4998 lines
1.2 MiB
4998 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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`;for(let h=2;h<u;h++)d+=`
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`;return m[m.length-1]=" "+m[m.length-1]+"]"+(s?"":d),m}function lh(r){let t=[];for(let e=0;e<r.length;e+=2)t.push([r[e],r[e+1]]);return t}var le=class{constructor(t,e,n){if(this.dtype=e,this.shape=t.slice(),this.size=Qt(t),n!=null){let o=n.length;_(o===this.size,()=>`Length of values '${o}' does not match the size inferred by the shape '${this.size}'.`)}if(e==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||qg(e,this.size),this.strides=ki(t)}set(t,...e){e.length===0&&(e=[0]),_(e.length===this.rank,()=>`The number of provided coordinates (${e.length}) must match the rank (${this.rank})`);let n=this.locToIndex(e);this.values[n]=t}get(...t){t.length===0&&(t=[0]);let e=0;for(let o of t){if(o<0||o>=this.shape[e]){let s=`Requested out of range element at ${t}. 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o=++this.pendingBackendInitId,s=n.then(i=>o<this.pendingBackendInitId?!1:(this.registry[t]=i,this.pendingBackendInit=null,!0)).catch(i=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,Xi(`Initialization of backend ${t} failed`),Xi(i.stack||i.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=n,{success:!0,asyncInit:!1}}catch(n){return Xi(`Initialization of backend ${t} failed`),Xi(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=b0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=s0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let 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Has rank ${n.rank}`);let o={s0:e,s1:n};return T.runKernel(Yl,o)}var cE=k({broadcastArgs_:HK});function qK(r,t){let e=v(r,"broadcastTo","x"),n=e.shape;if(Me(t),t.length<e.rank)throw new Error(`broadcastTo(): shape.length=${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 [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return ln(e);let a={x:e},u={reps:s};return T.runKernel(no,a,u)}var na=k({broadcastTo_:qK});function KK(r){let e={x:v(r,"x","ceil","float32")};return T.runKernel(ts,e)}var Cx=k({ceil_:KK});function Co(r,t,e){Me(r),e=e||jl(t);let n={shape:r,value:t,dtype:e};return T.runKernel(ru,{},n)}function jK(r,t,e){let n=v(r,"x","clipByValue");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return Co(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return T.runKernel(ho,o,s)}var vr=k({clipByValue_:jK});function XK(r){return oe(r,0)}var vx=k({concat1d_:XK});function YK(r,t){return oe(r,t)}var Sx=k({concat2d_:YK});function ZK(r,t){return oe(r,t)}var Nx=k({concat3d_:ZK});function JK(r,t){return oe(r,t)}var kx=k({concat4d_:JK});function QK(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}'`),_(ta(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),_(ta(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(es,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Sn=k({conv2d_:QK});function tj(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}'`),_(ta(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),_(ta(e),()=>"Error in conv1D: Stride should be larger than 0."),_(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=Sn(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var um=k({conv1d_:tj});function ej(r,t,e,n,o,s="NHWC",i){_(r.length===t.rank,()=>`Length of inShape (${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(rs,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var cm=k({conv2DBackpropInput_:ej});function rj(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_:rj});function nj(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 ${o} but only NDHWC is currently supported.`),_(ta(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),_(ta(e),()=>"Error in conv3D: Strides should be larger than 0.");let c={x:u,filter:a},p={strides:e,pad:n,dataFormat:o,dilations:s},m=T.runKernel(ns,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Tx=k({conv3d_:nj});function oj(r,t,e,n,o){_(r.length===t.rank,()=>`Length of inShape (${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 _x=k({conv3DBackpropInput_:oj});function sj(r,t,e,n,o){let s=v(r,"x","conv3dTranspose"),i=v(t,"filter","conv3dTranspose");return _x(e,s,i,n,o)}var Ex=k({conv3dTranspose_:sj});function ij(r){let e={x:v(r,"x","cos","float32")};return T.runKernel(os,e)}var Iu=k({cos_:ij});function aj(r){let e={x:v(r,"x","cosh","float32")};return T.runKernel(ss,e)}var mm=k({cosh_:aj});function lj(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 hc=k({cumprod_:lj});function uj(r,t=0,e=!1,n=!1){let s={x:v(r,"x","cumsum")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(is,s,i)}var fm=k({cumsum_:uj});function cj(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 dh=k({denseBincount_:cj});function pj(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 Ax=k({depthToSpace_:pj});function mj(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(as,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var oa=k({depthwiseConv2d_:mj});function fj(r){let e={x:v(r,"x","diag")};return T.runKernel(Ql,e)}var pE=k({diag_:fj});function dj(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(ls,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Dx=k({dilation2d_:dj});var Ur={};Kt(Ur,{assertAndGetBroadcastShape:()=>Mt,getBroadcastDims:()=>mE,getReductionAxes:()=>ye});function mE(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 hj(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_:hj});function gj(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=na(s,i),u=na(n,i),l=na(o,i),c={condition:a,t:u,e:l};return T.runKernel(Bi,c)}var be=k({where_:gj});function xj(r){let e={x:v(r,"x","zerosLike")};return T.runKernel(Hi,e)}var vt=k({zerosLike_:xj});function yj(r,t){let e=v(r,"a","div"),n=v(t,"b","div");[e,n]=jt(e,n);let o=ut(e,n),s=vt(o),i=Rr(n,s);return be(i,s,o)}var $x=k({divNoNan_:yj});function bj(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 ${s}.`),e.rank===1&&n.rank===1){let i=R(e,[1,-1]),a=R(n,[-1,1]),u=Bt(i,a);return R(u,[])}else if(e.rank===1&&n.rank===2){let i=R(e,[1,-1]),a=R(n,[n.shape[0],n.shape[1]]),u=Bt(i,a);return R(u,[u.size])}else if(e.rank===2&&n.rank===1){let i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var Rx=k({dot_:bj});function wj(r,...t){let e=t.map((o,s)=>v(o,`tensors${s}`,"einsum")),n={equation:r};return T.runKernel(zp,e,n)}var fE=k({einsum_:wj});function Ij(r){let e={x:v(r,"x","elu","float32")};return T.runKernel(cs,e)}var sa=k({elu_:Ij});function Cj(r){let t=v(r,"x","erf");_(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=J(t,"float32"));let e={x:t};return T.runKernel(La,e)}var Fx=k({erf_:Cj});function M0(r,t){for(let e=0;e<r.length;++e)if(r[r.length-e-1]!==t-1-e)return!1;return!0}function dE(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 L0(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 vo(r,t){let e=t.map(n=>1);return dE(r,e,t)}function vj(r,t,e){_(M0(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 Vn=k({lessEqual_:jj});function gE(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 Xj(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(vs,u,l);return a?R(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Bx=k({localResponseNormalization_:Xj});function Yj(r){let e={x:v(r,"x","log","float32")};return T.runKernel(Is,e)}var Nr=k({log_:Yj});function Zj(r){let e={x:v(r,"x","log1p")};return T.runKernel(Cs,e)}var Su=k({log1p_:Zj});function Jj(r){return _(Ni(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)"),Gx(i),i[0]})}}function Qj(r){return _(Ni(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,...])"),Gx(i),i})}}function t6(r){return _(Ni(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 Gx(n),{grad:n[0],value:o}}}function e6(r){return _(Ni(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,...])"),Gx(n.grads),n}}function Vx(r,t){_(Ni(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 pn(r){return T.customGrad(r)}function Gx(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 r6(r){let e={x:v(r,"x","neg")};return T.runKernel(Pi,e)}var Ut=k({neg_:r6});function n6(r){let e={x:v(r,"x","softplus")};return T.runKernel(Xs,e)}var ii=k({softplus_:n6});function o6(r){let t=v(r,"x","logSigmoid");return pn(n=>({value:Ut(ii(Ut(n))),gradFunc:i=>$(i,tn(Ut(n)))}))(t)}var Wx=k({logSigmoid_:o6});function s6(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(ti,o)}var lt=k({sub_:s6});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: ${o}.
