u[s-2]} + 1) >= ${this.outputShape[s-2]};
|
|
bool nextColOutOfBounds =
|
|
(${u[s-1]} + 1) >= ${this.outputShape[s-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${i}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function rr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $z={kernelName:xo,backendName:"webgl",kernelFunc:rr};function Fn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var Rz={kernelName:Op,backendName:"webgl",kernelFunc:Fn};var g1="return (a < 0.) ? b * a : a;",x1=`
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|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function aot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new zo(x1,o.shape,i.shape):new mo(g1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var Fz={kernelName:Is,backendName:"webgl",kernelFunc:aot};var y1="return (a < 0.) ? b * a : a;",b1=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function lot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new zo(b1,n.shape,o.shape):new mo(y1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var Oz={kernelName:Ps,backendName:"webgl",kernelFunc:lot};var Bo="if (isnan(x)) return x;";function wt({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Rn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ue({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new mo(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Fn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new zo(t,u.shape,l.shape,e):f=new mo(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Vl(r,t=!1){if(r==="linear")return t?Nz:bz;if(r==="relu")return t?Tz:Iz;if(r==="elu")return t?kz:wz;if(r==="relu6")return t?_z:Cz;if(r==="prelu")return t?b1:y1;if(r==="leakyrelu")return t?x1:g1;if(r==="sigmoid")return t?Ez:vz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Md=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=de(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${a}
|
|
}`:l?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${a}
|
|
}`:g=`vec4 activation(vec4 x) {
|
|
${a}
|
|
}`,x="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",I="rc.x";t[0]<e[0]?w=`imod(rc.x, ${t[0]})`:e[0]<t[0]&&(I=`imod(rc.x, ${e[0]})`),this.userCode=`
|
|
${g}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${p}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
int batchA = ${w};
|
|
int batchB = ${I};
|
|
for (int i = 0; i < ${p}; i++) {
|
|
vec4 a = getMatrixA(batchA, ${m});
|
|
vec4 b = getMatrixB(batchB, ${f});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${d[0]} * ${h[0]});
|
|
result += (${d[1]} * ${h[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${b}
|
|
|
|
${x}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};var w1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},mg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}};var Pz="return a * b;";function fg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new mg(w1.REAL,n.shape,o.shape),c=new mg(w1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Fn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=HL(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new zo(Pz,n.shape,o.shape):i=new mo(Pz,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var Mz={kernelName:$s,backendName:"webgl",kernelFunc:fg};function Lz(r,t,e){let n=[zl(r.shape),...Bl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[zl(t),...Bl(t)],i=new Od(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Ku(o.shape,u)&&!(c.texture!==null&&Ku(c.shape,u))?Lz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var zz={kernelName:Bi,backendName:"webgl",kernelFunc:rt};var dg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${a}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${a};
|
|
if (${u===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${u===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${u===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}};var nI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
|
|
if (${e==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${e==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${u}(values, minMaxValue);
|
|
if (${e==="min"} || ${e==="max"}) {
|
|
minMaxValue = ${u}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,f="vec4";e==="all"?(a="1.0",m=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,f="bvec4"):e==="any"&&(a="0.0",m=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,f="bvec4");let d="";s%n>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${a};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${a});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${m}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${p===1}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===2}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===3}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function cot(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Zn(r,t,e,n){let o=cot(r.shape),s=r;for(let i=0;i<o.length;i++){let{inSize:a,windowSize:u,outSize:l}=o[i],c,p;e==="mean"?c=i===0?new dg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},a):new dg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l}):c=new nI({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},e),p=s,s=n.runWebGLProgram(c,[s],t),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var oI=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[e[i]];this.outputShape=n,this.rank=n.length;let o=zt(this.rank),s=pot(e);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function pot(r){let t=r.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o<r.length;o++)n[r[o]]=e[o];return n.join()}var sI=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(t.length);for(let c=0;c<n.length;c++)n[c]=t[e[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=d1("rc",this.rank),i=new Array(this.rank);for(let c=0;c<e.length;c++)i[e[c]]=s[c];let a=`vec2(${i.slice(-2).join()})`,u=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${u}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${s[this.rank-1]};
|
|
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${u}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Xu(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sI(r.shape,t):new oI(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function Bz(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=Xu(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=mc(r.dtype),w=Zn(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function xp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return Bz(o,s,i,e)}var Vz={kernelName:Js,backendName:"webgl",kernelFunc:xp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c<u.length;c++)u[c]=o.shape[s[c]];let l;if(i.shouldExecuteOnCPU([o])){let p=i.texData.get(o.dataId).values,m=gp(p,o.shape,o.dtype,s,u);l=i.makeTensorInfo(u,o.dtype);let f=i.texData.get(l.dataId);f.values=m}else l=Xu(o,s,i);return l}var Gz={kernelName:io,backendName:"webgl",kernelFunc:Pe};var I1=1e3;function yp({a:r,b:t,transposeA:e,transposeB:n,backend:o,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:u=null}){let l=r.shape.length,c=t.shape.length,p=e?r.shape[l-2]:r.shape[l-1],m=n?t.shape[c-1]:t.shape[c-2],f=e?r.shape[l-1]:r.shape[l-2],d=n?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),I=Ur.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],P=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Vl(u,!0):null,K=G||W||q||H!=null,X;if((f===1||d===1)&&V>I1&&K===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,at=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[P,V,1]}}),F.push(ot));let it=d===1?2:1,mt=nt;at&&(mt=rt({inputs:{x:nt},backend:o,attrs:{shape:[P,1,V]}}),F.push(mt));let gt=fg({inputs:{a:ot,b:mt},backend:o});X=xp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Md(N,E,[P,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let at=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(at),F.push(at)}X=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:X},backend:o,attrs:{shape:I}});F.push(X);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function mot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return yp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var Wz={kernelName:Ki,backendName:"webgl",kernelFunc:mot};var Uz="return abs(x);";function fot(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Zw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Rn(n.shape,Uz):o=new zr(n.shape,Uz),e.runWebGLProgram(o,[n],n.dtype)}var Hz={kernelName:_i,backendName:"webgl",kernelFunc:fot};var dot=yr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,hot=wt({opSnippet:dot}),qz={kernelName:Ho,backendName:"webgl",kernelFunc:hot};var got=yr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,xot=wt({opSnippet:got}),Kz={kernelName:qo,backendName:"webgl",kernelFunc:xot};var jz="return a + b;",yot=ue({opSnippet:jz,packedOpSnippet:jz,supportsComplex:!0,cpuKernelImpl:NL}),Xz={kernelName:oo,backendName:"webgl",kernelFunc:yot};var iI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${o};
|
|
setOutput(result);
|
|
}
|
|
`}};var aI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${o};
|
|
setOutput(result);
|
|
}
|
|
`}};function lI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return rr({inputs:{x:n[0]},backend:e});if(n.length>L().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=lI({inputs:n.slice(0,u),backend:e}),c=lI({inputs:n.slice(u),backend:e});return lI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new aI(n[0].shape,s):new iI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var Yz={kernelName:Ko,backendName:"webgl",kernelFunc:lI};function bot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Zz={kernelName:Ea,backendName:"webgl",kernelFunc:bot};function wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var Jz={kernelName:Aa,backendName:"webgl",kernelFunc:wot};var uI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${o};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${o}; i++) {
|
|
int inIdx = ${u};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${a} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}};var cI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=er("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`
|
|
${D} sourceLocR = ${D}(${c.join()}, 0);
|
|
++${c[u-1]};
|
|
${D} sourceLocG = ${D}(${c.join()}, 0);
|
|
++${c[u-2]};
|
|
${D} sourceLocA = ${D}(${c.join()}, 0);
|
|
--${c[u-1]};
|
|
${D} sourceLocB = ${D}(${c.join()}, 0);
|
|
--${c[u-2]};`}else m=u,p=`
|
|
${l} sourceLocR = coords;
|
|
++${c[u-1]};
|
|
${l} sourceLocG = coords;
|
|
++${c[u-2]};
|
|
${l} sourceLocA = coords;
|
|
--${c[u-1]};
|
|
${l} sourceLocB = coords;
|
|
--${c[u-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(D=>"int "+D),g=er("sourceLocR",m-1).concat("inIdx.r"),x=er("sourceLocG",m-1).concat("inIdx.g"),b=er("sourceLocB",m-1).concat("inIdx.b"),w=er("sourceLocA",m-1).concat("inIdx.a"),I=n==="max"?"greaterThan":"lessThan",N=o?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${x.join()}),
|
|
getBestIndicesAChannel(${b.join()}),
|
|
getBestIndicesAChannel(${w.join()})));`,E=`vec4(
|
|
getAChannel(${g.join()}),
|
|
hasNextCol ? getAChannel(${x.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${b.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?"":`
|
|
float getBestIndicesAChannel(${h.join()}) {
|
|
return getChannel(getBestIndicesA(${f.join()}),
|
|
vec2(${f.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${h.join()}) {
|
|
return getChannel(getA(${f.join()}),
|
|
vec2(${f.slice(-2).join()}));
|
|
}
|
|
${A}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};
|
|
bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};
|
|
${p}
|
|
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
|
|
sourceLocB${d}, sourceLocA${d}) * ${e};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${E};
|
|
|
|
for (int i = 0; i < ${e}; i++) {
|
|
inIdx = srcIdx;
|
|
${N}
|
|
vec4 candidate = ${E};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${I}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function Qz(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new uI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=Qz(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function t3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new cI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=t3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function pI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=Qz(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return t3(r,t,n)}function Iot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=pI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var e3={kernelName:Ei,backendName:"webgl",kernelFunc:Iot};function Cot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=pI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var r3={kernelName:Ai,backendName:"webgl",kernelFunc:Cot};var vot=yr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,Sot=wt({opSnippet:vot}),n3={kernelName:jo,backendName:"webgl",kernelFunc:Sot};var Not=yr+"return log(x + sqrt(x * x + 1.0));",kot=wt({opSnippet:Not}),o3={kernelName:Xo,backendName:"webgl",kernelFunc:kot};var Tot=yr+`
|
|
return atan(x);
|
|
`,_ot=wt({opSnippet:Tot}),s3={kernelName:Yo,backendName:"webgl",kernelFunc:_ot};var Eot=Pd+`
|
|
return atan(a, b);
|
|
`,Aot=`
|
|
vec4 result = atan(a, b);
|
|
bvec4 isNaNA = isnan(a);
|
|
bvec4 isNaNB = isnan(b);
|
|
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
|
|
`+Yn+`
|
|
return result;
|
|
`,Dot=ue({opSnippet:Eot,packedOpSnippet:Aot}),i3={kernelName:Jo,backendName:"webgl",kernelFunc:Dot};var $ot=yr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Rot=wt({opSnippet:$ot}),a3={kernelName:Zo,backendName:"webgl",kernelFunc:Rot};var vi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${a}, ${u});
|
|
const ivec2 pads = ivec2(${f}, ${d});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m};
|
|
wC += ${c}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${D} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let w="max",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(I="avgValue / max(count, 1.0)");let N=Math.floor(i/4)*4,E=i%4,A=`
|
|
if (${h}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${w}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${a}, ${u});
|
|
const ivec2 pads = ivec2(${f}, ${d});
|
|
const float initializationValue = ${b};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${b});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${N}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${A}
|
|
}
|
|
|
|
int xC = xCCorner + ${N};
|
|
if (${E===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${A}
|
|
} else if (${E===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${A}
|
|
} else if (${E===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${A}
|
|
}
|
|
}
|
|
setOutput(${I});
|
|
}
|
|
`}},Yu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",I="0.0";if(w||(I="-1.0 / 1e-20"),n){let P=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${a}, ${u}, ${l});
|
|
const ivec3 pads = ivec3(${g}, ${x}, ${b});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${f};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${t.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h};
|
|
wC += ${m}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xD, xR, xC, ch);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${P} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +
|
|
wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let N="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / max(count, 1.0)");let A=Math.floor(i/4)*4,D=i%4,F=`
|
|
if (${w}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${N}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${a}, ${u}, ${l});
|
|
const ivec3 pads = ivec3(${g}, ${x}, ${b});
|
|
const float initializationValue = ${I};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${I});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${f};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${t.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${A}; wC += 4) {
|
|
int xC = xCCorner + wC * ${m};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
|
|
);
|
|
|
|
${F}
|
|
}
|
|
|
|
int xC = xCCorner + ${A};
|
|
if (${D===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${F}
|
|
} else if (${D===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${F}
|
|
} else if (${D===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${F}
|
|
}
|
|
}
|
|
}
|
|
setOutput(${E});
|
|
}
|
|
`}};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ii(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new vi(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var l3={kernelName:Qo,backendName:"webgl",kernelFunc:Fot};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Yu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var u3={kernelName:Di,backendName:"webgl",kernelFunc:Oot};var mI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${p});
|
|
const float avgMultiplier = float(${m});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${i}) {
|
|
float dyR = float(dyRCorner + wR) / ${o}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC+= ${a}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},fI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`
|
|
const ivec3 pads = ivec3(${d}, ${h}, ${g});
|
|
const float avgMultiplier = float(${x});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${u}) {
|
|
float dyD = float(dyDCorner + wD) / ${s}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${m};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${i}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${c}) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Pot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new fI(m);return e.runWebGLProgram(f,[o],i.dtype)}var c3={kernelName:Xl,backendName:"webgl",kernelFunc:Pot};function Mot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;Ii([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new mI(c);return e.runWebGLProgram(p,[o],i.dtype)}var p3={kernelName:Fp,backendName:"webgl",kernelFunc:Mot};function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return yp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var m3={kernelName:ts,backendName:"webgl",kernelFunc:Lot};var dI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${a};
|
|
float scale = ${u};
|
|
float inv = scale * inversesqrt(variance + float(${i}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}};var hI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${a};
|
|
vec4 scale = ${u};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${i}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}};var zot=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new hI(n.shape,o.shape,s.shape,c,p,u):new dI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},f3={kernelName:gs,backendName:"webgl",kernelFunc:zot};var gI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Bot(this.rank),o,s=t.map((i,a)=>`sourceLoc.${C1[a]} = start[${a}] + coords.${C1[a]};`);o=`
|
|
${e} sourceLoc;
|
|
${e} coords = getOutputCoords();
|
|
${s.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${o}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},C1=["x","y","z","w","u","v"];function Bot(r){if(r===1)return"sourceLoc";if(r<=6)return C1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var xI=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`
|
|
result.x = ${i};
|
|
if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
|
|
++${o[this.rank-1]};
|
|
result.y = ${i};
|
|
--${o[this.rank-1]};
|
|
}
|
|
`,u=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${t[this.rank-2]}) {
|
|
++${o[this.rank-2]};
|
|
result.z = ${i};
|
|
if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
|
|
++${o[this.rank-1]};
|
|
result.w = ${i};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${e} coords = getOutputCoords();
|
|
${e} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${a}
|
|
${u}
|
|
setOutput(result);
|
|
}
|
|
`}};function Vot(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=ze.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function Si(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=ze.parseSliceParams(o,s,i);if(ze.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=rz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=ze.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xI(u):new gI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Vot(o,a,u,e)}var d3={kernelName:Gi,backendName:"webgl",kernelFunc:Si};var Got=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=Si({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},h3={kernelName:$i,backendName:"webgl",kernelFunc:Got};function Wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Yw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var g3={kernelName:Da,backendName:"webgl",kernelFunc:Wot};function Uot(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var x3={kernelName:Yl,backendName:"webgl",kernelFunc:Uot};var Hot="return float(a != b);",v1=ue({opSnippet:Hot,cpuKernelImpl:KL,dtype:"bool"}),y3={kernelName:Ja,backendName:"webgl",kernelFunc:v1};function Gl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.real},backend:e})}var b3={kernelName:jp,backendName:"webgl",kernelFunc:Gl};var qot="return float(int(x));";function w3(r,t){let e=new zr(r.shape,qot),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function S1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return rr({inputs:{x:o},backend:e});let i=Te(o.shape),a=S1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Fn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Gl({inputs:{input:o},backend:e}),a=S1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=rr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=TL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return w3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=v1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var I3={kernelName:ho,backendName:"webgl",kernelFunc:S1};var C3="return ceil(x);",Kot=wt({opSnippet:C3,packedOpSnippet:C3,cpuKernelImpl:_L}),v3={kernelName:es,backendName:"webgl",kernelFunc:Kot};var yI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}};var bI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new bI(o.shape):a=new yI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var S3={kernelName:go,backendName:"webgl",kernelFunc:jot};var wI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
float re = abs(getRealAtOutCoords());
|
|
float im = abs(getImagAtOutCoords());
|
|
float mx = max(re, im);
|
|
|
|
// sadly the length function in glsl is not underflow-safe
|
|
// (at least not on Intel GPUs). So the safe solution is
|
|
// to ensure underflow-safety in all cases.
|
|
setOutput(
|
|
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
|
|
);
|
|
}
|
|
`}};function N3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Xot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new wI(n.shape),i=[N3(n,o.complexTensorInfos.real),N3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var k3={kernelName:Zl,backendName:"webgl",kernelFunc:Xot};var II=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i<e.length;i++)e[i]=e[i-1]+t[i][1];let n=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<e.length;i++){let a=e[i-1];n.push(`else if (yC < ${e[i]}) setOutput(getT${i}(yR, yC-${a}));`)}let o=e.length,s=e[e.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}};var vI=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=zt(o),i=er("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h<u.length;h++)u[h]=u[h-1]+t[h][e];let l=a[e],c=a.slice(-2),p=a.join(),m=`if (${l} < ${u[0]}) {
|
|
return getChannel(
|
|
getT0(${p}), vec2(${c.join()}));
|
|
}`;for(let h=1;h<u.length;h++){let g=u[h-1];m+=`
|
|
if (${l} < ${u[h]} && ${l} >= ${u[h-1]}) {
|
|
return getChannel(
|
|
getT${h}(${CI(a,l,g)}),
|
|
vec2(${CI(c,l,g)}));
|
|
}`}let f=u.length,d=u[u.length-1];m+=`
|
|
return getChannel(
|
|
getT${f}(${CI(a,l,d)}),
|
|
vec2(${CI(c,l,d)}));`,this.userCode=`
|
|
float getValue(${a.map(h=>"int "+h)}) {
|
|
${m}
|
|
}
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${i}), 0., 0., 0.);
|
|
|
|
${i[o-1]} = ${i[o-1]} + 1;
|
|
if (${i[o-1]} < ${n[o-1]}) {
|
|
result.g = getValue(${i});
|
|
}
|
|
|
|
${i[o-2]} = ${i[o-2]} + 1;
|
|
if (${i[o-2]} < ${n[o-2]}) {
|
|
result.a = getValue(${i});
|
|
}
|
|
|
|
${i[o-1]} = ${i[o-1]} - 1;
|
|
if (${i[o-2]} < ${n[o-2]} &&
|
|
${i[o-1]} < ${n[o-1]}) {
|
|
result.b = getValue(${i});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function CI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function bp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var T3={kernelName:Gp,backendName:"webgl",kernelFunc:bp};function Ld(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Gl({inputs:{input:b},backend:e})),d=r.map(b=>bp({inputs:{input:b},backend:e})),h=Ld(f,t,e),g=Ld(d,t,e),x=Fn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=EL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Rn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;h<s.length;h+=a){let g=s.slice(h,h+a);f.push(Ld(g,t,e))}let d=Ld(f,t,e);for(let h of f)e.disposeIntermediateTensorInfo(h);return d}if(i){let f=new vI(s.map(d=>d.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Yot(s,t,e),c=new II(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Yot(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function N1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?rr({inputs:{x:u[0]},backend:e}):Ld(u,s,e)}var _3={kernelName:Ri,backendName:"webgl",kernelFunc:N1};var zd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?I=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:I=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,N="result = activation(result);");let E=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${I}
|
|
|
|
const ivec2 strides = ivec2(${u}, ${l});
|
|
const ivec2 pads = ivec2(${i}, ${a});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${w}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${x}], coords[${b}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${m}; wR++) {
|
|
int xR = xRCorner + wR * ${c};
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${d}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${h===1}) {
|
|
|
|
if (${g}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${d}) *
|
|
getW(wR, wC, ${d}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${d}, xR, xC) *
|
|
getW(wR, wC, ${d}, d2);
|
|
}
|
|
|
|
} else if (${h===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${d}, d2),
|
|
getW(wR, wC, ${d} + 1, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${d}),
|
|
getX(batch, xR, xC, ${d} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${d}, xR, xC),
|
|
getX(batch, ${d} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${h===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${d}, d2),
|
|
getW(wR, wC, ${d} + 1, d2),
|
|
getW(wR, wC, ${d} + 2, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${d}),
|
|
getX(batch, xR, xC, ${d} + 1),
|
|
getX(batch, xR, xC, ${d} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${d}, xR, xC),
|
|
getX(batch, ${d} + 1, xR, xC),
|
|
getX(batch, ${d} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${E}
|
|
${N}
|
|
setOutput(result);
|
|
}
|
|
`}},SI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${s}, ${i}, ${a});
|
|
const ivec3 pads = ivec3(${e}, ${n}, ${o});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d2 = coords.u;
|
|
|
|
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xFCorner = xFRCCorner.x;
|
|
int xRCorner = xFRCCorner.y;
|
|
int xCCorner = xFRCCorner.z;
|
|
|
|
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
|
|
// y(yF, yR, yC, d2). ? = to be determined. : = across all
|
|
// values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${p}; wF++) {
|
|
int xF = xFCorner + wF * ${u};
|
|
|
|
if (xF < 0 || xF >= ${t.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${m}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${d}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${h===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${d}) *
|
|
getW(wF, wR, wC, ${d}, d2);
|
|
} else if (${h===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${d}),
|
|
getX(batch, xF, xR, xC, ${d} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${d}, d2),
|
|
getW(wF, wR, wC, ${d} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${h===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${d}),
|
|
getX(batch, xF, xR, xC, ${d} + 1),
|
|
getX(batch, xF, xR, xC, ${d} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${d}, d2),
|
|
getW(wF, wR, wC, ${d} + 1, d2),
|
|
getW(wF, wR, wC, ${d} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};var Bd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)m+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;m+=`
|
|
for (int r = 0; r < ${l}; r++) {
|
|
for (int d1 = 0; d1 < ${t.inChannels}; d1 += 2) {
|
|
`;for(let g=0;g<c;g++)m+=`
|
|
xTexelC${g*2} = vec4(0.0);
|
|
xTexelC${g*2}Ready = 0;
|
|
xTexelC${g*2+1} = vec4(0.0);
|
|
xTexelC${g*2+1}Ready = 0;
|
|
xC${g} = vec4(0.0);`;m+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`
|
|
xC = xCCorner + ${x*u};
|
|
`,a===1){if(x<c&&(i%2===1?(m+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
|
|
xTexelC${x} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${x}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x}Ready = 1;
|
|
}
|
|
`,u===1&&x>0?m+=`
|
|
xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);
|
|
`:m+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${x} = vec4(previous.zw, xTexelC${x}.xy);
|
|
} else {
|
|
xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);
|
|
}
|
|
`):m+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
|
|
xTexelC${x} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${x}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x}Ready = 1;
|
|
}
|
|
|
|
xC${x} = xTexelC${x};
|
|
`,x+1<c)){let b=i%2===0?y.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(m+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${b};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
|
|
xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${x+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x+1}Ready = 1;
|
|
}
|
|
`,u>1?m+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);
|
|
} else {
|
|
xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);
|
|
}
|
|
`:m+=`
|
|
xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);
|
|
`):b===1?m+=`
|
|
xC${x+1} = xTexelC${x};
|
|
`:m+=`
|
|
xCOffset = xC + ${b};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
|
|
xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${x+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x+1}Ready = 1;
|
|
}
|
|
|
|
xC${x+1} = xTexelC${x+1};
|
|
`}}else x<c&&(i%2===1?(m+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
|
|
xTexelC${x} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${x}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {
|
|
xTexelC${x+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${x+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x+1}Ready = 1;
|
|
}
|
|
|
|
xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
|
|
`,x+1<c&&(m+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);
|
|
`)):(m+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
|
|
xTexelC${x} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${x}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${x}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {
|
|
xTexelC${x+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${x+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${x+1}Ready = 1;
|
|
}
|
|
|
|
xC${x} = vec4(
|
|
xTexelC${x}.xy, xTexelC${x+1}.xy);
|
|
`,x+1<c&&(m+=`
|
|
xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
|
|
`)));x<c&&(m+=`
|
|
wTexel = getW(r, ${x}, d1, d2);
|
|
dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);
|
|
if(d1 + 1 < ${t.inChannels}) {
|
|
dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);
|
|
}
|
|
`,x+1<c&&(m+=`
|
|
wTexel = getW(r, ${x+1}, d1, d2);
|
|
dotProd += xC${x+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
|
|
if(d1 + 1 < ${t.inChannels}) {
|
|
dotProd += xC${x+1}.yyww * vec4(wTexel.zw, wTexel.zw);
|
|
}
|
|
`))}m+=`
|
|
}
|
|
`,m+=`
|
|
}
|
|
`,m+=`
|
|
}
|
|
`;let f="",d="";n&&(o?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:f=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,d="result = activation(result);");let h=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${f}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${m}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${h}
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}};var NI=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let{dataFormat:n}=e,o=We(),s=n==="channelsLast",i=s?1:2,a=s?2:3,u=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${t[2]} && pos < ${t[1]}) {`,l="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)l+=`
|
|
blockIndex = rc.z + ${p};
|
|
pos = rc.y + ${c};
|
|
|
|
${u}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${i}] && d0 >= 0) {
|
|
// Use custom imod instead mod. On Intel GPU, mod may generate
|
|
// unexpected value.
|
|
// https://github.com/tensorflow/tfjs/issues/5447
|
|
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
|
|
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
|
|
inChannels);
|
|
|
|
if(d1 < inputShape[${a}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${s}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${c*2+p}] = getChannel(
|
|
getA(rc.x, d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${c*2+p}] = getChannel(
|
|
getA(rc.x, ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${l}
|
|
|
|
${o.output} = result;
|
|
}
|
|
`}};function kI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function TI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=kI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=kI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>I1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Ku(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=yp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=rr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=yp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function _I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=kI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=kI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new NI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let P=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Vl(a,!0):null,q=new Md(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,P,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let K=n.runWebGLProgram(q,H,"float32"),X=rt({inputs:{x:K},backend:n,attrs:{shape:e.outShape}});I.push(K);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return X}function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=TI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new Bd(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=_I({x:o,filter:s,convInfo:m,backend:e});else{let h=new zd(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var E3={kernelName:rs,backendName:"webgl",kernelFunc:Zot};var EI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int d2 = coords.w;
|
|
|
|
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${t.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${t.outHeight}; yR++) {
|
|
int xR = wR + yR * ${e} - ${o};
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${t.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${s};
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
${i?`float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);`}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},AI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${u});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${p}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${e}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${o}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${e} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
|
|
|
|
if (${i}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},DI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${t.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${t.outDepth}; yF++) {
|
|
int xF = wF + yF * ${e} - ${s};
|
|
|
|
if (xF < 0 || xF >= ${t.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${t.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${i};
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${t.outWidth}; yC++) {
|
|
int xC = wC + yC * ${o} - ${a};
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},$I=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${l}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${e}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${s}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${e} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${i}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${o}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${o} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new EI(m);return e.runWebGLProgram(f,[o,s],"float32")}var A3={kernelName:Pp,backendName:"webgl",kernelFunc:Jot};var RI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=de(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${s});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
|
|
ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
vec4 result = vec4(0.);
|
|
for (int wR = 0; wR < ${e}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / strides[0];
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
int wRPerm = ${e} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
float dyC = float(dyCCorner + wC) / strides[1];
|
|
bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)
|
|
&& (fract(dyC) == 0.0);
|
|
int idyC = int(dyC);
|
|
|
|
float dyC2 = float(dyCCorner + wC + 1) / strides[1];
|
|
bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)
|
|
&& (fract(dyC2) == 0.0);
|
|
int idyC2 = int(dyC2);
|
|
|
|
if (idyCVal && idyCVal2) {
|
|
for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
|
|
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
vec4 dySample = getDy(batch, idyR, idyC, d2);
|
|
vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
|
|
dySample : getDy(batch, idyR, idyC2, d2);
|
|
|
|
vec2 dyValue = mod(float(idyC), 2.) == 0. ?
|
|
dySample.xy : dySample.zw;
|
|
result.xy += vec2(dot(dyValue, wValue.xy),
|
|
dot(dyValue, wValue.zw));
|
|
|
|
dyValue = mod(float(idyC2), 2.) == 0. ?