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o=v(r,"labels","absoluteDifference"),s=v(t,"predictions","absoluteDifference"),i=null;e!=null&&(i=v(e,"weights","absoluteDifference")),Re(o.shape,s.shape,"Error in absoluteDifference: ");let a=Ee(lt(o,s));return Hr(a,i,n)}var OA=k({absoluteDifference_:G5});function W5(r,t,e,n,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),i=v(t,"predictions","cosineDistance"),a=null;n!=null&&(a=v(n,"weights","cosineDistance")),Re(s.shape,i.shape,"Error in cosineDistance: ");let u=ft(1),l=lt(u,pt($(s,i),e,!0));return Hr(l,a,o)}var PA=k({cosineDistance_:W5});function U5(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 MA=k({hingeLoss_:U5});function H5(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=ao(l,u),p=lt(l,c),m=Y($(ft(.5),Gt(c)),$(u,p));return Hr(m,a,o)}var LA=k({huberLoss_:H5});function q5(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 zA=k({logLoss_:q5});function K5(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 BA=k({meanSquaredError_:K5});function j5(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=$(n,e),i=Su(sr(Ut(Ee(n))));return Y(lt(o,s),i)}function X5(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),i=v(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=v(e,"weights","sigmoidCrossEntropy")),Re(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=ft(n),c=ft(1),p=ft(.5);s=Y($(s,lt(c,l)),$(p,l))}let u=j5(s,i);return Hr(u,a,o)}var VA=k({sigmoidCrossEntropy_:X5});function Y5(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. Labels / logits was rank ${t.rank} and dim was ${e}`);return pn((o,s,i)=>{let u=hm(s,[e],!0),l=lt(J(s,"float32"),u);i([o,l]);let c=Ut($(l,o));return{value:pt(c,[e]),gradFunc:(f,d)=>{let[h,g]=d,x=vo(f.shape,[e]);return[$(R(f,x),lt(J(h,"float32"),sr(g))),$(R(f,x),lt(sr(g),J(h,"float32")))]}}})(r,t)}function Z5(r,t,e,n=0,o=Ze.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),i=v(t,"logits","softmaxCrossEntropy"),a=null;if(e!=null&&(a=v(e,"weights","softmaxCrossEntropy")),Re(s.shape,i.shape,"Error in softmaxCrossEntropy: "),n>0){let l=ft(n),c=ft(1),p=ft(s.shape[1]);s=Y($(s,lt(c,l)),ut(l,p))}let u=Y5(s,i);return Hr(u,a,o)}var GA=k({softmaxCrossEntropy_:Z5});function J5(r,t,e,n){let o=v(r,"indices","sparseFillEmptyRows","int32"),s=v(t,"values","sparseFillEmptyRows"),i=v(e,"denseShape","sparseFillEmptyRows","int32"),a=v(n,"defaultValue","sparseFillEmptyRows",s.dtype);if(o.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
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${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 ${a.shape}`);let u={indices:o,values:s,denseShape:i,defaultValue:a},l=T.runKernel(su,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var WA=k({sparseFillEmptyRows_:J5});function Q5(r,t,e){let n=v(r,"inputIndices","sparseReshape","int32"),o=v(t,"inputShape","sparseReshape","int32"),s=v(e,"newShape","sparseReshape","int32");if(n.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${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 UA=k({sparseReshape_:Q5});function t8(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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|
${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(iu,i)}var HA=k({sparseSegmentMean_:t8});function e8(r,t,e){let n=v(r,"data","sparseSegmentSum"),o=v(t,"indices","sparseSegmentSum","int32"),s=v(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${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 qA=k({sparseSegmentSum_:e8});function r8(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 KA=k({stringNGrams_:r8});function n8(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 ${o.shape}`);let s={skipEmpty:e},i={input:n,delimiter:o},a=T.runKernel(cu,i,s);return{indices:a[0],values:a[1],shape:a[2]}}var jA=k({stringSplit_:n8});function o8(r,t){let e=v(r,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let o={input:e};return T.runKernel(pu,o,n)}var XA=k({stringToHashBucketFast_:o8});function s8(r,t,e,n=!0){let o=v(r,"input","staticRegexReplace","string"),s={pattern:t,rewrite:e,replaceGlobal:n};return T.runKernel(oc,{x:o},s)}var YA=k({staticRegexReplace_:s8});var i8={fft:Au,ifft:Il,rfft:Du,irfft:km},a8={hammingWindow:hA,hannWindow:dy,frame:hy,stft:gA},lo={flipLeftRight:yA,grayscaleToRGB:bA,resizeNearestNeighbor:wy,resizeBilinear:by,rotateWithOffset:wA,cropAndResize:xA,nonMaxSuppression:IA,nonMaxSuppressionAsync:SA,nonMaxSuppressionWithScore:NA,nonMaxSuppressionWithScoreAsync:kA,nonMaxSuppressionPadded:TA,nonMaxSuppressionPaddedAsync:_A,threshold:EA,transform:AA},Q0={bandPart:DA,gramSchmidt:$A,qr:FA},l8={absoluteDifference:OA,computeWeightedLoss:Hr,cosineDistance:PA,hingeLoss:MA,huberLoss:LA,logLoss:zA,meanSquaredError:BA,sigmoidCrossEntropy:VA,softmaxCrossEntropy:GA},u8={sparseFillEmptyRows:WA,sparseReshape:UA,sparseSegmentMean:HA,sparseSegmentSum:qA},c8={stringNGrams:KA,stringSplit:jA,stringToHashBucketFast:XA,staticRegexReplace:YA};var Q={};Kt(Q,{Serializable:()=>wh,SerializationMap:()=>pa,registerClass:()=>tN});var wh=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},pa=class{constructor(){this.classNameMap={}}static getMap(){return pa.instance==null&&(pa.instance=new pa),pa.instance}static register(t){pa.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function tN(r){_(r.className!=null,()=>"Class being registered does not have the static className property defined."),_(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),_(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),pa.register(r)}var qr=class extends wh{minimize(t,e=!1,n){let{value:o,grads:s}=this.computeGradients(t,n);if(n!=null){let i=n.map(a=>({name:a.name,tensor:s[a.name]}));this.applyGradients(i)}else this.applyGradients(s);return Tt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Vx(t,e)}dispose(){this.iterations_!=null&&Tt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ft(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(qr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var kc=class extends qr{static get className(){return"Adadelta"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let 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setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Tc=class extends qr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Co(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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indices.shape[0] = ${r}`}function wY(r,t){return`indices(${r}, 0) is invalid: ${t} < 0`}function IY(r,t,e){return`indices(${r}, 0) is invalid: ${t} >= ${e}`}function CY(r,t){return`only one output dimension may be -1, not both ${r} and ${t}`}function vY(r,t){return`size ${r} must be non-negative, not ${t}`}function SY(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function NY(r,t){let e=Qt(r),n=Qt(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 kY(r,t){let e=Qt(r),n=Qt(t);return`Input to reshape is a tensor with ${e} dense values, but the requested shape has ${n}. inputShape=${r} outputShape=${t}`}function TY(){return"segment ids must be >= 0"}function _Y(){return"segment ids are not increasing"}function EY(r,t){return`Segment id ${r} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function AY(r,t,e){return`Bad: indices[${r}] == ${t} out of range [0, ${e})`}var mN={};Kt(mN,{collectGatherOpShapeInfo:()=>RY,computeOutShape:()=>$Y,segOpComputeOptimalWindowSize:()=>DY});function DY(r,t){let e=!1,n;for(r<=ky?(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 $Y(r,t,e){let n=[],o=r.length;for(let s=0;s<o;s++)s!==t?n.push(r[s]):n.push(e);return n}function RY(r,t,e,n){let o=t.shape.length,s=r.shape.length;if(n!==0&&(n<-o||n>o))throw new Error(`Expect batchDims in the range of [-${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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St("validationData including sample weights is not supported yet."):new z(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${n.validationData} is invalid.`);let F=!0,P=await this.standardizeUserData(u,l,null,null,F,f);c=P[0],p=P[1],x=c.concat(p)}else if(n.validationSplit!=null&&n.validationSplit>0&&n.validationSplit<1){g=!0;let F=Math.floor(o[0].shape[0]*(1-n.validationSplit)),P=o[0].shape[0];c=ef(o,F,P),i=o,o=ef(o,0,F),p=ef(s,F,P),a=s,s=ef(s,0,F),x=c.concat(p)}else n.validationSteps!=null&&(g=!0);let b=o.concat(s).concat(m);this.checkTrainableWeightsConsistency();let w=this.makeTrainFunction(),I=this.getDedupedMetricsNames(),N,E;g?(this.makeTestFunction(),N=this.testFunction,E=I.slice().concat(I.map(F=>"val_"+F))):(N=null,x=[],E=I.slice());let A=Ky(n.callbacks,n.yieldEvery);return await 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t={};return t.className=r,t.config={},FN(t)}else return r instanceof nn?r:FN(r)}function ON(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var wb=class extends Q.Serializable{},Lu=class extends wb{constructor(t){super(),ON(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=Te([1]);return this.hasL1&&(e=Y(e,pt($(this.l1,Ee(t))))),this.hasL2&&(e=Y(e,pt($(this.l2,Oc(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";Q.registerClass(Lu);function gR(r){return ON(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function xR(r){return ON(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var dR={l1l2:"L1L2"};function me(r){return Rm(r)}function hR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function 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Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=he(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ce(t.alphaRegularizer),this.alphaConstraint=Ve(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Wt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o<t.length;++o)n[o]=t[o];this.inputSpec=[new Ie({ndim:t.length,axes:n})],this.built=!0}call(t,e){return t=Nt(t),_u(t,this.alpha.read())}getConfig(){let t={alphaInitializer:_e(this.alphaInitializer),alphaRegularizer:me(this.alphaRegularizer),alphaConstraint:Be(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}};sf.className="PReLU";Q.registerClass(sf);var af=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA=1,t==null&&(t={}),t.alpha!=null&&t.alpha!==this.DEFAULT_ALPHA)throw new St(`Non-default alpha value (${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=Nt(t);return sa(n)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};af.className="ELU";Q.registerClass(af);var lf=class extends Et{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=Nt(t);return $(n,J(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";Q.registerClass(lf);var uf=class extends Et{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=Nt(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";Q.registerClass(uf);function zu(r,t,e){if(typeof r=="number")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${r.length} elements.`);for(let n=0;n<t;++n){let o=r[n];if(!E$(o))throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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instead`);if(s==="channelsFirst"&&(r=Vt(r,[0,2,1])),o==="causal")throw new St("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=um(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=gn(a,e)),a})}function yR(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=hn()),Oe(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Lh(r,s);if(o==="causal")throw new St("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function h7(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=hn()),Oe(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=PN(r,s);if(o==="causal")throw new St("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Tx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=gn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var qc=class extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",qc.verifyArgs(e),this.rank=t,Qe(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new St(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,fn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Oe(this.dataFormat),this.activation=di(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=he(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ve(e.biasConstraint),this.biasRegularizer=Ce(e.biasRegularizer),this.activityRegularizer=Ce(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else 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(uo("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Ey(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:fi(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 qc{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=Wt(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=Nt(t);let n,o=this.bias==null?null:this.bias.read(),s=Ay(this.activation.getClassName());if(s!