|
|
dySample2.xy : dySample2.zw;
|
|
result.zw += vec2(dot(dyValue, wValue.xy),
|
|
dot(dyValue, wValue.zw));
|
|
}
|
|
} else if (idyCVal) {
|
|
for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
|
|
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
vec4 dySample = getDy(batch, idyR, idyC, d2);
|
|
vec2 dyValue = mod(float(idyC), 2.) == 0. ?
|
|
dySample.xy : dySample.zw;
|
|
result.xy += vec2(dot(dyValue, wValue.xy),
|
|
dot(dyValue, wValue.zw));
|
|
}
|
|
} else if (idyCVal2) {
|
|
for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
|
|
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
vec4 dySample = getDy(batch, idyR, idyC2, d2);
|
|
vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
|
|
dySample.xy : dySample.zw;
|
|
result.zw += vec2(dot(dyValue, wValue.xy),
|
|
dot(dyValue, wValue.zw));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Qot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new RI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new AI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var D3={kernelName:ns,backendName:"webgl",kernelFunc:Qot};function tst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new SI(l);return e.runWebGLProgram(c,[o,s],"float32")}var $3={kernelName:os,backendName:"webgl",kernelFunc:tst};function est(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new DI(l);return e.runWebGLProgram(c,[o,s],"float32")}var R3={kernelName:$a,backendName:"webgl",kernelFunc:est};function rst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new $I(l);return e.runWebGLProgram(c,[o,s],"float32")}var F3={kernelName:Ra,backendName:"webgl",kernelFunc:rst};var nst=Bo+`
|
|
return cos(x);
|
|
`,ost=`
|
|
vec4 result = cos(x);
|
|
bvec4 isNaN = isnan(x);
|
|
${Yn}
|
|
return result;
|
|
`,sst=wt({opSnippet:nst,packedOpSnippet:ost}),O3={kernelName:ss,backendName:"webgl",kernelFunc:sst};var ist=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,ast=wt({opSnippet:ist}),P3={kernelName:is,backendName:"webgl",kernelFunc:ast};var FI=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
|
|
const float height_ratio = float(${g});
|
|
const float width_ratio = float(${w});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${i}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${x};
|
|
float width_scale = ${I};
|
|
|
|
float in_y = ${b};
|
|
if( in_y < 0.0 || in_y > ${d} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
float in_x = ${N};
|
|
if( in_x < 0.0 || in_x > ${h} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${f} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}};var lst=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new FI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},M3={kernelName:Oa,backendName:"webgl",kernelFunc:lst};var wp;(function(r){r.Prod="*",r.Sum="+"})(wp||(wp={}));var hg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===wp.Prod?"1.0":"0.0",a=n?i:`getX(${L3(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${zt(s)} coords = getOutputCoords();
|
|
int end = ${z3(s,"coords",this.op)};
|
|
float val = ${a};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${l}) {
|
|
int idx = ${c};
|
|
${z3(s,"coords",this.op)} = idx;
|
|
val ${this.op}= getX(${L3(s,"coords",this.op)});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function L3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function z3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function OI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new hg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new hg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function ust(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(wp.Prod,o,e,s,i,a)}var B3={kernelName:Fa,backendName:"webgl",kernelFunc:ust};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return OI(wp.Sum,o,e,s,i,a)}var V3={kernelName:as,backendName:"webgl",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Yw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=kL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var G3={kernelName:Jl,backendName:"webgl",kernelFunc:pst};var PI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int h = ${this.getHeightCoordString()};
|
|
int w = ${this.getWidthCoordString()};
|
|
int d = ${this.getDepthCoordString()};
|
|
|
|
int in_h = h / ${e};
|
|
int offset_h = imod(h, ${e});
|
|
int in_w = w / ${e};
|
|
int offset_w = imod(w, ${e});
|
|
int offset_d = (offset_h * ${e} + offset_w) *
|
|
${this.getOutputDepthSize()};
|
|
int in_d = d + offset_d;
|
|
|
|
float result = ${this.getInputSamplingString()};
|
|
setOutput(result);
|
|
}
|
|
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new PI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var W3={kernelName:Pa,backendName:"webgl",kernelFunc:mst};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,c="result = activation(result);");let p=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${l}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${u};
|
|
int q = d2 - d1 * ${u};
|
|
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
|
|
for (int wR = 0; wR < ${i}; wR++) {
|
|
int xR = xRCorner + wR * dilations[0];
|
|
|
|
if (xR < 0 || xR >= inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
int xC = xCCorner + wC * dilations[1];
|
|
|
|
if (xC < 0 || xC >= inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${p}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}};var Gd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<p;x++)f+=`
|
|
vec4 xTexelC${x*2};
|
|
int xTexelC${x*2}Ready;
|
|
vec4 xTexelC${x*2+1};
|
|
int xTexelC${x*2+1}Ready;
|
|
vec4 xC${x};`;f+=`
|
|
for (int r = 0; r < ${c}; r++) {
|
|
`;for(let x=0;x<p;x++)f+=`
|
|
xTexelC${x*2} = vec4(0.0);
|
|
xTexelC${x*2}Ready = 0;
|
|
xTexelC${x*2+1} = vec4(0.0);
|
|
xTexelC${x*2+1}Ready = 0;
|
|
xC${x} = vec4(0.0);`;f+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`
|
|
xC = xCCorner + ${b*l};
|
|
`,u===1){if(b<p&&(a%2===1?(f+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
|
|
xTexelC${b} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${b}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b}Ready = 1;
|
|
}
|
|
`,l===1&&b>0?f+=`
|
|
xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
|
|
`:f+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${b} = vec4(previous.zw, xTexelC${b}.xy);
|
|
} else {
|
|
xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
|
|
}
|
|
`):f+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
|
|
xTexelC${b} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${b}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b}Ready = 1;
|
|
}
|
|
|
|
xC${b} = xTexelC${b};
|
|
`,b+1<p)){let w=a%2===0?y.nearestLargerEven(l):l;l%2===0&&a%2===1||l%2!==0&&a%2!==1?(f+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${w};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
|
|
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${b+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b+1}Ready = 1;
|
|
}
|
|
`,l>1?f+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);
|
|
} else {
|
|
xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);
|
|
}
|
|
`:f+=`
|
|
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
|
|
`):w===1?f+=`
|
|
xC${b+1} = xTexelC${b};
|
|
`:f+=`
|
|
xCOffset = xC + ${w};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
|
|
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${b+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b+1}Ready = 1;
|
|
}
|
|
|
|
xC${b+1} = xTexelC${b+1};
|
|
`}}else b<p&&(a%2===1?(f+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
|
|
xTexelC${b} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${b}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
|
|
xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${b+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b+1}Ready = 1;
|
|
}
|
|
|
|
xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
|
|
`,b+1<p&&(f+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
|
|
`)):(f+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
|
|
xTexelC${b} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${b}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${b}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
|
|
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${b+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${b+1}Ready = 1;
|
|
}
|
|
|
|
xC${b} = vec4(
|
|
xTexelC${b}.xy, xTexelC${b+1}.xy);
|
|
`,b+1<p&&(f+=`
|
|
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
|
|
`)));b<p&&(f+=`
|
|
wTexel = getW(r, ${b}, d1, q);
|
|
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
|
|
`,b+1<p&&(f+=`
|
|
wTexel = getW(r, ${b+1}, d1, q);
|
|
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}f+=`
|
|
}
|
|
`,f+=`
|
|
}
|
|
`;let d="",h="";n&&(o?d=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?d=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:d=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,h="result = activation(result);");let g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${d}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${i};
|
|
int q = d2 - d1 * ${i};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${f}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${g}
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Gd(p):m=new Vd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var U3={kernelName:ls,backendName:"webgl",kernelFunc:fst};var MI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int dm = coords.w;
|
|
int d2 = d1 * ${i} + dm;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
// TO DO: Vec4 over the batch size
|
|
for (int b = 0; b < ${t.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${t.outHeight}; yR++) {
|
|
int xR = wR + yR * ${e} - ${o};
|
|
|
|
if (xR < 0 || xR >= ${t.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${t.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${s};
|
|
|
|
if (xC < 0 || xC >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},LI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${a});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
ivec2 dyCorner = coords.yz - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int wR = 0; wR < ${e}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${o}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${e} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${u}; dm++) {
|
|
int d2 = d1 * ${u} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function dst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new MI(p);return e.runWebGLProgram(m,[o,s],"float32")}var H3={kernelName:Mp,backendName:"webgl",kernelFunc:dst};function hst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new LI(p);return e.runWebGLProgram(m,[o,s],"float32")}var q3={kernelName:Lp,backendName:"webgl",kernelFunc:hst};var zI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function gst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new zI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var K3={kernelName:Ql,backendName:"webgl",kernelFunc:gst};var BI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`
|
|
const ivec2 strides = ivec2(${s}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${m});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${a}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${e}) {
|
|
for (int w = 0; w < ${u}; w++) {
|
|
int wIn = wBeg + w * ${c};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new BI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var j3={kernelName:us,backendName:"webgl",kernelFunc:xst};function yst(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(x)?w=s[g]:(w=Pe({inputs:{x:s[g]},backend:e,attrs:{perm:x}}),d.push(w));let I=w.shape.slice();for(let N=0;N<b.length;++N)I.splice(b[N],0,1);y.arraysEqual(w.shape,I)||(w=rt({inputs:{x:w},backend:e,attrs:{shape:I}}),d.push(w)),m===null?m=w:(m=fg({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=xp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var X3={kernelName:zp,backendName:"webgl",kernelFunc:yst};var bst="return (x >= 0.0) ? x : (exp(x) - 1.0);",wst=`
|
|
vec4 result;
|
|
|
|
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
|
|
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
|
|
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
|
|
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
|
|
|
|
return result;
|
|
`,Ist=wt({opSnippet:bst,packedOpSnippet:wst}),Y3={kernelName:ps,backendName:"webgl",kernelFunc:Ist};var Cst="return (b >= 0.0) ? a : a * (b + 1.0);",vst=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,Sst=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new zo(vst,n.shape,o.shape):new mo(Cst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},Z3={kernelName:Ma,backendName:"webgl",kernelFunc:Sst};var Nst=`
|
|
return vec4(equal(a, b));
|
|
`,kst="return float(a == b);",Tst=ue({opSnippet:kst,packedOpSnippet:Nst,dtype:"bool",cpuKernelImpl:AL}),J3={kernelName:za,backendName:"webgl",kernelFunc:Tst};var _st=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${S.ERF_P};
|
|
float a1 = ${S.ERF_A1};
|
|
float a2 = ${S.ERF_A2};
|
|
float a3 = ${S.ERF_A3};
|
|
float a4 = ${S.ERF_A4};
|
|
float a5 = ${S.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,Est=wt({opSnippet:_st}),Q3={kernelName:La,backendName:"webgl",kernelFunc:Est};var Ast=Bo+`
|
|
return exp(x);
|
|
`,Dst=`
|
|
vec4 result = exp(x);
|
|
bvec4 isNaN = isnan(x);
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,k1=wt({opSnippet:Ast,packedOpSnippet:Dst,cpuKernelImpl:DL,dtype:"float32"}),tB={kernelName:ms,backendName:"webgl",kernelFunc:k1};function VI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var eB={kernelName:Fi,backendName:"webgl",kernelFunc:VI};var rB="return exp(x) - 1.0;",$st=wt({opSnippet:rB,packedOpSnippet:rB,cpuKernelImpl:$L}),nB={kernelName:fs,backendName:"webgl",kernelFunc:$st};var gg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
|
|
const float exponentMultiplier = ${s};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${a}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${o});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${o}; i++) {
|
|
// x = (-2|2 * PI / N) * index * i;
|
|
float x = exponentMultiplierTimesIndexRatio * float(i);
|
|
float expR = cos(x);
|
|
float expI = sin(x);
|
|
float real = getReal(batch, i);
|
|
float imag = getImag(batch, i);
|
|
|
|
result +=
|
|
unaryOpComplex(real, expR, imag, expI) / ${i};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function GI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new gg("real",u,t),c=new gg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Fn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Rst(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!1,e)}var oB={kernelName:Bp,backendName:"webgl",kernelFunc:Rst};var WI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}};function Wl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new WI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var sB={kernelName:ru,backendName:"webgl",kernelFunc:Wl};var UI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${e} - x - 1;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${e}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var iB={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new UI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var aB="return floor(x);",Fst=wt({opSnippet:aB,packedOpSnippet:aB,cpuKernelImpl:RL}),lB={kernelName:ds,backendName:"webgl",kernelFunc:Fst};var Ost=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,Pst=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Mst=ue({opSnippet:Ost,packedOpSnippet:Pst,dtype:"int32"}),uB={kernelName:hs,backendName:"webgl",kernelFunc:Mst};var HI=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
|
|
|
|
vec4 values = ${e.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}};var qI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${o}.0, ${n}.0);
|
|
vec4 values = ${e.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${e.output} = result;
|
|
}
|
|
`}};var cB={kernelName:oh,backendName:"webgl",kernelFunc:Lst},Wd,T1=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Lst(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Wd==null||h!==T1)&&(T1=h,Wd=document.createElement("canvas").getContext("2d",{willReadFrequently:T1})),Wd.canvas.width=u,Wd.canvas.height=l,Wd.drawImage(o,0,0,u,l),o=Wd.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Zr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool("WEBGL_PACK")?new qI(p):new HI(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function zst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f==="leakyrelu",E=()=>{let D=[o,s],F=(P,V)=>{if(V==="NCHW"&&P.shape.length===1&&P.shape[0]!==1){let G=rt({inputs:{x:P},backend:e,attrs:{shape:[P.shape[0],1,1]}});return b.push(G),G}return P};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let P=e.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));D.push(P),b.push(P)}return D};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=TI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let D=f?Vl(f,!0):null,F=new Bd(g,w,D,I,N),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,"float32",P)}else if(L().getBool("WEBGL_CONV_IM2COL"))x=_I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Vl(f,!1):null,F=new zd(g,w,D,I,N),P=E();x=e.runWebGLProgram(F,P,"float32")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var pB={kernelName:ji,backendName:"webgl",kernelFunc:zst};function Bst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Vl(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m==="leakyrelu";if(I&&w.push(i),N&&w.push(a),E){let P=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(P),d.push(P)}let A;x?A=new Gd(g,I,b,N,E):A=new Vd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,"float32",D);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var mB={kernelName:Xi,backendName:"webgl",kernelFunc:Bst};var KI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=["x","indices"],this.outputShape=n;let s=zt(n.length),i=`
|
|
int index;`;for(let a=0;a<this.sliceDim;a++)i+=`
|
|
index = round(getIndices(coords[0], ${a}));
|
|
out_of_bounds = out_of_bounds || index < 0;
|
|
out_of_bounds = out_of_bounds || index >= ${this.paramsShape[a]};
|
|
flattenIndex += index * ${this.strides[a]};`;this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
bool out_of_bounds = false;
|
|
|
|
${i}
|
|
|
|
setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function Vst(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=FL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new KI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var fB={kernelName:Va,backendName:"webgl",kernelFunc:Vst};var jI=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Gst(t,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
int index = int(getIndices(resRC.x, resRC.z));
|
|
float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;
|
|
setOutput(inBounds * getA(${o}));
|
|
}
|
|
`}};function Gst(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("index"):n.push(`${e[o]}`);return n.join()}function _1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0];if(L().get("DEBUG")){let b=e.readSync(s.dataId),w=o.shape[u];for(let I=0;I<b.length;++I){let N=b[I];y.assert(N<=w-1&&N>=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),I=OL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new jI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var dB={kernelName:Oi,backendName:"webgl",kernelFunc:_1};var Wst="return float(a > b);",Ust=`
|
|
return vec4(greaterThan(a, b));
|
|
`,Hst=ue({opSnippet:Wst,packedOpSnippet:Ust,cpuKernelImpl:PL,dtype:"bool"}),hB={kernelName:Ga,backendName:"webgl",kernelFunc:Hst};var qst="return float(a >= b);",Kst=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,jst=ue({opSnippet:qst,packedOpSnippet:Kst,dtype:"bool",cpuKernelImpl:ML}),gB={kernelName:xs,backendName:"webgl",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e}=r,{input:n}=t;return GI(n,!0,e)}var xB={kernelName:Vp,backendName:"webgl",kernelFunc:Xst};var Yst="return float(!isnan(x) && !isinf(x));",Zst=wt({opSnippet:Yst,dtype:"bool"}),yB={kernelName:ys,backendName:"webgl",kernelFunc:Zst};var Jst="return float(isinf(x));",Qst=wt({opSnippet:Jst,dtype:"bool"}),bB={kernelName:bs,backendName:"webgl",kernelFunc:Qst};var tit="return float(isnan(x));",eit=wt({opSnippet:tit,dtype:"bool"}),wB={kernelName:ws,backendName:"webgl",kernelFunc:eit};var rit="return float(a < b);",nit=`
|
|
return vec4(lessThan(a, b));
|
|
`,oit=ue({opSnippet:rit,packedOpSnippet:nit,cpuKernelImpl:LL,dtype:"bool"}),IB={kernelName:Wa,backendName:"webgl",kernelFunc:oit};var sit="return float(a <= b);",iit=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,ait=ue({opSnippet:sit,packedOpSnippet:iit,cpuKernelImpl:zL,dtype:"bool"}),CB={kernelName:Ua,backendName:"webgl",kernelFunc:ait};function lit(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=BL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var vB={kernelName:Ha,backendName:"webgl",kernelFunc:lit};var uit=Bo+`
|
|
return x < 0.0 ? 0./0. : log(x);
|
|
`,cit=`
|
|
vec4 result = log(x);
|
|
bvec4 isNaN = isnan(x);
|
|
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
|
|
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
|
|
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
|
|
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
|
|
return result;
|
|
`,pit=wt({opSnippet:uit,packedOpSnippet:cit,cpuKernelImpl:VL}),SB={kernelName:Cs,backendName:"webgl",kernelFunc:pit};var mit=Bo+`
|
|
return log(1.0 + x);
|
|
`,fit=wt({opSnippet:mit}),NB={kernelName:vs,backendName:"webgl",kernelFunc:fit};var dit="return float(a >= 1.0 && b >= 1.0);",hit=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,git=ue({opSnippet:dit,packedOpSnippet:hit,dtype:"bool"}),kB={kernelName:qa,backendName:"webgl",kernelFunc:git};var xit="return float(!(x >= 1.0));",yit=wt({opSnippet:xit}),TB={kernelName:Ka,backendName:"webgl",kernelFunc:yit};var bit="return float(a >= 1.0 || b >= 1.0);",wit=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,Iit=ue({opSnippet:bit,packedOpSnippet:wit,dtype:"bool"}),_B={kernelName:ja,backendName:"webgl",kernelFunc:Iit};var XI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${i}; j <= ${i}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${a}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${u};
|
|
setOutput(val);
|
|
}
|
|
`}};var YI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${i};
|
|
vec2 cache = vec2(0.);
|
|
if(firstChannel >= 0){
|
|
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
|
|
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
|
|
if(hasNextRow){
|
|
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
|
|
}
|
|
}
|
|
|
|
ivec2 depth = ivec2(d, d + 1);
|
|
for (int j = - ${i}; j <= ${i}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));
|
|
|
|
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
|
|
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
|
|
|
|
if(depthInRange || depthPlusOneInRange){
|
|
vec4 z = vec4(0.);
|
|
vec4 xFragAtCurrentDepth;
|
|
z.xz = cache.xy;
|
|
if(depthPlusOneInRange && hasNextCol){
|
|
xFragAtCurrentDepth = idx.y != d ?
|
|
getX(b, r, c, idx.y) : xFragAtOutputCoords;
|
|
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
|
|
if(hasNextRow){
|
|
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
|
|
}
|
|
}
|
|
cache.xy = z.yw;
|
|
sum += z * z;
|
|
}
|
|
}
|
|
vec4 result = xAtOutputCoords * ${u};
|
|
setOutput(result);
|
|
}
|
|
`}};var Cit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new YI(o.shape,s,i,a,u):new XI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},EB={kernelName:Ss,backendName:"webgl",kernelFunc:Cit};var ZI=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${e})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${e} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${o}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${o})
|
|
* float(${s})
|
|
* getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${s});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};var vit=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new ZI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},AB={kernelName:Xa,backendName:"webgl",kernelFunc:vit};function DB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Zn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function E1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A<I.length;A++)I[A]=o.shape[c[A]];let N=gp(w,o.shape,o.dtype,c,I);f=e.makeTensorInfo(I,o.dtype);let E=e.texData.get(f.dataId);E.values=N}else f=Xu(o,c,e);l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("max",l,a);let[d,h]=S.computeOutAndReduceShapes(f.shape,l),g=d;i&&(g=S.expandShapeToKeepDim(d,u));let x;if(m){let w=e.texData.get(f.dataId).values,I=GL(w,y.sizeFromShape(h),g,o.dtype);x=e.makeTensorInfo(g,o.dtype);let N=e.texData.get(x.dataId);N.values=I}else x=DB(f,h,g,e);return p&&e.disposeIntermediateTensorInfo(f),x}var $B={kernelName:Ns,backendName:"webgl",kernelFunc:E1};var Sit=Pd+`
|
|
return max(a, b);
|
|
`,Nit=`
|
|
vec4 result = vec4(max(a, b));
|
|
bvec4 isNaNA = isnan(a);
|
|
bvec4 isNaNB = isnan(b);
|
|
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
|
|
`+Yn+`
|
|
return result;
|
|
`,kit=ue({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:WL}),RB={kernelName:ks,backendName:"webgl",kernelFunc:kit};function Tit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;Ii(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return rr({inputs:{x:o},backend:e});let p=new vi(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var FB={kernelName:Ts,backendName:"webgl",kernelFunc:Tit};function _it(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Yu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var OB={kernelName:Pi,backendName:"webgl",kernelFunc:_it};var JI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${u});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${s};
|
|
wR += ${o}) {
|
|
float dyR = float(dyRCorner + wR) / ${e}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${i}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${i} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},QI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${m}, ${f});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${u};
|
|
wD += ${s}) {
|
|
float dyD = float(dyDCorner + wD) / ${e}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${i}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${a}) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${d} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Eit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Yu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new QI(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var PB={kernelName:nu,backendName:"webgl",kernelFunc:Eit};function Ait(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;Ii([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new vi(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new JI(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var MB={kernelName:Wp,backendName:"webgl",kernelFunc:Ait};function LB(r,t,e,n){let o=new vi(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new vi(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var zB={kernelName:Up,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=LB(n,a,c,u);return[p,m]}};function BB(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Zn(a,"float32","mean",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var VB={kernelName:_s,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let I=i.texData.get(d.dataId).values,N=new Array(a);for(let D=0;D<N.length;D++)N[D]=n.shape[c[D]];let E=gp(I,n.shape,n.dtype,c,N);d=i.makeTensorInfo(N,n.dtype);let A=i.texData.get(d.dataId);A.values=E}else d=Xu(n,c,i);f.push(d),l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("sum",l,a);let[h,g]=S.computeOutAndReduceShapes(d.shape,l),x=h;o&&(x=S.expandShapeToKeepDim(h,u));let b=BB(d,g,x,i);for(let w of f)i.disposeIntermediateTensorInfo(w);return b}};function Dit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Zn(h,h.dtype,"min",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var GB={kernelName:Es,backendName:"webgl",kernelFunc:Dit};var $it=Pd+`
|
|
return min(a, b);
|
|
`,Rit=`
|
|
vec4 result = vec4(min(a, b));
|
|
bvec4 isNaNA = isnan(a);
|
|
bvec4 isNaNB = isnan(b);
|
|
bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
|
|
`+Yn+`
|
|
return result;
|
|
`,Fit=ue({opSnippet:$it,packedOpSnippet:Rit,cpuKernelImpl:UL}),WB={kernelName:As,backendName:"webgl",kernelFunc:Fit};var tC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[0]+t[p]+c[1]);let o=t.length,s=zt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=`
|
|
int start = ${i};
|
|
int end = ${a};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${s} start = ${s}(${i});
|
|
${s} end = ${s}(${a});
|
|
|
|
void main() {
|
|
${s} outC = getOutputCoords();
|
|
for (int i = 0; i < ${o}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${s} coords = outC - start;
|
|
setOutput(getX(${u}));
|
|
}
|
|
`}};var eC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
|
|
${s} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${m};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${m};
|
|
}
|
|
source -= start;
|
|
`;f=`
|
|
${s} rc = outputLoc;
|
|
${d}
|
|
result[0] = getChannel(getX(${l.join()}), ${p});
|
|
${u[o-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
`}else{let d=`
|
|
${s} source = rc;
|
|
${s} lt = ${s}(lessThan(source, start));
|
|
${s} gte = ${s}(greaterThanEqual(source, end));
|
|
${s} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${m}) +
|
|
gte * ((end - 1) * 2 - source + ${m});
|
|
source -= start;
|
|
`;f=`
|
|
${s} rc = outputLoc;
|
|
${d}
|
|
result[0] = getChannel(getX(${l.join()}), ${p});
|
|
${u[o-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
rc = outputLoc;
|
|
${u[o-2]} += 1;
|
|
if(${u[o-2]} < ${this.outputShape[o-2]}) {
|
|
${d}
|
|
result[2] = getChannel(getX(${l.join()}), ${p});
|
|
${u[o-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[3] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${s} start = ${s}(${i});
|
|
const ${s} end = ${s}(${a});
|
|
|
|
void main() {
|
|
${s} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};var Oit=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eC(n.shape,o,s):new tC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},UB={kernelName:Ds,backendName:"webgl",kernelFunc:Oit};var Pit=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Mit=`
|
|
vec4 result = mod(a, b);
|
|
bvec4 isNaN = equal(b, vec4(0.0));
|
|
`+Yn+`
|
|
return result;
|
|
`,Lit=ue({opSnippet:Pit,packedOpSnippet:Mit}),HB={kernelName:Ya,backendName:"webgl",kernelFunc:Lit};var rC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${e-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${e-1}));
|
|
}
|
|
`}};var zit=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Bit=`
|
|
// vec4 one = vec4(equal(a, b));
|
|
// return one + (vec4(1.0) - one) * a / b;
|
|
vec4 result = a / b;
|
|
if(a.x == b.x) {
|
|
result.x = 1.;
|
|
}
|
|
if(a.y == b.y) {
|
|
result.y = 1.;
|
|
}
|
|
if(a.z == b.z) {
|
|
result.z = 1.;
|
|
}
|
|
if(a.w == b.w) {
|
|
result.w = 1.;
|
|
}
|
|
|
|
return result;
|
|
`,A1=ue({opSnippet:zit,packedOpSnippet:Bit,checkOutOfBounds:!0}),qB={kernelName:cs,backendName:"webgl",kernelFunc:A1};var KB="return a - b;",D1=ue({opSnippet:KB,packedOpSnippet:KB,supportsComplex:!0,cpuKernelImpl:pz}),jB={kernelName:ei,backendName:"webgl",kernelFunc:D1};function $1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=E1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=D1({inputs:{a:o,b:l},backend:e}),p=k1({inputs:{x:c},backend:e}),m=xp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=A1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var XB={kernelName:Qs,backendName:"webgl",kernelFunc:$1};function Vit(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:$1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new rC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var YB={kernelName:Za,backendName:"webgl",kernelFunc:Vit};var Git=yr+`
|
|
return -x;
|
|
`,Wit=`
|
|
vec4 result = -x;
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`;function Uit(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=qL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Rn(n.shape,Wit):o=new zr(n.shape,Git),e.runWebGLProgram(o,[n],n.dtype)}var ZB={kernelName:Mi,backendName:"webgl",kernelFunc:Uit};var Hit=Kr.nonMaxSuppressionV3Impl;function qit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=Hit(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var JB={kernelName:Qa,backendName:"webgl",kernelFunc:qit};var Kit=Kr.nonMaxSuppressionV4Impl;function jit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Kit(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var QB={kernelName:tl,backendName:"webgl",kernelFunc:jit};var Xit=Kr.nonMaxSuppressionV5Impl;function Yit(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Xit(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var tV={kernelName:el,backendName:"webgl",kernelFunc:Yit};var nC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${o}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}};var Zit=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new nC(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},eV={kernelName:Rs,backendName:"webgl",kernelFunc:Zit};function xg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=xg({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Fn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var rV={kernelName:qi,backendName:"webgl",kernelFunc:xg};function nV(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=nV({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=xg({inputs:{x:i},backend:e}),u=Fn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Wl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var oV={kernelName:Li,backendName:"webgl",kernelFunc:nV};function Jit(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return VI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=VI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=N1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var sV={kernelName:zi,backendName:"webgl",kernelFunc:Jit};var oC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
|
|
int start = ${i};
|
|
int end = ${a};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${s} start = ${s}(${i});
|
|
${s} end = ${s}(${a});
|
|
|
|
void main() {
|
|
${s} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${s} coords = outC - start;
|
|
setOutput(getX(${u}));
|
|
}
|
|
}
|
|
`}};var sC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=er("rc",o),l=er("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;
|
|
if(${c}) {
|
|
`,o===1?"":`}
|
|
rc = outputLoc;
|
|
${u[o-2]} += 1;
|
|
if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${u[o-1]} += 1;
|
|
if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
|
|
${m[h]}
|
|
if (${f}) {
|
|
result[${h}] = float(value);
|
|
} else {
|
|
${s} source = rc - start;
|
|
result[${h}] = getChannel(getX(${l.join()}), ${p});
|
|
}
|
|
`;d+=o===1?"} ":"}}",this.userCode=`
|
|
const ${s} start = ${s}(${i});
|
|
const ${s} end = ${s}(${a});
|
|
|
|
void main() {
|
|
${s} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}};var R1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Wl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sC(o.shape,s,i):new oC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},iV={kernelName:Fs,backendName:"webgl",kernelFunc:R1};var Qit=`
|
|
if(a < 0.0 && floor(b) < b){
|
|
return NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
return (round(mod(b, 2.0)) != 1) ?