=null&&this.rank===2)n=yR(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=yR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=h7(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new St("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Wt(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=Tn(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"&&!Ey(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";Q.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";Q.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 ${this.padding}`)}build(t){if(t=Wt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=hi(u,m,c,this.padding),h=hi(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=gn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Wt(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]=hi(e[o],u,i,this.padding),e[s]=hi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};cf.className="Conv2DTranspose";Q.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 ${this.padding}`)}build(t){if(t=Wt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ie({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Nt(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=hi(l,h,m,this.padding),w=hi(c,g,f,this.padding),I=hi(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=Ex(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=gn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Wt(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]=hi(e[o],c,a,this.padding),e[s]=hi(e[s],p,u,this.padding),e[i]=hi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};pf.className="Conv3DTranspose";Q.registerClass(pf);var Ib=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 ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ce(e.depthwiseRegularizer),this.depthwiseConstraint=Ve(e.depthwiseConstraint),this.pointwiseInitializer=he(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ce(e.pointwiseRegularizer),this.pointwiseConstraint=Ve(e.pointwiseConstraint)}build(t){if(t=Wt(t),t.length<this.rank+2)throw new z(`Inputs to SeparableConv${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=Nt(t);let n;if(this.rank===1)throw new St("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=gn(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}};Ib.className="SeparableConv";var mf=class extends Ib{constructor(t){super(2,t)}};mf.className="SeparableConv2D";Q.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"&&!Ey(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};Vu.className="Conv1D";Q.registerClass(Vu);var ff=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=Nt(t),this.dataFormat==="channelsLast"){let n=_h(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return _h(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=_h(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return _h(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};ff.className="Cropping2D";Q.registerClass(ff);var df=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Oe(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,T$(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=Nt(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"?lo.resizeNearestNeighbor(n,[s,i]):lo.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"?lo.resizeNearestNeighbor(n,[s,i]):lo.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";Q.registerClass(df);function g7(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=hn()),Oe(o);let i=Lh(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=oa(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var hf=class extends qc{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=Wt(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=Nt(t);let n=g7(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=gn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Wt(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=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(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";Q.registerClass(hf);function MN(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 LN(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 ${u}D.`);let l=[1,0].concat(dn(2,u));if(t=Vt(t,l),s!=null)throw new St("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=ir(o,-1)),o=Vt(o,l)),n&&(t=hr(t,0),o!=null&&(o=hr(o,0)));let c=[],p,m=e,f=t.shape[0],d=xr(t),h;o!=null&&(h=xr(o));for(let x=0;x<f;++x){let b=d[x],w=B(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=lt(Ir(N),N),A=Y($(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 _n=class extends Et{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 Xc({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 dn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){zy(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 St("Constants support is not implemented in RNN yet.");zy(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 kn("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=MN(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=Nt(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=LN((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?Py(e,[1,n]):e):this.cell.stateSize>1?[Py(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()===_n.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=bn(o,n);return new t(Object.assign(e,{cell:s}))}};_n.className="RNN";Q.registerClass(_n);var Dl=class extends Et{},Kc=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=di(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=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Wt(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 ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Rl({ones:()=>Ir(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Rl({ones:()=>Ir(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=gn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=Y(s,Do(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:fi(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)}};Kc.className="SimpleRNNCell";Q.registerClass(Kc);var gf=class extends _n{constructor(t){t.cell=new Kc(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";Q.registerClass(gf);var jc=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=di(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=di(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=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([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=Wt(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=Do(t,this.kernel.read());this.useBias&&(c=gn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,i[0]));let p=this.recurrentKernel.read(),[m,f]=gr(p,[2*this.units,this.units],p.rank-1),d=Do(o,m),[h,g,x]=gr(c,3,c.rank-1),[b,w]=gr(d,2,d.rank-1);a=this.recurrentActivation.apply(Y(h,b)),u=this.recurrentActivation.apply(Y(g,w));let I=Do($(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:fi(this.activation),recurrentActivation:fi(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)}};jc.className="GRUCell";Q.registerClass(jc);var xf=class extends _n{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 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 e.implmentation===0&&(e.implementation=1),new t(e)}};xf.className="GRU";Q.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=di(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=di(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=Fc([1,pi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Fc([1,pi([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=Wt(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 xn{apply(u,l){let c=s.apply([i]),p=new Pu().apply([i]),m=s.apply([i*2]);return vN(vN(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=Do(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,a[0])),m=Y(m,Do(o,this.recurrentKernel.read())),this.useBias&&(m=gn(m,this.bias.read()));let[f,d,h,g]=gr(m,4,m.rank-1);u=this.recurrentActivation.apply(f),l=this.recurrentActivation.apply(d),c=Y($(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:fi(this.activation),recurrentActivation:fi(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)}};$l.className="LSTMCell";Q.registerClass($l);var yf=class extends _n{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 $l(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};yf.className="LSTM";Q.registerClass(yf);var Xc=class extends Dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a<this.cells.length;++a){let u=this.cells[a];n=o[a],a===0?i=[t[0]].concat(n):i=[i[0]].concat(n),i=u.call(i,e),s.push(i.slice(1))}n=[];for(let a of s.slice().reverse())n.push(...a);return[i[0]].concat(n)})}build(t){zy(t)&&(t=t[0]),t=t;let e;this.cells.forEach((n,o)=>{ci(`RNNCell_${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(bn(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 Ah(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)}};Xc.className="StackedRNNCells";Q.registerClass(Xc);function Rl(r){let{ones:t,rate:e,training:n=!1,count:o=1,dropoutFunc:s}=r,i=()=>s!=null?s(t(),e):Ly(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 x7=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 Cb=class extends _n{constructor(t){if(t.unroll)throw new St("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new St("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 kn("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=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Cb.className="ConvRNN2D";var Yc=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",fn(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=Wt(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 xn{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=x7(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=Sn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?gn(s,n,this.dataFormat):s}recurrentConv(t,e){return Sn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Yc.className="ConvLSTM2DCell";Q.registerClass(Yc);var bf=class extends Cb{constructor(t){let e=new Yc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};bf.className="ConvLSTM2D";Q.registerClass(bf);var Zc=class extends Et{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=Nt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(n);return Ou(()=>Ly(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()}};Zc.className="Dropout";Q.registerClass(Zc);var wf=class extends Zc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};wf.className="SpatialDropout1D";Q.registerClass(wf);var If=class extends Et{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=di(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=Wt(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=Wt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Nt(t),o=Ay(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=gn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:fi(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";Q.registerClass(If);var Cf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Wt(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)}). 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n=Nt(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";Q.registerClass(Nf);var kf=class extends Et{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=dn(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=Wt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return 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Et{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=Nt(t);return Ou(()=>Y(Pm(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Pf.className="GaussianNoise";Q.registerClass(Pf);var Mf=class extends Et{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=Nt(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";Q.registerClass(Mf);var Lf=class extends Et{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=Nt(t),s=o.shape,i=s.length,a=dn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,dn(0,i).slice(0,i-1)),m=()=>{if(p){let 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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";Q.registerClass(zf);var Bf=class extends Et{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=Wt(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!==Eo(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=Nt(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=xc(n,this.axis,!0),l=To(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)),Bh(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";Q.registerClass(Bf);function C7(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=hn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Wt(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(()=>C7(Nt(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";Q.registerClass(Vf);function _b(r,t,e,n,o,s){return B(()=>{Oe(o),bN(s),fn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=hn()),s==null&&(s="max"),r=Lh(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 bR(r,t,e,n,o,s){return B(()=>{Oe(o),bN(s),fn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=hn()),s==null&&(s="max"),r=PN(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=qx(r,t,e,a):i=xx(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var vb=class extends Et{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,fn(this.padding),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){t=Wt(t);let e=Tn(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(Nt(t),2);let n=this.poolingFunction(Nt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Gn(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 vb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,"max")}};Gf.className="MaxPooling1D";Q.registerClass(Gf);var Wf=class extends vb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,"avg")}};Wf.className="AveragePooling1D";Q.registerClass(Wf);var Sb=class extends Et{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),fn(this.padding),this.inputSpec=[new Ie({ndim:4})]}computeOutputShape(t){t=Wt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(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(Nt(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 Sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,"max")}};Uf.className="MaxPooling2D";Q.registerClass(Uf);var Hf=class extends Sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),_b(t,e,n,o,s,"avg")}};Hf.className="AveragePooling2D";Q.registerClass(Hf);var Nb=class extends Et{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),fn(this.padding),this.inputSpec=[new Ie({ndim:5})]}computeOutputShape(t){t=Wt(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=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(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(Nt(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 Nb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),bR(t,e,n,o,s,"max")}};qf.className="MaxPooling3D";Q.registerClass(qf);var Kf=class extends Nb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Oe(s),fn(o),bR(t,e,n,o,s,"avg")}};Kf.className="AveragePooling3D";Q.registerClass(Kf);var kb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ie({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new St}},jf=class extends kb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Nt(t);return ke(n,1)})}};jf.className="GlobalAveragePooling1D";Q.registerClass(jf);var Xf=class extends kb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Nt(t);return Sr(n,1)})}};Xf.className="GlobalMaxPooling1D";Q.registerClass(Xf);var Tb=class extends Et{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 St}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Yf=class extends Tb{call(t,e){return B(()=>{let n=Nt(t);return this.dataFormat==="channelsLast"?ke(n,[1,2]):ke(n,[2,3])})}};Yf.className="GlobalAveragePooling2D";Q.registerClass(Yf);var Zf=class extends Tb{call(t,e){return B(()=>{let n=Nt(t);return this.dataFormat==="channelsLast"?Sr(n,[1,2]):Sr(n,[2,3])})}};Zf.className="GlobalMaxPooling2D";Q.registerClass(Zf);var Eb=class extends Et{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=bn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},Jf=class extends Eb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Wt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Wt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=Nt(t),LN((i,a)=>[Nt(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};Jf.className="TimeDistributed";Q.registerClass(Jf);function v7(r){da(N$,"BidirectionalMergeMode",r)}var S7="concat",Qf=class extends Eb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=bn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=bn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?S7:t.mergeMode,v7(this.mergeMode),t.weights)throw new St("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=MN(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 St("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){ci(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),ci(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=bn(e.layer);if(delete e.layer,e.numConstants!=null)throw new St("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";Q.registerClass(Qf);var td=class extends Et{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 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OR=(r,t,e,n=se)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[n.add(C("a",r,t,e),C("b",r,t,e))];case"AddN":return[n.addN(C("tensors",r,t,e))];case"FloorMod":case"Mod":return[n.mod(C("a",r,t,e),C("b",r,t,e))];case"Mul":return[n.mul(C("a",r,t,e),C("b",r,t,e))];case"RealDiv":case"Div":return[n.div(C("a",r,t,e),C("b",r,t,e))];case"DivNoNan":return[n.divNoNan(C("a",r,t,e),C("b",r,t,e))];case"FloorDiv":return[n.floorDiv(C("a",r,t,e),C("b",r,t,e))];case"Sub":return[n.sub(C("a",r,t,e),C("b",r,t,e))];case"Minimum":return[n.minimum(C("a",r,t,e),C("b",r,t,e))];case"Maximum":return[n.maximum(C("a",r,t,e),C("b",r,t,e))];case"Pow":return[n.pow(C("a",r,t,e),C("b",r,t,e))];case"SquaredDifference":return[n.squaredDifference(C("a",r,t,e),C("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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TypeError(`Node type ${r.op} is not implemented`)}};function Hn(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 MR(r){return!(typeof r=="number"||r.some(t=>t<0))}function od(r,t,e){let n=qb(r,e),o=!MR(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=qb(s.shape,n)}),!MR(n))throw new Error(`Non-fully-defined elementShape: ${n}`);return n}function qb(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. ${t}`);e[n]=o>=0?o:s}return e}var Kb=class{constructor(t,e,n,o,s,i,a){this.name=t,this.dtype=e,this.maxSize=n,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=i,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=ft(0),$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),Hn(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 or([],[0].concat(this.elementShape));let n=this.readMany(t);return Hn(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 ${t}`);if(this.size()===0)return or([],[0].concat(this.elementShape));let e=[];for(let o=0;o<this.size();o++)e.push(o);let n=this.readMany(e);return Hn(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),oe(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 ${s.dtype}`);Hn(e,s.shape,"TensorList shape mismatch: "),$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.`);Hn(t,this.elementShape,"TensorList shape mismatch: ");let o=od(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=od(this.elementShape,this.tensors,t),o=this.tensors.pop();return o.kept=!1,Hn(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 ${this.elementDtype}`);if(Hn(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");$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=qb(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 GR=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("tensorArrayId",r,t,e),o=C("tensor",r,t,e),s=C("lengths",r,t,e),i=e.getTensorArray(n.id);return i.split(s,o),[i.idTensor]}case"TensorArraySizeV3":{let n=C("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return[ft(o.size(),"int32")]}case"TensorArrayCloseV3":{let n=C("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return o.clearAndClose(),[o.idTensor]}case"TensorListSetItem":{let n=C("tensorListId",r,t,e),o=C("index",r,t,e),s=C("tensor",r,t,e),i=e.getTensorList(n.id);return i.setItem(o,s),[i.idTensor]}case"TensorListGetItem":{let n=C("tensorListId",r,t,e),o=C("index",r,t,e),s=C("elementShape",r,t,e),i=C("elementDType",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=C("indices",r,t,e),o=C("tensor",r,t,e),s=C("elementShape",r,t,e),i=C("numElements",r,t,e),a=BR(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let 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=LR(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=VR(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 WR(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=Vh(r,t,e),i=C("dataFormat",r,t,e).toUpperCase(),a=C("dilations",r,t,e);return[n.conv2d(C("x",r,t,e),C("filter",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=WR(r,t,e);return[n.fused.conv2d({x:C("x",r,t,e),filter:C("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=WR(r,t,e);return[n.fused.depthwiseConv2d({x:C("x",r,t,e),filter:C("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let o=C("outputShape",r,t,e),s=C("strides",r,t,e),i=Vh(r,t,e);return[n.conv2dTranspose(C("x",r,t,e),C("filter",r,t,e),o,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let o=C("strides",r,t,e),s=Vh(r,t,e),i=C("dilations",r,t,e),a=C("dataFormat",r,t,e).toUpperCase();return[n.depthwiseConv2d(C("input",r,t,e),C("filter",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case"Conv3D":{let o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("dataFormat",r,t,e).toUpperCase(),a=C("dilations",r,t,e);return[n.conv3d(C("x",r,t,e),C("filter",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case"AvgPool":{let o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("kernelSize",r,t,e);return[n.avgPool(C("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPool":{let o=C("strides",r,t,e),s=C("pad",r,t,e),i=C("kernelSize",r,t,e);return[n.maxPool(C("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPoolWithArgmax":{let 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 HR=(r,t,e,n=se)=>{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"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 o=C("shape",r,t,e),s=C("mean",r,t,e),i=C("stdDev",r,t,e),a=C("seed",r,t,e);return[n.truncatedNormal(o,s,i,C("dtype",r,t,e),a)]}case"Zeros":return[n.zeros(C("shape",r,t,e),C("dtype",r,t,e))];case"ZerosLike":return[n.zerosLike(C("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function ck(r,t,e){let n=C("boxes",r,t,e),o=C("scores",r,t,e),s=C("maxOutputSize",r,t,e),i=C("iouThreshold",r,t,e),a=C("scoreThreshold",r,t,e),u=C("softNmsSigma",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var qR=async(r,t,e,n,o=se)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=ck(r,t,e),p=await o.image.nonMaxSuppressionWithScoreAsync(s,i,a,u,l,c);return[p.selectedIndices,p.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=ck(r,t,e),c=C("padToMaxOutputSize",r,t,e),p=await o.image.nonMaxSuppressionPaddedAsync(s,i,a,u,l,c);return[p.selectedIndices,p.