|
|
pow(abs(a), b) : sign(a) * pow(abs(a), b);
|
|
`,tat=`
|
|
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
|
|
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
|
|
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
|
|
vec4 result = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
bvec4 isExpZero = equal(b, vec4(0.0));
|
|
result.r = isExpZero.r ? 1.0 : result.r;
|
|
result.g = isExpZero.g ? 1.0 : result.g;
|
|
result.b = isExpZero.b ? 1.0 : result.b;
|
|
result.a = isExpZero.a ? 1.0 : result.a;
|
|
|
|
bvec4 isNaN1 = lessThan(a, vec4(0.0));
|
|
bvec4 isNaN2 = lessThan(floor(b), b);
|
|
bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
|
|
`+Yn+`
|
|
return result;
|
|
`,eat=ue({opSnippet:Qit,packedOpSnippet:tat}),aV={kernelName:Os,backendName:"webgl",kernelFunc:eat};function rat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=jL(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=mc(o.dtype),w=Zn(x,b,"prod",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var lV={kernelName:Ms,backendName:"webgl",kernelFunc:rat};function nat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=XL(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var uV={kernelName:Hp,backendName:"webgl",kernelFunc:nat};function oat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=YL(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var cV={kernelName:qp,backendName:"webgl",kernelFunc:oat};function sat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=ZL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var pV={kernelName:Kp,backendName:"webgl",kernelFunc:sat};var F1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=JL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},mV={kernelName:ou,backendName:"webgl",kernelFunc:F1};var iat="return 1.0 / x;",aat=wt({opSnippet:iat}),fV={kernelName:Ls,backendName:"webgl",kernelFunc:aat};var lat=yr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,uat=`
|
|
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,cat=wt({opSnippet:lat,packedOpSnippet:uat}),dV={kernelName:zs,backendName:"webgl",kernelFunc:cat};var pat=yr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,mat=`
|
|
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,fat=wt({opSnippet:pat,packedOpSnippet:mat}),hV={kernelName:Gs,backendName:"webgl",kernelFunc:fat};var iC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${m};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};var aC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]},
|
|
${c[1]/p[1]});
|
|
const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
|
|
${u}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${m};
|
|
|
|
// Compute the four integer indices.
|
|
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
|
|
ivec3 sourceCeilRC = ivec3(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function dat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aC(o.shape,u,l,s,i):new iC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var gV={kernelName:Vs,backendName:"webgl",kernelFunc:dat};var lC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${m});
|
|
const float invWidthScale = float(${f});
|
|
|
|
const int winHeight = int(${d});
|
|
const int winWidth = int(${h});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function hat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new lC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var xV={kernelName:nl,backendName:"webgl",kernelFunc:hat};var uC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${f};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};var cC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]},
|
|
${c[1]/p[1]});
|
|
const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
|
|
${u}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${f};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function gat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cC(o.shape,u,l,s,i):new uC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var yV={kernelName:Bs,backendName:"webgl",kernelFunc:gat};var pC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${m});
|
|
const float invWidthScale = float(${f});
|
|
|
|
const int winHeight = int(${d});
|
|
const int winWidth = int(${h});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${u[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${u[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${o}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${s}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function xat(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new pC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var bV={kernelName:rl,backendName:"webgl",kernelFunc:xat};var mC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${t[0]} - coord - 1));
|
|
}
|
|
`;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=zt(n);this.userCode=`
|
|
void main() {
|
|
${i} coords = getOutputCoords();
|
|
setOutput(getX(${s}));
|
|
}
|
|
`}};var fC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=er("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${t[0]} - rc - 1),
|
|
${t[0]} - rc - 1);
|
|
if(${s}){
|
|
result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),
|
|
${t[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${u(o.slice())};
|
|
if(${s}){
|
|
result.g = ${l(o.slice())};
|
|
}
|
|
if(${i}) {
|
|
result.b = ${c(o.slice())};
|
|
if(${s}) {
|
|
result.a = ${p(o.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function yat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return rr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fC(o.shape,a):new mC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var wV={kernelName:Ws,backendName:"webgl",kernelFunc:yat};var dC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=`
|
|
vec3 fill = vec3(${e.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${s}
|
|
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var IV={kernelName:fl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new dC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var bat=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,wat=wt({opSnippet:bat}),CV={kernelName:Us,backendName:"webgl",kernelFunc:wat};var Iat="return inversesqrt(x);",Cat=wt({opSnippet:Iat,cpuKernelImpl:QL}),vV={kernelName:Hs,backendName:"webgl",kernelFunc:Cat};var Zu=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=`
|
|
${l} strides = ${l}(${s});
|
|
|
|
void main() {
|
|
${c} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${t}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${e}; j++) {
|
|
int index = round(${m});
|
|
flattenedIndex += index * ${x};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${d};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(${g}, sum, float(found)));
|
|
}
|
|
`}};var hC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=`
|
|
${l} strides = ${l}(${s});
|
|
|
|
void main() {
|
|
${c} coords = getOutputCoords();
|
|
vec4 sum = vec4(0.);
|
|
vec4 found = vec4(0.);
|
|
for (int i = 0; i < ${t}; i+=2) {
|
|
ivec2 flattenedIndex = ivec2(0);
|
|
for (int j = 0; j < ${e}; j+=2) {
|
|
ivec4 index = round(${m});
|
|
flattenedIndex += index.xz * ${x};
|
|
if (j + 1 < ${e}) {
|
|
flattenedIndex += index.yw * ${b};
|
|
}
|
|
}
|
|
if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
|
|
flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
|
|
vec4 updVals = ${d};
|
|
if (flattenedIndex[0] == coords[0]) {
|
|
sum.xy += updVals.xy;
|
|
found.xy = vec2(1.);
|
|
} else if (flattenedIndex[0] == coords[0] + 1) {
|
|
sum.zw += updVals.xy;
|
|
found.zw = vec2(1.);
|
|
}
|
|
if (flattenedIndex[1] == coords[0]) {
|
|
sum.xy += updVals.zw;
|
|
found.xy = vec2(1.);
|
|
} else if (flattenedIndex[1] == coords[0] + 1) {
|
|
sum.zw += updVals.zw;
|
|
found.zw = vec2(1.);
|
|
}
|
|
}
|
|
}
|
|
setOutput(mix(${g}, sum, found));
|
|
}
|
|
`}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new hC(u,a,f.shape.length,d.shape.length,c,m):g=new Zu(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var SV={kernelName:ol,backendName:"webgl",kernelFunc:vat};var gC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=`
|
|
int findBound(int batch, float value) {
|
|
int left = 0;
|
|
int right = numInputs;
|
|
int mid;
|
|
${a}
|
|
mid = (left + right) / 2;
|
|
if (getSortedSequence(batch, mid) ${u} value) {
|
|
left = mid + 1;
|
|
} else {
|
|
right = mid;
|
|
}
|
|
}
|
|
return right;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int valueIndex = coords[1];
|
|
|
|
float value = getValues(batch, valueIndex);
|
|
|
|
setOutput(float(findBound(batch, value)));
|
|
}
|
|
`}};function Sat(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new gC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var NV={kernelName:il,backendName:"webgl",kernelFunc:Sat};var xC=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c<e.length;c++)l.push(`${a[c]}`),c<t&&u.push(`${a[c]}`);o=u.join(),s=l.join()}let i=zt(n);this.userCode=`
|
|
void main() {
|
|
${i} resRC = getOutputCoords();
|
|
float cVal = getC(${o});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${s}));
|
|
} else {
|
|
setOutput(getB(${s}));
|
|
}
|
|
}
|
|
`}};function Nat(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new xC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var kV={kernelName:Vi,backendName:"webgl",kernelFunc:Nat};var kat=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${S.SELU_SCALEALPHA};
|
|
float scale = ${S.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Tat=wt({opSnippet:kat}),TV={kernelName:qs,backendName:"webgl",kernelFunc:Tat};var _at=Bo+`
|
|
return 1.0 / (1.0 + exp(-1.0 * x));
|
|
`,Eat=`
|
|
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,Aat=wt({opSnippet:_at,packedOpSnippet:Eat,cpuKernelImpl:ez}),_V={kernelName:Xs,backendName:"webgl",kernelFunc:Aat};var Dat=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,$at=wt({opSnippet:Dat}),EV={kernelName:js,backendName:"webgl",kernelFunc:$at};var Rat=Bo+`
|
|
return sin(x);
|
|
`,Fat=`
|
|
vec4 result = sin(x);
|
|
bvec4 isNaN = isnan(x);
|
|
${Yn}
|
|
return result;
|
|
`,Oat=wt({opSnippet:Rat,packedOpSnippet:Fat}),AV={kernelName:Ks,backendName:"webgl",kernelFunc:Oat};var Pat=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Mat=wt({opSnippet:Pat}),DV={kernelName:al,backendName:"webgl",kernelFunc:Mat};var Lat=`
|
|
float epsilon = 1.1920928955078125e-7;
|
|
float threshold = log(epsilon) + 2.0;
|
|
|
|
bool too_large = x > -threshold;
|
|
bool too_small = x < threshold;
|
|
|
|
float result;
|
|
float exp_x = exp(x);
|
|
|
|
if (too_large){
|
|
result = x;
|
|
}
|
|
else if (too_small){
|
|
result = exp_x;
|
|
}
|
|
else{
|
|
result = log(exp_x + 1.0);
|
|
}
|
|
return result;
|
|
`,zat=wt({opSnippet:Lat}),$V={kernelName:Ys,backendName:"webgl",kernelFunc:zat};var Bat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;x<o.shape.length;++x)u.push([0,0]);let l=[],c=R1({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,s,a,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,a,!1),d=rt({inputs:{x:c},backend:e,attrs:{shape:p}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:m}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:f}});return l.push(c),l.push(d),l.push(h),l.forEach(x=>e.disposeIntermediateTensorInfo(x)),g},RV={kernelName:Wi,backendName:"webgl",kernelFunc:Bat};function Vat(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var FV={kernelName:su,backendName:"webgl",kernelFunc:Vat};function Gat(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=oz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var OV={kernelName:ll,backendName:"webgl",kernelFunc:Gat};function Wat(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var PV={kernelName:iu,backendName:"webgl",kernelFunc:Wat};function Uat(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=Jw(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var MV={kernelName:au,backendName:"webgl",kernelFunc:Uat};function Hat(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=tz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Zu(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var LV={kernelName:ul,backendName:"webgl",kernelFunc:Hat};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=Si({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var zV={kernelName:Ui,backendName:"webgl",kernelFunc:qat};var BV="return sqrt(x);",Kat=wt({opSnippet:BV,packedOpSnippet:BV,cpuKernelImpl:sz}),VV={kernelName:Zs,backendName:"webgl",kernelFunc:Kat};var jat="return x * x;",Xat=wt({opSnippet:jat}),GV={kernelName:lu,backendName:"webgl",kernelFunc:Xat};var WV="return (a - b) * (a - b);",Yat=ue({opSnippet:WV,packedOpSnippet:WV}),UV={kernelName:ti,backendName:"webgl",kernelFunc:Yat};function Zat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=iz(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var HV={kernelName:sc,backendName:"webgl",kernelFunc:Zat};function Jat({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var qV={kernelName:yo,backendName:"webgl",kernelFunc:Jat};var yC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${s} begin = ${s}(${t});
|
|
${s} strides = ${s}(${e});
|
|
|
|
void main() {
|
|
${i} coords = getOutputCoords();
|
|
setOutput(getX(${a}));
|
|
}
|
|
`}};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=ze.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=ze.computeOutShape(b,w,I),D=Si({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=bt(o.shape,o.dtype,D),P=az(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new yC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var KV={kernelName:cl,backendName:"webgl",kernelFunc:Qat};function tlt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=lz(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var jV={kernelName:uu,backendName:"webgl",kernelFunc:tlt};function elt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=uz(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var XV={kernelName:cu,backendName:"webgl",kernelFunc:elt};function rlt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=cz(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var YV={kernelName:pu,backendName:"webgl",kernelFunc:rlt};var nlt="return tan(x);",olt=wt({opSnippet:nlt}),ZV={kernelName:ri,backendName:"webgl",kernelFunc:olt};var slt=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,ilt=wt({opSnippet:slt}),JV={kernelName:ni,backendName:"webgl",kernelFunc:ilt};function alt(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Zu(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var QV={kernelName:sl,backendName:"webgl",kernelFunc:alt};var bC=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[i]*e[i];this.outputShape=n,this.rank=n.length;let o=zt(this.rank),s=llt(t);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function llt(r){let t=r.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${e[o]}, ${r[o]})`);return n.join()}function O1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let u=e.readSync(o.dataId),l=o.dtype==="string"?u.map(m=>y.decodeString(m)):u,c=bt(o.shape,o.dtype,l),p=mz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new bC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var tG={kernelName:so,backendName:"webgl",kernelFunc:O1};var wC=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced above,
|
|
// Figure5(a) shows that element[1] is in the
|
|
// second half of the group when group size is 2, but it is in the
|
|
// first half of the group when group size is 4.