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=ck(r,t,e);return[await o.image.nonMaxSuppressionAsync(s,i,a,u,l)]}case"Where":{let s=o.cast(C("condition",r,t,e),"bool"),i=[await o.whereAsync(s)];return s.dispose(),i}case"ListDiff":return o.setdiff1dAsync(C("x",r,t,e),C("y",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KR=(r,t,e,n=se)=>{switch(r.op){case"LowerBound":{let o=C("sortedSequence",r,t,e),s=C("values",r,t,e);return[n.lowerBound(o,s)]}case"TopKV2":{let o=C("x",r,t,e),s=C("k",r,t,e),i=C("sorted",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case"UpperBound":{let o=C("sortedSequence",r,t,e),s=C("values",r,t,e);return[n.upperBound(o,s)]}case"Unique":{let o=C("x",r,t,e),s=n.unique(o);return[s.values,s.indices]}case"UniqueV2":{let o=C("x",r,t,e),s=C("axis",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jR=(r,t,e,n=se)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let o=C("default",r,t,e);return[cr(r.name,t,e)||o];case"Placeholder":return[cr(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=C("x",r,t,e);return[xi(c)]}case"IdentityN":return C("x",r,t,e).map(c=>xi(c));case"Snapshot":let s=C("x",r,t,e);return[xi(s)];case"Shape":return[n.tensor1d(C("x",r,t,e).shape,"int32")];case"ShapeN":return C("x",r,t,e).map(c=>n.tensor1d(c.shape));case"Size":return[n.scalar(C("x",r,t,e).size,"int32")];case"Rank":return[n.scalar(C("x",r,t,e).rank,"int32")];case"NoOp":return[n.scalar(1)];case"Print":let i=C("x",r,t,e),a=C("data",r,t,e),u=C("message",r,t,e),l=C("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(u);for(let c=0;c<a.length;c++)console.log(Array.prototype.slice.call(a[c].dataSync()).slice(0,l));return[i];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jb=class{get id(){return this.handle.id}constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=ft(0),this.tensorMap=new Map,$e(this.handle)}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ft(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=xr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a<s;a++){let u=n[a],l=o[a];$e(l),this.tensorMap.set(u,l)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return 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 XR=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 jb(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 YR=(r,t,e,n=se)=>{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 ZR=(r,t,e,n=se)=>{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))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var JR=(r,t,e,n=se)=>{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 QR=(r,t,e,n=se)=>{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))];case"SparseToDense":return[n.sparseToDense(C("sparseIndices",r,t,e),C("outputShape",r,t,e),C("sparseValues",r,t,e),C("defaultValue",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tF=(r,t,e,n=se)=>{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 o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(C("starts",r,t,e),C("limits",r,t,e),C("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(C("shape",r,t,e),C("values",r,t,e),C("defaultValue",r,t,e),C("rowPartitionTensors",r,t,e),C("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eF=(r,t,e,n=se)=>{switch(r.op){case"Max":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.max(C("x",r,t,e),a,u)]}case"Mean":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.mean(C("x",r,t,e),a,u)]}case"Min":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.min(C("x",r,t,e),a,u)]}case"Sum":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.sum(C("x",r,t,e),a,u)]}case"All":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.all(C("x",r,t,e),a,u)]}case"Any":{let a=C("axis",r,t,e),u=C("keepDims",r,t,e);return[n.any(C("x",r,t,e),a,u)]}case"ArgMax":{let 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 rF=(r,t,e,n=se)=>{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 does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=C("axis",r,t,e),s=C("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=C("reps",r,t,e);return[n.tile(C("x",r,t,e),o)]}case"Split":case"SplitV":{let o=C("axis",r,t,e),s=C("numOrSizeSplits",r,t,e),i=C("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=C("indices",r,t,e),s=C("values",r,t,e),i=C("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=C("x",r,t,e),s=C("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=C("sparseIndices",r,t,e),s=C("outputShape",r,t,e),i=C("sparseValues",r,t,e),a=C("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=C("indices",r,t,e),s=C("values",r,t,e),i=C("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nF=(r,t,e,n=se)=>{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 oF=(r,t,e,n=se)=>{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 sF=(r,t,e,n=se)=>{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 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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 pk(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>OR(i,a,u));case"basic_math":return o(()=>PR(i,a,u));case"control":return GR(i,a,u);case"convolution":return o(()=>UR(i,a,u));case"creation":return o(()=>HR(i,a,u));case"dynamic":return qR(i,a,u);case"evaluation":return o(()=>KR(i,a,u));case"image":return o(()=>YR(i,a,u));case"graph":return o(()=>jR(i,a,u));case"logical":return o(()=>ZR(i,a,u));case"matrices":return o(()=>JR(i,a,u));case"normalization":return o(()=>QR(i,a,u));case"ragged":return o(()=>tF(i,a,u));case"reduction":return o(()=>eF(i,a,u));case"slice_join":return o(()=>rF(i,a,u));case"sparse":return o(()=>nF(i,a,u));case"spectral":return o(()=>oF(i,a,u));case"string":return o(()=>sF(i,a,u));case"transformation":return o(()=>iF(i,a,u));case"hash_table":return XR(i,a,u,n);case"custom":let l=Rb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Hb(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 Wh=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 mk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>wn(m)[0]));n=n||[];let c=new Set(n.map(m=>wn(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Jc(m)||o9(m)||s9(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 aF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>wn(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=QQ(d,u);return t9(h,u),h}function QQ(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 sd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function t9(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 sd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new sd(`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 sd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new sd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function lF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Jc(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;a<r.length;++a){let u=s[a];if(u===e)continue;let l=r[a],c=r[u];i.has(c.name)||i.set(c.name,[]),i.get(c.name).push(l.name)}return i}var e9=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),r9=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),n9=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Jc(r){return e9.has(r.op)}function o9(r){return r9.has(r.op)}function s9(r){return n9.has(r.op)}var Qc=class{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Qc(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=mk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${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=aF(this.graph,n),u=lF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return $e(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[wn(m)[0]]),s=e.map(m=>wn(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Wh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=wn(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=pk(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${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=>cr(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(!(Jc(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(Jc(u))continue;let l=WN(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){if(!(Jc(t)||s.has(t.name))&&i!=null)for(let a of i){let u=WN(a,e,n);for(let l of u)!l||l.kept||o.has(l.id)||l.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 Wh(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=>cr(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[wn(I)[0]]),a=n.map(I=>wn(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}=mk(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]=wn(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=>!Jc(I)&&!cr(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}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,o,n)&&([m]=gi(p.node.name,n)),o[p.node.name]==null){let f=pk(p.node,o,n,this._resourceManager);m||([m]=gi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=gi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=wn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=wn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=wn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var Xb=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var i9="?tfjs-format=file",a9="model.json",Uh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new Xb}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${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}.${e.versions.minConsumer}`;let o=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Qc(Gh.Instance.transformGraph(e,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Gh.Instance.transformGraph(t.modelInitializer);this.initializer=new Qc(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o<n.length;o++){let 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r=="object"&&r instanceof Pt||y.isTypedArray(r)}function p9(r){return r===null||typeof r!="object"&&typeof r!="function"}function dF(r){return cF(r,m9)}function m9(r){return r instanceof Pt?{value:r.clone(),recurse:!1}:Gu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var id=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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id{constructor(){super(tp.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;o<n;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};tp.INITIAL_CAPACITY=32;function Tk(r){return new hk(r)}function Hh(r){return new gk(r)}function gF(r,t){return new Qb(r,t)}function xF(r,t=Pl.FAIL){return new Nk(r,t)}var tr=class{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],n=await t.next();for(;!n.done;)e.push(n.value),n=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),n=t(e.value);for(;!e.done&&n;)e=await 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this.upstream.next()}},bk=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()}},wk=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Ik=class extends tr{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},Ck=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},vk=class extends tr{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Jb=class extends tr{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},ep=class extends tr{constructor(){super(),this.outputQueue=new tp,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Sk=class extends ep{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},Qb=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 Nk=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 Zb(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 ${this.count}.`);case Pl.SHORTEST:return{value:null,done:!0};case Pl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},tw=class extends tr{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new id(e)}summary(){return`${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()}},kk=class extends tw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=hF.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 yi=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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${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),En(async()=>(await n.iterator()).columnMajorBatch(t,e,f9),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,En(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,En(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return En(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return En(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return En(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,En(async()=>{let o=Hh(async()=>({value:await e.iterator(),done:!1}));return gF(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?n=0:n=null,En(async()=>(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=yF.alea(e||y.now().toString());return En(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,En(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()}};yi.MAX_BUFFER_SIZE=1e4;function En(r,t=null){return new class extends yi{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function bF(r){return En(async()=>Tk(r),r.length)}function wF(r){if(!Gu(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 En(async()=>{let e=await Zb(r,n=>{if(n instanceof yi)return{value:n.iterator(),recurse:!1};if(Gu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return xF(e,Pl.SHORTEST)},t)}function f9(r){if(r===null)return null;let t=r[0];return fF(t)?