|
|
|
|
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
|
|
int i = isFirstInPair ? elemIdx : elemIdx - inc;
|
|
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
|
|
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
|
|
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
|
|
|
|
// Denotes which direction indices are in (ascending or descending).
|
|
bool reverse = imod(elemIdx, 2 * dir) >= dir;
|
|
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) { // Elements in opposite order of direction
|
|
int iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutput(float(i0));
|
|
} else {
|
|
setOutput(float(i1));
|
|
}
|
|
}
|
|
`}},IC=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
|
|
// we only need to output the indices at positions |, the indices at
|
|
// positions _ can be thrown away, see Figure5(b) After Phase 2
|
|
// (Merge phase) in the Bitonic Top K paper referenced above.
|
|
// For example, the paper shows we only need to output the orange bars.
|
|
// The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back
|
|
// to the previous sequence to find the corresponding value,
|
|
// we need to double the index. When we double the index,
|
|
// we basically interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
|
|
// of each 2k positions by - elemIdx % k. E.g. for output at
|
|
// index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
|
|
|
|
float x0 = getX(batch, i0);
|
|
float x1 = i1 < n ? getX(batch, i1) : x0;
|
|
|
|
setOutput(x0 >= x1 ? float(i0) : float(i1));
|
|
}
|
|
`}};function Ip(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function eG(r){let t=1;for(;t<r;)t*=2;return t}function ult(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n,a=L().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=L().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=o.shape,c=l[l.length-1];if(e.shouldExecuteOnCPU([o])||c<a||s>u){let P=e.readSync(o.dataId),[V,G]=fz(P,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,"int32",[])];if(c===1)return[o,Wl({attrs:{shape:l,dtype:"int32",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&Ip(e,f);let x=eG(s),b=eG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=I(),q=new wC(G),K=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[V]],X=w;w=e.runWebGLProgram(q,W,"int32",K),Ip(e,X)};for(let P=1;P<x;P*=2){let V=P*2;for(let G=P;G>=1;G/=2)N(V,G,[h,b])}for(let P=b;P>x;P/=2){let V=I(),G=new IC([h,P/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,"int32",q),Ip(e,H);let K=x/2,X=K*2;for(let Z=K;Z>=1;Z/=2)N(X,Z,w.shape)}let E=w;w=Si({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,E);let A=_1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});Ip(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),Ip(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),Ip(e,F),[A,w]}var rG={kernelName:pl,backendName:"webgl",kernelFunc:ult};var CC=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${u} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${u} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${u} == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
} else {
|
|
return outCoord;
|
|
}
|
|
}
|
|
|
|
float readWithFillValue(int batch, int coordY, int coordX,
|
|
int channel) {
|
|
float outputValue;
|
|
if (0 <= coordY && coordY < ${t} && 0 <= coordX && coordX < ${e}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${s});
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
float outputValue;
|
|
int batch = coords[0];
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
int channel = coords[3];
|
|
float xf = float(x);
|
|
float yf = float(y);
|
|
float a1 = getTransforms(batch, 0);
|
|
float a2 = getTransforms(batch, 1);
|
|
float a3 = getTransforms(batch, 2);
|
|
float b1 = getTransforms(batch, 3);
|
|
float b2 = getTransforms(batch, 4);
|
|
float b3 = getTransforms(batch, 5);
|
|
float c1 = getTransforms(batch, 6);
|
|
float c2 = getTransforms(batch, 7);
|
|
float projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = float(${s});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${e}));
|
|
float mapY = mapCoord(inY, float(${t}));
|
|
|
|
if (${a} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function clt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new CC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],"float32")}var nG={kernelName:ml,backendName:"webgl",kernelFunc:clt};function plt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;Ii(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=dz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var oG={kernelName:mu,backendName:"webgl",kernelFunc:plt};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;h<a;h++)h!==s&&(l[c++]=i.shape[h]);let p=[],m=new Array(a).fill(0),f=i.shape.slice();f[s]=1;let d=new Array(u);for(let h=0;h<d.length;h++){m[s]=h;let g=Si({inputs:{x:i},backend:e,attrs:{begin:m,size:f}}),x=rt({inputs:{x:g},backend:e,attrs:{shape:l}});d[h]=x,p.push(g)}return p.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var sG={kernelName:Hi,backendName:"webgl",kernelFunc:mlt};var vC=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
|
|
sumValue += dot(values, segFilter);
|
|
`,f="";s%n>0&&(f=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`);let d="";s%n>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${u};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${f}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${d}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${i})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${i})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${m}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${p===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
0
|
|
);
|
|
|
|
${m}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function flt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=mc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],P=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(P,D),G={windowSize:V,inSize:P,batchSize:F,numSegments:D},W=new vC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=F1({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),K=O1({inputs:{x:H},backend:e,attrs:{reps:[P/V]}});return u.push(H),u.push(K),g(q,N,K,A,D)},x=g(d,"unsortedSegmentSum",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var iG={kernelName:fu,backendName:"webgl",kernelFunc:flt};var dlt=[Wz,Hz,qz,Kz,Xz,Yz,Zz,Jz,e3,r3,n3,o3,s3,i3,a3,l3,u3,c3,p3,m3,f3,h3,g3,x3,I3,v3,S3,Rz,k3,_3,E3,A3,D3,$3,R3,F3,O3,P3,M3,B3,V3,G3,W3,U3,H3,q3,K3,j3,X3,Y3,Z3,J3,Q3,tB,eB,nB,oB,sB,iB,lB,uB,cB,pB,mB,fB,dB,hB,gB,$z,xB,T3,yB,bB,wB,Fz,IB,CB,vB,SB,NB,kB,TB,_B,EB,AB,$B,RB,FB,OB,PB,MB,zB,VB,GB,WB,UB,HB,YB,Mz,ZB,JB,QB,tV,y3,eV,oV,sV,iV,aV,Oz,lV,uV,cV,pV,mV,b3,qB,fV,dV,hV,zz,gV,xV,yV,bV,wV,IV,CV,vV,SV,NV,kV,TV,_V,EV,AV,DV,d3,XB,$V,RV,FV,OV,PV,MV,LV,zV,VV,GV,UV,HV,qV,KV,jV,XV,YV,jB,Vz,ZV,JV,QV,tG,rG,nG,Gz,oG,sG,iG,rV];for(let r of dlt)ic(r);var kt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(kt||(kt={}));var Ju;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu",r[r.sigmoid=5]="sigmoid",r[r.elu=6]="elu"})(Ju||(Ju={}));var aG;function hlt(r){aG=r.wasm.cwrap(Ki,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function glt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Ur.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return aG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var lG={kernelName:Ki,backendName:"wasm",setupFunc:hlt,kernelFunc:glt};function Ct(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,kt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var uG=Ct(_i);var cG=Ct(Ho);var pG=Ct(qo);function ae(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return(()=>n(p,g,l.shape.length,m,x,c.shape.length,kt[l.dtype],b))(),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var xlt=!0,mG=ae(oo,xlt);var fG;function ylt(r){fG=r.wasm.cwrap(Ko,null,["array","number","number","number"])}function blt(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return fG(s,o.length,kt[n.dtype],i),n}var dG={kernelName:Ko,backendName:"wasm",setupFunc:ylt,kernelFunc:blt};function 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e=[],n=[];for(let o=0;o<r.length;++o)r[o]!==1&&e.push(r[o]),r[t[o]]!==1&&n.push(t[o]);for(let o=0;o<n.length;++o){let s=-1;for(let i=0;i<n.length;++i)n[i]>=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var xG={kernelName:io,backendName:"wasm",kernelFunc:fo,setupFunc:wlt};function vn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f<c.length;f++)c[f]=n[a[f]];i=S.getInnerMostAxes(i.length,o),u=fo({inputs:{x:r},attrs:{perm:a},backend:e});let p=e.dataIdMap.get(r.dataId).id;e.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var yG;function vlt(r){yG=r.wasm.cwrap(Ea,null,["number, number, number"])}function Slt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("all",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;yG(u,x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var bG={kernelName:Ea,backendName:"wasm",setupFunc:vlt,kernelFunc:Slt};var wG;function Nlt(r){wG=r.wasm.cwrap(Aa,null,["number, number, number"])}function klt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("any",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;wG(u,x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var IG={kernelName:Aa,backendName:"wasm",setupFunc:Nlt,kernelFunc:klt};function SC(r){let t;function e(o){t=o.wasm.cwrap(r,null,["number","number","number","number","number"])}function n(o){let{backend:s,inputs:i,attrs:a}=o,{axis:u}=a,{x:l}=i,c=s.dataIdMap.get(l.dataId).id,p=c,m=l,{transposed:f,axes:d,inputWasTransposed:h}=vn(l,u,s);if(h){let N=s.dataIdMap.get(f.dataId).id;N!==c&&(m=f,p=N)}let g=m.shape.slice(0,-1),x=s.makeOutput(g,"int32"),b=s.dataIdMap.get(x.dataId).id,w=y.sizeFromShape(x.shape),I=m.shape[d[0]];return t(p,kt[m.dtype],w,I,b),h&&s.disposeData(f.dataId),x}return{kernelName:r,backendName:"wasm",setupFunc:e,kernelFunc:n}}var CG=SC(Ei);var vG=SC(Ai);var SG=Ct(jo);var NG=Ct(Xo);var kG=Ct(Yo);var TG=ae(Jo,!1);var _G=Ct(Zo);var EG;function Tlt(r){EG=r.wasm.cwrap(Qo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _lt(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.strideHeight,b=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let I=n.makeOutput(c.outShape,"float32"),N=n.dataIdMap.get(I.dataId).id;return EG(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,N),I}var AG={kernelName:Qo,backendName:"wasm",setupFunc:Tlt,kernelFunc:_lt};var DG;function Elt(r){DG=r.wasm.cwrap("AvgPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Alt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return DG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var $G={kernelName:Di,backendName:"wasm",setupFunc:Elt,kernelFunc:Alt};var RG;function Dlt(r){RG=r.wasm.cwrap("AvgPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $lt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return RG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left,c.filterDepth,c.filterHeight,c.filterWidth),p}var FG={kernelName:Xl,backendName:"wasm",setupFunc:Dlt,kernelFunc:$lt};function mr(r){let{inputs:t,attrs:e}=r,{x:n}=t,{shape:o}=e,s=y.sizeFromShape(n.shape),i=y.inferFromImplicitShape(o,s);return y.assert(s===y.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var OG={kernelName:Bi,backendName:"wasm",kernelFunc:mr};var PG;function Rlt(r){PG=r.wasm.cwrap(ts,null,["number","array","number","number","array","number","number","number","number"])}function Flt(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Ur.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${i} and transposeB=${a} must match.`);let I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=mr({inputs:{x:o},backend:e,attrs:{shape:I}}),A=mr({inputs:{x:s},backend:e,attrs:{shape:N}}),D=e.dataIdMap.get(E.dataId).id,F=e.dataIdMap.get(A.dataId).id,P=i?E.shape[2]:E.shape[1],V=a?A.shape[1]:A.shape[2],G=Math.max(g,x),W=e.makeOutput([G,P,V],E.dtype),q=e.dataIdMap.get(W.dataId).id,H=new Uint8Array(new Int32Array(E.shape).buffer),K=new Uint8Array(new Int32Array(A.shape).buffer);return PG(D,H,E.shape.length,F,K,A.shape.length,i,a,q),e.disposeData(E.dataId),e.disposeData(A.dataId),W.shape=w,W}var MG={kernelName:ts,backendName:"wasm",setupFunc:Rlt,kernelFunc:Flt};function Vo(r){let{inputs:{x:t},attrs:{begin:e,size:n},backend:o}=r,[s,i]=ze.parseSliceParams(t,e,n),a=ze.isSliceContinous(t.shape,s,i),u=o.readSync(t.dataId),l=o.makeOutput(i,t.dtype),c=y.computeStrides(t.shape),p=o.dataIdMap.get(l.dataId);if(a){let d=ze.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype==="string"){let d=sp(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Olt(u,c[0],m,s,i);else if(f===3)Plt(u,c[0],c[1],m,s,i);else if(f===4)Mlt(u,c[0],c[1],c[2],m,s,i);else{let d=sp(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Olt(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;l<u;l++){let c=l*t+a;e.set(r.subarray(c,c+o[1]),s),s+=o[1]}}function Plt(r,t,e,n,o,s){let i=0,a=o[0],u=o[1],l=o[2],c=a+s[0],p=u+s[1];for(let m=a;m<c;m++)for(let f=u;f<p;f++){let d=m*t+f*e+l;n.set(r.subarray(d,d+s[2]),i),i+=s[2]}}function Mlt(r,t,e,n,o,s,i){let a=0,u=s[0],l=s[1],c=s[2],p=u+i[0],m=l+i[1],f=c+i[2],d=s[3];for(let h=u;h<p;h++)for(let g=l;g<m;g++)for(let x=c;x<f;x++){let b=h*t+g*e+x*n+d;o.set(r.subarray(b,b+i[3]),a),a+=i[3]}}var LG={kernelName:Gi,backendName:"wasm",kernelFunc:Vo};function Llt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n,a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=fo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Vo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(f.dataId),g}var zG={kernelName:$i,backendName:"wasm",kernelFunc:Llt};var BG;function zlt(r){BG=r.wasm.cwrap(Da,null,["number","number","boolean","number","number","number"])}function Blt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return BG(c(o),i,a,c(s),kt[s.dtype],c(l)),l}var VG={kernelName:Da,backendName:"wasm",setupFunc:zlt,kernelFunc:Blt};function Vlt(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],"int32",void 0,new Int32Array(a))}var GG={kernelName:Yl,backendName:"wasm",kernelFunc:Vlt};function On(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var WG={kernelName:ho,backendName:"wasm",kernelFunc:On};var UG=Ct(es);var HG;function Glt(r){HG=r.wasm.cwrap(go,null,["number","number","number","number"])}function Wlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return HG(a,s,i,l),u}var qG={kernelName:go,backendName:"wasm",setupFunc:Glt,kernelFunc:Wlt};function P1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return Cp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return mr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=rp(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f<u;f++){let d=f*l;for(let h=0;h<p.length;h++){let g=c[h],x=f*g,b=p[h].subarray(x,x+g);m.set(b,d),d+=g}}return a}var KG={kernelName:Ri,backendName:"wasm",kernelFunc:P1};var jG;function Ult(r){jG=r.wasm.cwrap(rs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hlt(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:m}=e,f=S.convertConv2DDataFormat(m),d=S.computeConv2DInfo(o.shape,s.shape,u,l,c,p,!1,f),h=d.filterHeight,g=d.filterWidth,x=d.padInfo.top,b=d.padInfo.right,w=d.padInfo.bottom,I=d.padInfo.left,N=d.dilationHeight,E=d.dilationWidth,A=d.strideHeight,D=d.strideWidth,F=d.inChannels,P=d.outChannels,V=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let G=n.makeOutput(d.outShape,"float32"),W=n.dataIdMap.get(G.dataId).id;return jG(i,o.shape[0],o.shape[1],o.shape[2],a,h,g,x,b,w,I,V,N,E,A,D,F,P,W),G}var XG={kernelName:rs,backendName:"wasm",setupFunc:Ult,kernelFunc:Hlt};var YG;function qlt(r){YG=r.wasm.cwrap(ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Klt(r){let{backend:t,inputs:e,attrs:n}=r,{dy:o,filter:s}=e,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,inputShape:c}=n,p=1,m=S.convertConv2DDataFormat(u),f=S.computeConv2DInfo(c,s.shape,i,p,a,l,!1,m),{batchSize:d,filterHeight:h,filterWidth:g,inChannels:x,inHeight:b,inWidth:w,outChannels:I,outHeight:N,outWidth:E,strideHeight:A,strideWidth:D}=f,F=h-1-f.padInfo.top,P=g-1-f.padInfo.left,V=f.dataFormat==="channelsLast",G=y.computeStrides(f.inShape),W=y.computeStrides(o.shape),[q,H,K]=y.computeStrides(s.shape),X=G[0],Z=V?G[1]:G[2],et=V?G[2]:1,nt=V?1:G[1],st=W[0],at=V?W[1]:W[2],ot=V?W[2]:1,it=V?1:W[1],mt=t.makeOutput(f.inShape,"float32"),gt=t.dataIdMap.get(mt.dataId).id,It=t.dataIdMap.get(o.dataId).id,Rt=t.dataIdMap.get(s.dataId).id;return YG(It,Rt,d,h,g,b,w,x,N,E,I,A,D,F,P,q,H,K,X,Z,et,nt,st,at,ot,it,gt),mt}var ZG={kernelName:ns,backendName:"wasm",setupFunc:qlt,kernelFunc:Klt};var JG;function jlt(r){JG=r.wasm.cwrap(os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Xlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor x must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=e.makeOutput(l.outShape,o.dtype);return JG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var QG={kernelName:os,backendName:"wasm",setupFunc:jlt,kernelFunc:Xlt};var tW;function Ylt(r){tW=r.wasm.cwrap($a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,u,i,1,a),c=e.makeOutput(l.filterShape,s.dtype);return tW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var eW={kernelName:$a,backendName:"wasm",setupFunc:Ylt,kernelFunc:Zlt};var rW;function Jlt(r){rW=r.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qlt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(u,s.shape,a,1,i),c=e.makeOutput(l.inShape,o.dtype);return rW(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var nW={kernelName:Ra,backendName:"wasm",setupFunc:Jlt,kernelFunc:Qlt};var oW=Ct(ss);var sW=Ct(is);var M1;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(M1||(M1={}));var iW;function tut(r){iW=r.wasm.cwrap(Oa,null,["number","number","number","number","array","number","number","number","number","number"])}function eut(r){let{backend:t,inputs:e,attrs:n}=r,{method:o,extrapolationValue:s,cropSize:i}=n,{image:a,boxes:u,boxInd:l}=e,c=u.shape[0],[p,m]=i,f=[c,p,m,a.shape[3]],d=t.dataIdMap.get(a.dataId),h;a.dtype!=="float32"&&(h=On({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),d=t.dataIdMap.get(h.dataId));let g=d.id,x=t.dataIdMap.get(u.dataId).id,b=t.dataIdMap.get(l.dataId).id,w=t.makeOutput(f,"float32"),I=t.dataIdMap.get(w.dataId).id,N=new Uint8Array(new Int32Array(a.shape).buffer);return iW(g,x,b,c,N,p,m,M1[o],s,I),h!=null&&t.disposeData(h.dataId),w}var aW={kernelName:Oa,backendName:"wasm",setupFunc:tut,kernelFunc:eut};var lW;function rut(r){lW=r.wasm.cwrap(Fa,null,["number","number","number","number","number","number"])}function nut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=fo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;lW(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=fo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var uW={kernelName:Fa,backendName:"wasm",setupFunc:rut,kernelFunc:nut};var cW;function out(r){cW=r.wasm.cwrap(as,null,["number","number","number","number","number","number"])}function sut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=fo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;cW(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=fo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var pW={kernelName:as,backendName:"wasm",setupFunc:out,kernelFunc:sut};var mW;function iut(r){mW=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function aut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return mW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),kt[s.dtype],a,p(c)),c}var fW={kernelName:Jl,backendName:"wasm",setupFunc:iut,kernelFunc:aut};var dW;function lut(r){dW=r.wasm.cwrap(Pa,null,["number","number","number","array","number","array","array","number","number"])}function uut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return dW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var hW={kernelName:Pa,backendName:"wasm",setupFunc:lut,kernelFunc:uut};var gW;function cut(r){gW=r.wasm.cwrap(ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function put(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,"float32"),G=n.dataIdMap.get(V.dataId).id;return gW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,P,I,N,E,A,D,F,G),V}var xW={kernelName:ls,backendName:"wasm",setupFunc:cut,kernelFunc:put};var yW;function mut(r){yW=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function fut(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return yW(e.dataIdMap.get(n.dataId).id,kt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var bW={kernelName:Ql,backendName:"wasm",setupFunc:mut,kernelFunc:fut};var wW;function dut(r){wW=r.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c=e.makeOutput(l.outShape,o.dtype);return wW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,kt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var IW={kernelName:us,backendName:"wasm",setupFunc:dut,kernelFunc:hut};var CW;function gut(r){CW=r.wasm.cwrap(eu,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return CW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var vW={kernelName:eu,backendName:"wasm",setupFunc:gut,kernelFunc:xut};var SW;function yut(r){SW=r.wasm.cwrap(tu,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function but(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return SW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var NW={kernelName:tu,backendName:"wasm",setupFunc:yut,kernelFunc:but};var kW=Ct(ps);var TW;function wut(r){TW=r.wasm.cwrap(Ma,null,["number","number","number"])}function Iut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return TW(i(o),i(n),i(s)),s}var _W={kernelName:Ma,backendName:"wasm",setupFunc:wut,kernelFunc:Iut};var Cut=!1,EW=ae(za,Cut,"bool");var AW=Ct(ms,"float32");function NC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var DW={kernelName:Fi,backendName:"wasm",kernelFunc:NC};var $W=Ct(fs,"float32");function L1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var RW={kernelName:ru,backendName:"wasm",kernelFunc:L1};var FW;function vut(r){FW=r.wasm.cwrap(Ba,null,["number","number","number","number","number","number"])}function Sut(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return FW(s,a,u,l,c,i),o}var OW={kernelName:Ba,backendName:"wasm",kernelFunc:Sut,setupFunc:vut};var PW=Ct(ds);var Nut=!1,MW=ae(hs,Nut);var LW;function kut(r){LW=r.wasm.cwrap(gs,null,["number","number","number","number","number","number","number"])}function Tut(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return LW(c,p,m,f,d,o,g),h}var zW={kernelName:gs,backendName:"wasm",setupFunc:kut,kernelFunc:Tut};var BW;function _ut(r){BW=r.wasm.cwrap(ji,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Eut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=Ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return BW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var VW={kernelName:ji,backendName:"wasm",setupFunc:_ut,kernelFunc:Eut};var GW;function Aut(r){GW=r.wasm.cwrap(Xi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=Ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,P=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,K=h.padInfo.type==="SAME"?1:0,X=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,at=a==null?0:n.dataIdMap.get(a.dataId).id;return GW(x,X,Z,et,b,N,E,I,A,D,F,P,K,V,G,W,q,H,w,g,at,d||0,st),nt}var WW={kernelName:Xi,backendName:"wasm",setupFunc:Aut,kernelFunc:Dut};var UW;function $ut(r){UW=r.wasm.cwrap(Va,null,["number","number","number","number","number","number","array","number"])}function Rut(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Dy.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return UW(f,kt[n.dtype],h,i,p,a,g,x),l}var HW={kernelName:Va,backendName:"wasm",setupFunc:$ut,kernelFunc:Rut};var qW;function Fut(r){qW=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function 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d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;hU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var gU={kernelName:Ns,backendName:"wasm",setupFunc:Zut,kernelFunc:Jut};var Qut=!1,xU=ae(ks,Qut);var yU;function tct(r){yU=r.wasm.cwrap(Ts,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ect(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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wU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var IU={kernelName:Pi,backendName:"wasm",setupFunc:rct,kernelFunc:nct};var CU;function oct(r){CU=r.wasm.cwrap("MaxPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sct(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return 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bct(r){zU=r.wasm.cwrap(tl,"number",["number","number","number","number","number","bool"])}function wct(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=zU(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Ud(t,m);t.wasm._free(h);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([],"int32",g);return[x,b]}var BU={kernelName:tl,backendName:"wasm",setupFunc:bct,kernelFunc:wct};var VU;function Ict(r){VU=r.wasm.cwrap(el,"number",["number","number","number","number","number","number"])}function Cct(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,softNmsSigma:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=VU(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Ud(t,m);t.wasm._free(g);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([d],"float32",h);return[x,b]}var GU={kernelName:el,backendName:"wasm",setupFunc:Ict,kernelFunc:Cct};var vct=!1,WU=ae(Ja,vct,"bool");var UU;function Sct(r){UU=r.wasm.cwrap(Rs,null,["number","number","number","number","number"])}function Nct(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=e.makeOutput([...o.shape,i],s),c=e.dataIdMap.get(l.dataId).id,m=e.dataIdMap.get(o.dataId).id;return UU(m,i,a,u,c),l}var HU={kernelName:Rs,backendName:"wasm",setupFunc:Sct,kernelFunc:Nct};function kct(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(1),n}var qU={kernelName:Li,backendName:"wasm",kernelFunc:kct};function Tct(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return NC({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching 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kC={kernelName:Fs,backendName:"wasm",kernelFunc:Ect,setupFunc:_ct};var Act=!1,XU=ae(Os,Act);var YU;function Dct(r){YU=r.wasm.cwrap(Ps,null,["number","number","number"])}function $ct(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,a=s,u=n,l=u;u.dtype!=="float32"&&(l=On({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),a=e.dataIdMap.get(l.dataId).id);let c=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(c.dataId).id;return YU(a,i,p),u.dtype!=="float32"&&e.disposeData(l.dataId),c}var ZU={kernelName:Ps,backendName:"wasm",setupFunc:Dct,kernelFunc:$ct};var JU;function Rct(r){JU=r.wasm.cwrap(Ms,null,["number","number","number","number"])}function Fct(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=vn(i,o,t),d=p;if(f){let 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|
${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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|