{value:d9(r),recurse:!1}:{value:null,recurse:!0}}function d9(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Pt?qe(r):or(r)}var ad=class extends yi{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 ew='"',qh=Symbol("out"),IF=Symbol("field"),rw=Symbol("quote"),_k=Symbol("quoteafterquote"),CF=Symbol("quoteinquote"),ld=class extends yi{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 ad(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=qh;for(let a=0;a<s;a++)switch(i){case qh:switch(t.charAt(a)){case ew:o=a+1,i=rw;break;case this.delimiter:if(o=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),i=qh;break;default:i=IF,o=a;break}break;case IF:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a)),i=qh,o=a+1;break;default:}break;case rw:switch(t.charAt(a)){case ew:i=_k;break;default:}break;case _k:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a-1)),i=qh,o=a+1;break;case ew:i=rw;break;default:i=CF;break}break;case CF:switch(t.charAt(a)){case ew:i=rw;break;default:}break;default:}if(i===_k?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 ud=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 ud(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),or(n,e)}};var cd=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=li([i,s,u,a],[1,4])}else this.cropBox=li([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 cd(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=Sy.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: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=ir(J(t,"float32"),0),n;n=lo.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var pd=class{};var Kh=class extends tr{split(t){return new Ek(this,t)}},Ek=class extends Kh{constructor(t,e){super(),this.upstream=t,this.impl=new Ak(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Ak=class extends ep{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${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 nw=class extends tr{decodeUTF8(){return new Dk(this)}},Dk=class extends Kh{constructor(t){super(),this.upstream=t,this.impl=new $k(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$k=class extends ep{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=fk();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 md=class extends nw{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function vF(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=h9(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new md(i,t)}else throw new Error(s.statusText)}var h9=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function ow(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var fd=class extends pd{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(ow(this.input)&&L().get("IS_NODE")){let t=sw();this.input=t.readFileSync(this.input.slice(7))}return new md(this.input,this.options)}};var dd=class extends pd{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return ow(this.url)?new fd(this.url,this.fileOptions).iterator():vF(this.url,this.fileOptions)}};function SF(r,t={}){return new ld(new dd(r),t)}function NF(r){let t=Hh(r);return En(async()=>t)}function kF(r){return En(async()=>{let t=await r();return Hh(()=>t.next())})}async function TF(r,t){return cd.create(r,t)}async function _F(r){return ud.create(r)}var Rk="4.3.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var g9=Kr.whereImpl,Wu=class extends Go{nextDataId(){return Wu.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ta(this,Bn())}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 Bn().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. Due to automatic garbage collection, the true allocated memory may be less."]}}where(t){tt([t],"where");let e=this.readSync(t.dataId);return g9(t.shape,e)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Wu.nextDataId=0;var bw={};Kt(bw,{addImpl:()=>Pk,bincountImpl:()=>xd,bincountReduceImpl:()=>iw,castImpl:()=>Ok,ceilImpl:()=>Mk,concatImpl:()=>rp,equalImpl:()=>Lk,expImpl:()=>Bk,expm1Impl:()=>Gk,floorDivImpl:()=>Uk,floorImpl:()=>Wk,gatherNdImpl:()=>aw,gatherV2Impl:()=>lw,greaterEqualImpl:()=>qk,greaterImpl:()=>Hk,lessEqualImpl:()=>jk,lessImpl:()=>Kk,linSpaceImpl:()=>uw,logImpl:()=>Xk,maxImpl:()=>cw,maximumImpl:()=>Yk,minimumImpl:()=>Zk,multiplyImpl:()=>jh,negImpl:()=>Jk,notEqualImpl:()=>Qk,prodImpl:()=>tT,raggedGatherImpl:()=>pw,raggedRangeImpl:()=>mw,raggedTensorToTensorImpl:()=>fw,rangeImpl:()=>op,rsqrtImpl:()=>eT,scatterImpl:()=>bi,sigmoidImpl:()=>sO,simpleAbsImpl:()=>Fk,sliceImpl:()=>sp,sparseFillEmptyRowsImpl:()=>dw,sparseReshapeImpl:()=>hw,sparseSegmentReductionImpl:()=>wd,sqrtImpl:()=>lO,squaredDifferenceImpl:()=>nT,staticRegexReplaceImpl:()=>oT,stridedSliceImpl:()=>gw,stringNGramsImpl:()=>ip,stringSplitImpl:()=>ap,stringToHashBucketFastImpl:()=>lp,subImpl:()=>iT,tileImpl:()=>xw,topKImpl:()=>yw,transposeImpl:()=>yd,uniqueImpl:()=>up});function Fk(r){let t=new Float32Array(r.length);for(let e=0;e<r.length;++e)t[e]=Math.abs(r[e]);return t}var x9=r=>{let{x:t}=r.inputs,e=r.backend;tt(t,"abs");let n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId).values;return n=Fk(o),e.makeOutput(n,t.shape,t.dtype)},AF={kernelName:Ti,backendName:"cpu",kernelFunc:x9};function te(r){return(t,e,n,o,s)=>{let i=S.assertAndGetBroadcastShape(t,e),a=i.length,u=y.computeStrides(i),l=y.sizeFromShape(i),c=y.getTypedArrayFromDType(s,l),p=t.length,m=e.length,f=y.computeStrides(t),d=y.computeStrides(e),h=S.getBroadcastDims(t,i),g=S.getBroadcastDims(e,i);if(h.length+g.length===0)for(let x=0;x<c.length;++x)c[x]=r(n[x%n.length],o[x%o.length]);else for(let x=0;x<c.length;++x){let b=y.indexToLoc(x,a,u),w=b.slice(-p);h.forEach(A=>w[A]=0);let I=y.locToIndex(w,p,f),N=b.slice(-m);g.forEach(A=>N[A]=0);let E=y.locToIndex(N,m,d);c[x]=r(n[I],o[E])}return[c,i]}}function 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 DF={kernelName:Op,backendName:"cpu",kernelFunc:Cr};function hd(r,t,e="float32"){if(e==="complex64"){let o=hd(r,t,"float32"),s=hd(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 $F={kernelName:go,backendName:"cpu",kernelFunc:Yr};function Fo(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 RF={kernelName:jp,backendName:"cpu",kernelFunc:Fo};function 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FF={kernelName:fo,backendName:"cpu",kernelFunc:Oo};function ie(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=tO(t,2)[1],a=tO(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 z9(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 L9(r,t,n,u,i,s),[i,s]}function pw(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(F9(s,i,u),n.length===0)throw new Error("params.rank must be nonzero");let l=n[0],{outSplits:c,valueSlices:p,numValues:m}=P9(s,i,r,l),f=M9(c),d=z9(e,n,o,p,m);return[f,d[0],d[1]]}var eO=2147483647;function mw(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>eO)throw new Error(`Requires ((limit - start) / delta) <= ${eO}`);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 Po=S.RowPartitionType,bd=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]===Po.FIRST_DIM_SIZE?this.rowPartitionTypes[t+1]:this.rowPartitionTypes[t]}getRowPartitionTensor(t){return this.rowPartitionTypes[0]===Po.FIRST_DIM_SIZE?this.rowPartitionValues[t+1]:this.rowPartitionValues[t]}getMaxWidth(t){let e=this.getRowPartitionTensor(t-1);switch(this.getRowPartitionTypeByDimension(t-1)){case Po.VALUE_ROWIDS:return bd.getMaxWidthValueRowID(e);case Po.ROW_SPLITS:return bd.getMaxWidthRowSplit(e);default:throw new Error(`Cannot handle partition type ${Po[this.getRowPartitionTypeByDimension(t-1)]}`)}}static getMaxWidthRowSplit(t){let e=t.length;if(e===0||e===1)return 0;let n=0;for(let o=0;o<e-1;++o){let s=t[o+1]-t[o];s>n&&(n=s)}return n}static getMaxWidthValueRowID(t){let e=t.length;if(e===0)return 0;let n=0,o=t[0],s=0;for(let i=1;i<e;++i){let a=t[i];a!==o&&(o=a,s=Math.max(i-n,s),n=i)}return Math.max(e-n,s)}tensorShapeFromTensor(t,e,n=!0){if(e.length===0){if(t[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return nO(t,n)}calculateOutputSize(t){let e=this.valuesShape,n=this.defaultValueShape;S.validateDefaultValueShape(n,e);let o=this.tensorShapeFromTensor(this.shape,this.shapeShape),i=S.combineRaggedTensorToTensorShapes(this.raggedRank,o,e);i[0]<0&&(i[0]=t);for(let a=1;a<=this.raggedRank;++a)i[a]<0&&(i[a]=this.getMaxWidth(a));return i}calculateFirstParentOutputIndex(t,e,n){let o=Math.min(t,n),s=[],i=0;for(let a=0;a<o;++a,i+=e)s.push(i);for(let a=o;a<t;++a)s.push(-1);return y.assert(s.length===t,()=>"Final length of result must be equal to firstDimension."),s}calculateOutputIndexRowSplit(t,e,n,o){let s=t.length,i=[];for(let a=0;a<s-1;++a){let u=t[a+1]-t[a],l=Math.min(o,u),c=e[a];c===-1&&(l=0);for(let p=0;p<l;++p)i.push(c),c+=n;for(let p=0;p<u-l;++p)i.push(-1)}if(s>0&&i.length!==t[s-1])throw new Error("Invalid row split size.");return i}calculateOutputIndexValueRowID(t,e,n,o){let s=t.length,i=[];if(s===0)return[];let a=0,u=t[0];if(u>=e.length)throw new Error(`Got currentValueRowId=${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 Po.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,e,n,o);case Po.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: ${Po[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 Po.FIRST_DIM_SIZE:return t[0];case Po.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Po.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${Po[e]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. 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I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=Jt({inputs:{x:o},backend:e,attrs:{shape:I}}),A=Jt({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 Fn=G[It*K+xe*X+De*Z],ar=W[De*et+fe*nt+Rt*st];Ae+=Fn*ar}it[gt*at+(xe*P+fe)]+=Ae}}}}}return e.disposeIntermediateTensorInfo(E),e.disposeIntermediateTensorInfo(A),e.makeTensorInfo(w,ot.dtype,ot.values)}var IO={kernelName:Qo,backendName:"cpu",kernelFunc:mT};function K9(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=mT({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 CO={kernelName:qi,backendName:"cpu",kernelFunc:K9};var j9=At(Uo,r=>Math.acos(r)),vO={kernelName:Uo,backendName:"cpu",kernelFunc:j9};var X9=At(Ho,r=>Math.acosh(r)),SO={kernelName:Ho,backendName:"cpu",kernelFunc:X9};function Y9(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 NO={kernelName:qo,backendName:"cpu",kernelFunc:Y9};function 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i=o.strideDepth,a=o.strideHeight,u=o.strideWidth,l=o.dilationDepth,c=o.dilationHeight,p=o.dilationWidth,m=o.effectiveFilterDepth,f=o.effectiveFilterHeight,d=o.effectiveFilterWidth,h=o.padInfo.front,g=o.padInfo.top,x=o.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=bt(o.outShape,e),I=w.values,N=o.outShape[1]*o.outShape[2]*o.outShape[3]*o.outShape[4],E=o.outShape[2]*o.outShape[3]*o.outShape[4],A=o.outShape[3]*o.outShape[4],D=o.outShape[4];for(let F=0;F<o.batchSize;++F){let P=F*N,V=F*n[0];for(let G=0;G<o.inChannels;++G)for(let W=0;W<o.outDepth;++W){let q=W*i-h,H=q;for(;H<0;)H+=l;let K=Math.min(o.inDepth,m+q),X=P+W*E;for(let Z=0;Z<o.outHeight;++Z){let et=Z*a-g,nt=et;for(;nt<0;)nt+=c;let st=Math.min(o.inHeight,f+et),at=X+Z*A;for(let ot=0;ot<o.outWidth;++ot){let it=ot*u-x,mt=it;for(;mt<0;)mt+=p;let gt=Math.min(o.inWidth,d+it),It=at+ot*D,Rt=b,Dt=0,Ht=0;for(let ce=H;ce<K;ce+=l){let ge=V+ce*n[1];for(let ee=nt;ee<st;ee+=c){let xe=ge+ee*n[2];for(let 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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 LO={kernelName:Xl,backendName:"cpu",kernelFunc:utt};function ctt(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 zO={kernelName:Fp,backendName:"cpu",kernelFunc:ctt};function ptt(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 BO={kernelName:hs,backendName:"cpu",kernelFunc:ptt};function mtt(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=Jt({inputs:{x:o},backend:e,attrs:{shape:u}}),d=Ge({inputs:{x:f},backend:e,attrs:{perm:l}}),h=Jt({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var VO={kernelName:Di,backendName:"cpu",kernelFunc:mtt};function ftt(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=xd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var GO={kernelName:Da,backendName:"cpu",kernelFunc:ftt};function dtt(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 WO={kernelName:Yl,backendName:"cpu",kernelFunc:dtt};var htt=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r<e.clipValueMin?e.clipValueMin:r}),UO={kernelName:ho,backendName:"cpu",kernelFunc:htt};var gtt=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")},HO={kernelName:Zl,backendName:"cpu",kernelFunc:gtt};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 qO={kernelName:Gp,backendName:"cpu",kernelFunc:ba};function Uu(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=>Fo({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Uu({inputs:h,backend:e,attrs:{axis:s}}),b=Uu({inputs:g,backend:e,attrs:{axis:s}}),w=Cr({inputs:{real:x,imag:b},backend:e});return 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xtt(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 e.makeTensorInfo(b.shape,b.dtype,b.values)}var XO={kernelName:Pp,backendName:"cpu",kernelFunc:xtt};function ytt(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;tt([o,s],"conv2dBackpropInput");let p=y.computeStrides(s.shape),m=y.computeStrides(o.shape),f=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,f),h=new le(d.inShape,"float32"),g=h.values,x=e.data.get(o.dataId).values,b=e.data.get(s.dataId).values,[w,I,N]=p,{batchSize:E,filterHeight:A,filterWidth:D,inChannels:F,inHeight:P,inWidth:V,outChannels:G,outHeight:W,outWidth:q,strideHeight:H,strideWidth:K}=d;f=d.dataFormat;let X=A-1-d.padInfo.top,Z=D-1-d.padInfo.left,et=f==="channelsLast",nt=h.strides[0],st=et?h.strides[1]:h.strides[2],at=et?h.strides[2]:1,ot=et?1:h.strides[1],it=m[0],mt=et?m[1]:m[2],gt=et?m[2]:1,It=et?1:m[1];for(let Rt=0;Rt<E;++Rt)for(let Dt=0;Dt<F;++Dt)for(let Ht=0;Ht<P;++Ht){let 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 ar=ce;ar<ge;++ar){let Yn=ar*H-qt;for(let Br=fe;Br<Ae;++Br){let je=Br*K-xe,Vr=it*Rt+mt*ar+gt*Br,Gr=w*(A-1-Yn)+I*(D-1-je)+N*Dt;for(let Zn=0;Zn<G;++Zn){let Jn=x[Vr+It*Zn],Jr=b[Gr+Zn];De+=Jn*Jr}}}let Fn=nt*Rt+st*Ht+at*ee+ot*Dt;g[Fn]=De}}return e.makeTensorInfo(h.shape,h.dtype,h.values)}var YO={kernelName:rs,backendName:"cpu",kernelFunc:ytt};function btt(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 ZO={kernelName:ns,backendName:"cpu",kernelFunc:btt};function wtt(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,Fn=Ae*F;for(let ar=at;ar<ot;++ar){let Br=(st+ar*m-Z)*H+De,je=ar*P+Fn;for(let Vr=gt;Vr<It;++Vr){let Zn=(mt+Vr*f-nt)*K+Br,Jn=Vr*V+je;for(let Jr=Ht;Jr<qt;++Jr){let Vo=(Dt+Jr*d-et)*X+Zn,Si=Jr*G+Jn;fe+=W[Vo+ge]*D[Si+xe]}}}}w[ee+xe]=fe}}}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var JO={kernelName:$a,backendName:"cpu",kernelFunc:wtt};function Itt(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)),Fn=Math.min(at,(q+Ae)/mt);for(let ar=0;ar<et;++ar){let Yn=ar-Dt,Br=Math.max(0,Math.ceil(Yn/gt)),je=Math.min(ot,(H+Yn)/gt),Vr=0;for(let Gr=ee;Gr<xe;++Gr){let Zn=Gr*it-ge;for(let Jn=De;Jn<Fn;++Jn){let Jr=Jn*mt-Ae;for(let Ca=Br;Ca<je;++Ca){let Vo=Ca*gt-Yn,Si=w*Ht+I*Gr+N*Jn+E*Ca,Er=D*(W-1-Zn)+F*(q-1-Jr)+P*(H-1-Vo)+V*qt;for(let va=0;va<nt;++va){let Ud=b[Si+va],Hd=A[Er+va];Vr+=Ud*Hd}}}}f[d*Ht+h*ce+g*fe+x*ar+qt]=Vr}}}return e.makeTensorInfo(m.shape,m.dtype,m.values)}var QO={kernelName:Ra,backendName:"cpu",kernelFunc:Itt};var Ctt=At(os,r=>Math.cos(r)),tP={kernelName:os,backendName:"cpu",kernelFunc:Ctt};var vtt=At(ss,r=>Math.cosh(r)),eP={kernelName:ss,backendName:"cpu",kernelFunc:vtt};function 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lP={kernelName:Mp,backendName:"cpu",kernelFunc:Ett};function Att(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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pP={kernelName:ls,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 mP={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=Mo({inputs:{x:a},backend:e,attrs:{begin:[g,0],size:[1,s]}}),b=Mo({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}=Gtt(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 Gtt(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(Wtt(n)){let a=hT(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=Jh.kernelFunc({inputs:{a:l,b:p},backend:e}),d=Jh.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=Utt(a,n,t);return S.splitRealAndImagArrays(u)}}function Wtt(r){return(r&r-1)===0}function hT(r,t,e,n,o){if(e===1)return{real:r,imag:t};let 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.bufferSync(s),m=OO(p,c),f=c.strideDepth,d=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,w=c.effectiveFilterDepth,I=c.effectiveFilterHeight,N=c.effectiveFilterWidth,E=w-1-c.padInfo.front,A=N-1-c.padInfo.left,D=I-1-c.padInfo.top,F=bt(s.shape,"float32"),P=e.bufferSync(o);for(let V=0;V<c.batchSize;++V)for(let G=0;G<c.inChannels;++G)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let H=0;H<c.inWidth;++H){let K=W-E,X=q-D,Z=H-A,et=0;for(let nt=0;nt<w;nt+=g){let st=(K+nt)/f;if(!(st<0||st>=c.outDepth||Math.floor(st)!==st))for(let at=0;at<I;at+=x){let ot=(X+at)/d;if(!(ot<0||ot>=c.outHeight||Math.floor(ot)!==ot))for(let it=0;it<N;it+=b){let mt=(Z+it)/h;if(mt<0||mt>=c.outWidth||Math.floor(mt)!==mt)continue;let gt=w*I*N-1-m.get(V,st,ot,mt,G),It=nt*I*N+at*N+it,Rt=gt===It?1:0;if(Rt===0)continue;let Dt=P.get(V,st,ot,mt,G);et+=Dt*Rt}}}F.set(et,V,W,q,H,G)}return e.makeTensorInfo(F.shape,F.dtype,F.values)}var BP={kernelName:nu,backendName:"cpu",kernelFunc:fet};function det(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,ww(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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trt=11920928955078125e-23,kM=Math.log(trt)+2,ert=At(Xs,r=>{let t=r>-kM,e=r<kM,n=Math.exp(r),o;return e?o=n:t?o=r:o=Math.log(1+n),o}),TM={kernelName:Xs,backendName:"cpu",kernelFunc:ert};function rrt(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=vw.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=Jt({inputs:{x:l},backend:e,attrs:{shape:c}}),b=Ge({inputs:{x:h},backend:e,attrs:{perm:p}}),N=Jt({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(b),N}var _M={kernelName:Gi,backendName:"cpu",kernelFunc:rrt};function nrt(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]=dw(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 EM={kernelName:su,backendName:"cpu",kernelFunc:nrt};function ort(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 ${s.shape}`);let i=Array.from(e.data.get(o.dataId).values),a=e.data.get(n.dataId).values,u=Array.from(e.data.get(s.dataId).values),[l,c,p]=hw(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var AM={kernelName:ll,backendName:"cpu",kernelFunc:ort};function srt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${s.shape}`);if(o.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=wd(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var DM={kernelName:iu,backendName:"cpu",kernelFunc:srt};function irt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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${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]=wd(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var $M={kernelName:au,backendName:"cpu",kernelFunc:irt};function art(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=bi(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=bi(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=bi(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=bi(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 RM={kernelName:ul,backendName:"cpu",kernelFunc:art};function lrt(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=Mo({inputs:{x:o},backend:e,attrs:{begin:l,size:m}});return l[a]+=p,f})}var FM={kernelName:Wi,backendName:"cpu",kernelFunc:lrt};var OM={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 urt=At(xo,(r,t)=>{let e=t;return isNaN(r)?NaN:r>0?1:e.alpha}),PM={kernelName:xo,backendName:"cpu",kernelFunc:urt};function crt(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;tt(o,"stridedSlice");let{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=Jt({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 E=ze.computeOutShape(b,w,I),A=Mo({inputs:{x:o},backend:e,attrs:{begin:b,size:E}});N=Jt({inputs:{x:A},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(A)}else{let E=e.bufferSync(o),A=gw(f,E,I,b);N=e.makeTensorInfo(d,A.dtype,A.values)}return N}var MM={kernelName:cl,backendName:"cpu",kernelFunc:crt};function prt(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 LM={kernelName:uu,backendName:"cpu",kernelFunc:prt};function mrt(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 zM={kernelName:cu,backendName:"cpu",kernelFunc:mrt};function frt(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 BM={kernelName:pu,backendName:"cpu",kernelFunc:frt};var drt=At(ei,r=>Math.tan(r)),VM={kernelName:ei,backendName:"cpu",kernelFunc:drt};var hrt=At(ri,r=>Math.tanh(r)),GM={kernelName:ri,backendName:"cpu",kernelFunc:hrt};function grt(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=bi(m,f,n.shape,c,u,a,i,l,d,p);return e.makeTensorInfo(n.shape,h.dtype,h.values)}var WM={kernelName:sl,backendName:"cpu",kernelFunc:grt};function xrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;tt(o,"tile");let i=xw(e.bufferSync(o),s);return e.makeTensorInfo(i.shape,i.dtype,i.values)}var UM={kernelName:no,backendName:"cpu",kernelFunc:xrt};function yrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n;tt(o,"topk");let a=e.data.get(o.dataId).values,[u,l]=yw(a,o.shape,o.dtype,s,i);return[e.makeTensorInfo(u.shape,u.dtype,u.values),e.makeTensorInfo(l.shape,l.dtype,l.values)]}var HM={kernelName:pl,backendName:"cpu",kernelFunc:yrt};function brt(r){let{inputs:t,attrs:e,backend:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=e,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=y.computeStrides(o.shape),b=x[0],w=x[1],I=x[2],N=y.computeStrides(g),E=N[0],A=N[1],D=N[2],F=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(g));F.fill(u);let P=n.data.get(o.dataId).values,V=n.data.get(s.dataId).values;for(let W=0;W<c;++W){let q=s.shape[0]===1?V:V.subarray(W*8,W*8+8);for(let 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pp={},Sw={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function bT(r,t){pp[r]=t}function qn(r,t){if(!(r in pp)||t!=null){let n=Drt(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],qn(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 Art(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 Drt(r,t){if(r!==1&&r!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let e=t==null?Art(r):t;return e.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete pp[r]},!1),L().getBool("SOFTWARE_WEBGL_ENABLED")&&(Sw.failIfMajorPerformanceCaveat=!1),r===1?e.getContext("webgl",Sw)||e.getContext("experimental-webgl",Sw):e.getContext("webgl2",Sw)}var Hu;(function(r){r[r.DENSE=0]="DENSE",r[r.SHARED_BATCH=1]="SHARED_BATCH"})(Hu||(Hu={}));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 ZM(r,t){return r*t}function rg(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 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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 vd(r,t){return Ll(r,()=>r.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function vT(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 ST(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 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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(`
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`)),console.log(t.split(`
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`)[0]),console.log(`%c ${y.rightPad(l[0],a)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
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|