${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=t.dataIdMap.get(n.dataId).id,a=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(s.dataId).id,l=n.shape[0],c=y.sizeFromShape(s.shape),p=t.makeOutput([l,c],n.dtype),m=t.dataIdMap.get(p.dataId).id,f=t.makeOutput([c],s.dtype),d=t.dataIdMap.get(f.dataId).id,h=t.makeOutput([3],"int32"),g=t.dataIdMap.get(h.dataId).id;F4(i,a,u,l,m,d,g);let x=t.readSync(h.dataId),b;switch(x[0]){case 0:{b=S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1],x[2]);break}case 1:{b=S.getSparseReshapeNegativeOutputDimErrorMessage(x[1],x[2]);break}case 2:b=S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let w=Array.from(t.readSync(o.dataId)),I=Array.from(t.readSync(f.dataId));b=S.getSparseReshapeInputOutputMultipleErrorMessage(w,I);break}case 4:{let w=Array.from(t.readSync(o.dataId)),I=Array.from(t.readSync(f.dataId));b=S.getSparseReshapeInputOutputMismatchErrorMessage(w,I);break}default:b=""}if(t.disposeData(h.dataId),b)throw t.disposeData(p.dataId),t.disposeData(f.dataId),new Error(b);return[p,f]}var O4={kernelName:ll,backendName:"wasm",setupFunc:ipt,kernelFunc:apt};var P4;function TC(r){P4=r.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function _C(r,t){let{backend:e,inputs:n}=r,{data:o,indices:s,segmentIds:i}=n,a=s.shape[0],u=e.readSync(i.dataId,a-1,a)[0],c=a>0?u+1:0;if(c<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=o.shape.slice();p[0]=c;let m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=e.dataIdMap.get(i.dataId).id,h=e.makeOutput(p,o.dtype),g=e.dataIdMap.get(h.dataId).id,x=e.makeOutput([4],"int32"),b=e.dataIdMap.get(x.dataId).id;P4(m,kt[o.dtype],o.shape[0],f,d,g,b,t,0);let w=e.readSync(x.dataId),I;switch(w[0]){case 0:{I=S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{I=S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:I=S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(w[1],w[2]);break;case 3:I=S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w[1],w[2],w[3]);break;default:I=""}if(e.disposeData(x.dataId),I)throw e.disposeData(h.dataId),new Error(I);return h}function lpt(r){return _C(r,!0)}var M4={kernelName:iu,backendName:"wasm",setupFunc:TC,kernelFunc:lpt};function upt(r){return _C(r,!1)}var L4={kernelName:au,backendName:"wasm",setupFunc:TC,kernelFunc:upt};var z4;function cpt(r){z4=r.wasm.cwrap(ul,null,["number","number","number","number","number","number","number","number","array","number","number"])}function ppt(r){let{backend:t,inputs:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=e,{outputShape:a}=n,u=t.makeOutput(a,i.dtype);if(y.sizeFromShape(a)===0)return u;let{sliceRank:l,numUpdates:c,sliceSize:p,strides:m,outputSize:f}=S.calculateShapes(s,o,a),d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,x=new Uint8Array(new Int32Array(m).buffer),b=t.dataIdMap.get(u.dataId).id;return z4(d,h,s.shape.length,g,kt[i.dtype],l,c,p,x,f,b),u}var B4={kernelName:ul,backendName:"wasm",setupFunc:cpt,kernelFunc:ppt};function mpt(r){let{inputs:t,attrs:e,backend:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=e,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=new Array(o.shape.length).fill(0),c=o.shape.slice();return u.map(p=>{let m=[...c];m[a]=p;let f=Vo({inputs:{x:o},attrs:{begin:l,size:m},backend:n});return l[a]+=p,f})}var V4={kernelName:Ui,backendName:"wasm",kernelFunc:mpt};var G4=Ct(Zs);var W4=Ct(lu);var fpt=!0,U4=ae(ti,fpt);var H4;function dpt(r){H4=r.wasm.cwrap(yo,null,["number","number","number","number"])}function hpt(r){let{backend:t,inputs:e,attrs:n}=r,{alpha:o}=n,{x:s}=e,i=t.dataIdMap.get(s.dataId).id,a=t.makeOutput(s.shape,s.dtype),u=t.dataIdMap.get(a.dataId).id;return H4(i,o,kt[s.dtype],u),a}var q4={kernelName:yo,backendName:"wasm",setupFunc:dpt,kernelFunc:hpt};var K4;function gpt(r){K4=r.wasm.cwrap(cl,null,["number","array","number","array","array","array","array","array","number","number"])}function xpt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=ze.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=mr({inputs:{x:o},backend:t,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let 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ypt(r){let{backend:t,inputs:e,attrs:n}=r,{data:o,dataSplits:s}=e,{separator:i,nGramWidths:a,leftPad:u,rightPad:l,padWidth:c,preserveShortSequences:p}=n,m=t.readSync(o.dataId),f=t.readSync(s.dataId),[d,h]=ip(m,f,i,a,u,l,c,p),g=t.makeOutput([d.length],"string"),x=t.dataIdMap.get(g.dataId);x.stringBytes=d;let b=t.makeOutput(s.shape,"int32");return t.typedArrayFromHeap(b).set(h),[g,b]}var X4={kernelName:uu,backendName:"wasm",kernelFunc:ypt};function bpt(r){let{backend:t,inputs:e,attrs:n}=r,{input:o,delimiter:s}=e,{skipEmpty:i}=n,a=t.readSync(o.dataId),u=t.readSync(s.dataId),[l,c,p]=ap(a,u[0],i),m=c.length,f=t.makeOutput([m,2],"int32");t.typedArrayFromHeap(f).set(l);let h=t.makeOutput([m],"string"),g=t.dataIdMap.get(h.dataId);g.stringBytes=c;let x=t.makeOutput([2],"int32");return t.typedArrayFromHeap(x).set(p),[f,h,x]}var Y4={kernelName:cu,backendName:"wasm",kernelFunc:bpt};function 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Opt=[lG,uG,cG,pG,mG,dG,bG,IG,CG,vG,SG,NG,kG,TG,_G,AG,$G,FG,MG,zG,VG,GG,WG,UG,qG,KG,XG,ZG,QG,eW,nW,oW,sW,aW,uW,pW,fW,hW,xW,bW,IW,vW,NW,kW,_W,EW,AW,DW,$W,RW,OW,PW,MW,zW,VW,WW,HW,KW,jW,XW,hG,YW,ZW,JW,tU,eU,rU,oU,iU,sU,aU,lU,uU,cU,mU,dU,gU,xU,bU,IU,vU,NU,TU,_U,AU,FU,OU,PU,LU,BU,GU,WU,HU,qU,KU,kC,XU,ZU,QU,t4,e4,r4,n4,o4,OG,i4,l4,c4,m4,d4,g4,x4,y4,w4,C4,S4,N4,T4,_4,E4,LG,$U,A4,D4,R4,O4,M4,L4,B4,V4,G4,W4,U4,q4,j4,X4,Y4,Z4,J4,tH,eH,rH,oH,iH,lH,cH,xG,pH,mH,fH];for(let r of Opt)ic(r);var V1=L();V1.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>{try{return WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11]))}catch(r){return!1}});V1.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(V1.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(r){return!1}});var 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i=this.dataIdNextNumber++;s={id:i},this.dataIdMap.set(s,{id:i,memoryOffset:n,shape:t,dtype:e,refCount:1});let a=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(i,a,n)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:n}){let o=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),i=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(o,s,i);case"int32":return new Int32Array(o,s,i);case"bool":return new Uint8Array(o,s,i);default:throw new Error(`Unknown dtype ${e}`)}}};function Mpt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function CH(r,t,e){if(DC!=null)return DC;let n="tfjs-backend-wasm.wasm";return r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),bg!=null&&bg[n]!=null?bg[n]:e+n}async function SH(){let[r,t]=await Promise.all([L().getAsync("WASM_HAS_SIMD_SUPPORT"),L().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(".worker.js")){let l=vH.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?CH(r,t,yg!=null?yg:u):u+a},Y1&&(o.instantiateWasm=Mpt(CH(r,t,yg!=null?yg:"")));let s=!1;o.onAbort=()=>{if(s||wg)return;wg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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GreaterEqual,Zy as History,Vp as IFFT,xo as Identity,Gp as Imag,Ie as InputSpec,ys as IsFinite,bs as IsInf,ws as IsNan,Wo as KernelBackend,Ss as LRN,Xa as LRNGrad,Dh as LayerVariable,qn as LayersModel,Is as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,Cs as Log,vs as Log1p,x_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,g_ as LogicalXor,rmt as LowerBound,Uu as MathBackendCPU,ju as MathBackendWebGL,nmt as MatrixBandPart,Ns as Max,Ts as MaxPool,Pi as MaxPool3D,nu as MaxPool3DGrad,Wp as MaxPoolGrad,Up as MaxPoolWithArgmax,ks as Maximum,_s as Mean,Es as Min,As as Minimum,Ds as MirrorPad,Ya as Mod,Dc as MomentumOptimizer,Za as Multinomial,$s as Multiply,Mi as Neg,Qa as NonMaxSuppressionV3,tl as NonMaxSuppressionV4,el as NonMaxSuppressionV5,Ja as NotEqual,A0 as OP_SCOPE_SUFFIX,Rs as OneHot,Li as OnesLike,qr as Optimizer,Nh as OptimizerConstructors,zi as Pack,Fs as PadV2,omt as Pool,Os as Pow,Ps as Prelu,Ms as Prod,$c as RMSPropOptimizer,An as RNN,Hp as RaggedGather,qp as RaggedRange,Kp as RaggedTensorToTensor,ou as Range,w0 as Rank,jp as Real,cs as RealDiv,Ls as Reciprocal,Ze as Reduction,zs as Relu,Gs as Relu6,Bi as Reshape,Vs as ResizeBilinear,nl as ResizeBilinearGrad,Bs as ResizeNearestNeighbor,rl as ResizeNearestNeighborGrad,Ws as Reverse,fl as RotateWithOffset,Us as Round,Hs as Rsqrt,Cl as SGDOptimizer,ol as ScatterNd,il as SearchSorted,Vi as Select,qs as Selu,xa as Sequential,Xs as Sigmoid,js as Sign,Ks as Sin,al as Sinh,Gi as Slice,Qs as Softmax,Ys as Softplus,Wi as SpaceToBatchND,su as SparseFillEmptyRows,ll as SparseReshape,iu as SparseSegmentMean,au as SparseSegmentSum,ul as SparseToDense,Ui as SplitV,Zs as Sqrt,lu as Square,ti as SquaredDifference,sc as StaticRegexReplace,yo as Step,cl as StridedSlice,uu as StringNGrams,cu as StringSplit,pu as StringToHashBucketFast,ei as Sub,Js as Sum,rn as SymbolicTensor,ri as Tan,ni as Tanh,Pt as Tensor,le as TensorBuffer,sl as TensorScatterUpdate,so as Tile,pl as TopK,ml as Transform,io as Transpose,mu as Unique,Hi as Unpack,fu as UnsortedSegmentSum,smt as UpperBound,dl as Variable,qi as ZerosLike,Ki as _FusedMatMul,Ee as abs,dx as acos,hx as acosh,Y as add,fE as addN,am as all,dc as any,ta as argMax,gx as argMin,xx as asin,yx as asinh,bx as atan,wx as atan2,Ix as atanh,bu as avgPool,Cx as avgPool3d,mE as backend,S as backend_util,gE as basicLSTMCell,na as batchNorm,vx as batchNorm2d,Sx as batchNorm3d,Nx as batchNorm4d,wu as batchToSpaceND,kx as bincount,yE as bitwiseAnd,c5 as booleanMaskAsync,bE as broadcastArgs,oa as broadcastTo,Ur as broadcast_util,Ay as browser,bt as buffer,T9 as callbacks,Q as cast,Tx as ceil,vr as clipByValue,un as clone,Nn as complex,se as concat,_x as concat1d,Ex as concat2d,Ax as concat3d,Dx as concat4d,nR as constraints,um as conv1d,kn as conv2d,pm as conv2dTranspose,$x as conv3d,Fx as conv3dTranspose,mmt as copyRegisteredKernels,Iu as cos,mm as cosh,Ih as cosineWindow,gc as cumprod,fm as cumsum,mn as customGrad,VF as data,gh as denseBincount,B0 as deprecationWarn,Ox as depthToSpace,sa as depthwiseConv2d,D9 as deregisterOp,xu as device_util,wE as diag,Px as dilation2d,vdt as disableDeprecationWarnings,Tt as dispose,Sdt as disposeVariables,ct as div,Mx as divNoNan,Lx as dot,nN as dropout,CE as einsum,ia as elu,Cdt as enableDebugMode,Idt as enableProdMode,oN as enclosingPowerOfTwo,Vn as engine,vE as ensureShape,L as env,Rr as equal,zx as erf,Bx as euclideanNorm,ir as exp,ar as expandDims,Vx as expm1,xc as eye,Au as fft,vo as fill,Adt as findBackend,Ddt as findBackendFactory,aa as floor,im as floorDiv,Dz as forceHalfFloat,Ru as fused,la as gather,w5 as gatherND,Dy as gather_util,_dt as getBackend,m0 as getGradient,ih as getKernel,Zg as getKernelsForBackend,Wpt as getThreadsCount,f1 as gpgpu_util,f6 as grad,d6 as grads,Fe as greater,pn as greaterEqual,Il as ifft,Cu as imag,dn as image,v5 as inTopKAsync,oR as initializers,BN as input,Mr as io,km as irfft,Gx as isFinite,Wx as isInf,Ux as isNaN,$e as keep,Kr as kernel_impls,LR as layers,vu as leakyRelu,bl as less,Gn as lessEqual,iN as linalg,kE as linspace,TQ as loadGraphModel,_Q as loadGraphModelSync,SR as loadLayersModel,Hx as localResponseNormalization,Nr as log,Su as log1p,jx as logSigmoid,dm as logSoftmax,hm as logSumExp,Or as logicalAnd,Nu as logicalNot,gm as logicalOr,Xx as logicalXor,S8 as losses,TE as lowerBound,Bt as matMul,y2 as math,Sr as max,ku as maxPool,Zx as maxPool3d,_E as maxPoolWithArgmax,Tn as maximum,ke as mean,fh as memory,EE as meshgrid,zR as metrics,xl as min,uo as minimum,Jx as mirrorPad,Qx as mod,T7 as model,BR as models,yc as moments,f5 as movingAverage,$ as mul,AE as multiRNNCell,DE as multinomial,Ut as neg,kh as nextFrame,yl as norm,li as notEqual,ua as oneHot,dr as ones,Ir as onesLike,k as op,$E as outerProduct,fn as pad,RE as pad1d,FE as pad2d,OE as pad3d,PE as pad4d,ty as pool,cn as pow,_u as prelu,fx as print,ey as prod,Ndt as profile,ME as raggedGather,LE as raggedRange,zE as raggedTensorToTensor,BE as rand,aA as randomGamma,Ic as randomNormal,lA as randomStandardNormal,Wn as randomUniform,uA as randomUniformInt,ca as range,Tdt as ready,wl as real,ay as reciprocal,sm as registerBackend,E7 as registerCallbackConstructor,b_ as registerGradient,ic as registerKernel,A9 as registerOp,VR as regularizers,Pr as relu,xm as relu6,Edt as removeBackend,R as reshape,hr as reverse,cA as reverse1d,pA as reverse2d,mA as reverse3d,fA as reverse4d,Du as rfft,ym as round,bm as rsqrt,ft as scalar,h5 as scatterND,$u as scatter_util,yh as searchSorted,wm as selu,Im as separableConv2d,_7 as sequential,J as serialization,bK as setBackend,$dt as setPlatform,Gpt as setThreadsCount,Bpt as setWasmPath,Vpt as setWasmPaths,TT as setWebGLContext,dA as setdiff1dAsync,Nw as shared,tn as sigmoid,ly as sign,v8 as signal,Cm as sin,vm as sinh,Ot as slice,Sm as slice1d,wh as slice2d,Nm as slice3d,Cc as slice4d,ze as slice_util,Eu as softmax,ai as softplus,Tu as spaceToBatchND,N8 as sparse,y5 as sparseToDense,C8 as spectral,gr as split,Ne as sqrt,Wt as square,Tm as squaredDifference,Un as squeeze,qe as stack,No as step,uy as stridedSlice,k8 as string,lt as sub,pt as sum,mc as sumOutType,cy as tan,ra as tanh,sr as tensor,Ke as tensor1d,ui as tensor2d,py as tensor3d,hA as tensor4d,gA as tensor5d,xA as tensor6d,bA as tensorScatterUpdate,Co as tensor_util,iA as test_util,B as tidy,Fr as tile,kdt as time,my as topk,Fc as train,Vt as transpose,Em as truncatedNormal,fy as unique,pmt as unregisterGradient,cmt as unregisterKernel,Am as unsortedSegmentSum,xr as unstack,ur as upcastType,wA as upperBound,y as util,h6 as valueAndGrad,g6 as valueAndGrads,dy as variable,qx as variableGrads,Ypt as version,IF as version_converter,A2 as version_core,TO as version_cpu,tf as version_layers,Upt as version_wasm,Az as version_webgl,aDe as webgl,Td as webgl_util,be as where,gy as whereAsync,Te as zeros,vt as zerosLike};
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