`))}function NT(r){return Ll(r,()=>r.createProgram(),"Unable to create WebGLProgram.")}function kT(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 og(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 TT(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 _T(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 Prt(){return L().getNumber("WEBGL_VERSION")===2?1:4}function ET(r){return 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qu(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||Nw(e)&&Nw(n)&&(r[0]===1||t[0]===1))return!0}return r[1]===t[1]&&Nw(r[0])&&Nw(t[0])}var kw,Tw;function PT(r){if(kw==null){let t=qn(r);kw=t.getParameter(t.MAX_TEXTURE_SIZE)}return kw}function zrt(){kw=null}function Brt(){Tw=null}function MT(r){if(Tw==null){let t=qn(r);Tw=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Tw)}function LT(r){if(r===0)return 0;let t,e=qn(r);return Kn(e,"EXT_disjoint_timer_query_webgl2")&&r===2?t=2:Kn(e,"EXT_disjoint_timer_query")?t=1:t=0,t}function Kn(r,t){return r.getExtension(t)!=null}function Dw(r){try{if(qn(r)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function zT(r){if(r===0)return!1;let t=qn(r);if(r===1){if(!Kn(t,"OES_texture_float"))return!1}else if(!Kn(t,"EXT_color_buffer_float"))return!1;return IT(t)}function BT(r){if(r===0)return!1;let t=qn(r);if(r===1){if(!Kn(t,"OES_texture_float")||!Kn(t,"WEBGL_color_buffer_float"))return!1}else{if(Kn(t,"EXT_color_buffer_float"))return IT(t);let n="EXT_color_buffer_half_float";if(Kn(t,n)){let o=t.getExtension(n);return Vrt(t,o)}return!1}return IT(t)}function IT(r){let t=ng(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 Vrt(r,t){let e=ng(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 VT(r){return r!==2?!1:qn(r).fenceSync!=null}function wi(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 _t=L();_t.registerFlag("HAS_WEBGL",()=>_t.getNumber("WEBGL_VERSION")>0);_t.registerFlag("WEBGL_VERSION",()=>Dw(2)?2:Dw(1)?1:0);_t.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);_t.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>_t.get("WEBGL_VERSION")===2);_t.registerFlag("WEBGL_CPU_FORWARD",()=>!0);_t.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);_t.registerFlag("WEBGL_PACK",()=>_t.getBool("HAS_WEBGL"));_t.registerFlag("WEBGL_PACK_NORMALIZATION",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_CLIP",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_PACK_REDUCE",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_LAZILY_UNPACK",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_CONV_IM2COL",()=>_t.getBool("WEBGL_PACK"));_t.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>PT(_t.getNumber("WEBGL_VERSION")));_t.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>MT(_t.getNumber("WEBGL_VERSION")));_t.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=_t.getNumber("WEBGL_VERSION");return r===0?0:LT(r)});_t.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>_t.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!xu.isMobile());_t.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>zT(_t.getNumber("WEBGL_VERSION")));_t.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>_t.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:_t.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));_t.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>BT(_t.getNumber("WEBGL_VERSION")));_t.registerFlag("WEBGL_FENCE_API_ENABLED",()=>VT(_t.getNumber("WEBGL_VERSION")));_t.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>_t.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);_t.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}.`)});_t.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}.`)});_t.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);_t.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);_t.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);_t.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);_t.registerFlag("WEBGL_EXP_CONV",()=>!1);_t.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>_t.getBool("IS_TEST"));_t.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);_t.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);_t.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);_t.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 Ii(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}; ${a};`}).join("")}function Grt(r,t){let e=r.length,n=r.map(s=>`${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 nL(r,t,e="index"){let n=r.map((s,i)=>i),o=Grt(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 Td(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 _d(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var $w=`
|
|
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:oL}=S;function sL(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}=Rw(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?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
|
|
`),s=r.map(f=>Wrt(f,t,e.packedInputs,e.enableShapeUniforms)).join(`
|
|
`),i=t.texShape,a=We(),u=qrt(a),l,c,p=Xrt(a);return t.isPacked?(l=Urt(t.logicalShape,i,e.enableShapeUniforms),c=jrt(a)):(l=Hrt(t.logicalShape,i,e.enableShapeUniforms),c=Krt(a)),e.packedInputs&&(p+=Qrt),[p,u,c,o,l,s,e.userCode].join(`
|
|
`)}function Ad(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return pnt(r,t);case 1:return fnt(r,t);case 2:return hnt(r,t);case 3:return xnt(r,t);case 4:return bnt(r,t);case 5:return wnt(r);case 6:return Int(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function iL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return cnt(r);case 1:return mnt(r,t);case 2:return dnt(r,t);case 3:return gnt(r,t);default:return ynt(r,t)}}function Wrt(r,t,e=!1,n){let o="";e?o+=iL(r,n):o+=Ad(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Cnt(r,t):o+=vnt(r,t)),o}function Urt(r,t,e){switch(r.length){case 0:return aL();case 1:return tnt(r,t,e);case 2:return lnt(r,t,e);case 3:return rnt(r,t,e);default:return ont(r,t,e)}}function Hrt(r,t,e){switch(r.length){case 0:return aL();case 1:return ent(r,t,e);case 2:return unt(r,t,e);case 3:return nnt(r,t,e);case 4:return snt(r,t,e);case 5:return int(r,t);case 6:return ant(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function qrt(r){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${r.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function Krt(r){return`
|
|
void setOutput(float val) {
|
|
${r.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function jrt(r){return`
|
|
void setOutput(vec4 val) {
|
|
${r.output} = val;
|
|
}
|
|
`}function Xrt(r){return`${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);
|
|
}
|
|
|
|
${Yrt}
|
|
${Zrt}
|
|
${Jrt}
|
|
`}var Yrt=`
|
|
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);
|
|
}
|
|
`,Zrt=`
|
|
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);
|
|
}
|
|
`,Jrt=`
|
|
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);
|
|
}
|
|
`,Qrt=`
|
|
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 aL(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function tnt(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 ent(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function rnt(r,t,e){if(e)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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 nnt(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;
|
|
${fp(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let n=Ii(["r","c","d"],r);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function ont(r,t,e){if(e)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l<r.length-1;l++)i*=r[r.length-l-1],a=`
|
|
int b${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 snt(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;
|
|
${fp(["r","c","d","d2"],r)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let n=Ii(["r","c","d","d2"],r);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function int(r,t){let e=Ii(["r","c","d","d2","d3"],r);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${e}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function ant(r,t){let e=Ii(["r","c","d","d2","d3","d4"],r);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${e}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function lnt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${o}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function unt(r,t,e){return y.arraysEqual(r,t)?e?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 cnt(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 pnt(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 mnt(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 fnt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Dd(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 dnt(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 hnt(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=$d(r,u),f=["row","col"];return`
|
|
${Ad(m,t)}
|
|
float ${o}(int row, int col) {
|
|
return ${o}(${Rd(f,a)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
|
|
${Dd(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 gnt(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=$d(r,m),h=["b","row","col"];return`
|
|
${iL(d,t)}
|
|
vec4 ${o}(int b, int row, int col) {
|
|
return ${o}(${Rd(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 xnt(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=$d(r,l),g=["row","col","depth"];return`
|
|
${Ad(h,t)}
|
|
float ${o}(int row, int col, int depth) {
|
|
return ${o}(${Rd(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)));
|
|
${Dd(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 ynt(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 bnt(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=$d(r,u),w=["row","col","depth","depth2"];return`
|
|
${Ad(b,t)}
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
return ${o}(${Rd(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)));
|
|
${Dd(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 wnt(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=$d(r,u),g=["row","col","depth","depth2","depth3"];return`
|
|
${Ad(h)}
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${n}(${Rd(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;
|
|
${Dd(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 Int(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=$d(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Ad(g)}
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${n}(${Rd(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)));
|
|
${Dd(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 Dd(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 ${t}[i];
|
|
}
|
|
}
|
|
`}function Cnt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=oL(r.shapeInfo.logicalShape,t.logicalShape),u=zt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${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 vnt(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=oL(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 Rw(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 $d(r,t){let e=JSON.parse(JSON.stringify(r));return e.shapeInfo.logicalShape=t,e}function Rd(r,t){return t.map(e=>r[e]).join(", ")}function uL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=sL(o,i,t),u=ST(r.gl,a),l=r.createProgram(u);return L().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},GT(r,t,l))}function GT(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,"NAN",!1),L().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${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 lL(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 cL(r,t,e,n,o){t.program.enableShapeUniforms||(lL(t.inShapeInfos,e),lL([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),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}=Rw(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 ${l.type} is not supported yet.`)}r.executeProgram()}function pL(r,t,e){let n="";t.concat(e).forEach(i=>{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Rw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${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 Fw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hu.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):Ii(["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 Ow=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Hu.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):Ii(["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 Pw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`
|
|
${$w}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${e.output} = encode_float(x);
|
|
}
|
|
`}};var Mw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zr.DOWNLOAD;let e=We();this.outputShape=t,this.userCode=`
|
|
${$w}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${e.output} = encode_float(x);
|
|
}
|
|
`}};var knt={R:0,G:1,B:2,A:3},ig=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 == ${a}) {
|
|
result = values[${knt[u]}];
|
|
}`}this.userCode=`
|
|
${this.enableShapeUniforms?_d():Td(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 Lw=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?_d():Td(t)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${o}
|
|
|
|
${n.output} = ${s};
|
|
}
|
|
`}};var s1={};Kt(s1,{bindVertexProgramAttributeStreams:()=>ZT,createBufferFromOutputTexture:()=>t1,createFloat16MatrixTexture:()=>KT,createFloat16PackedMatrixTexture:()=>YT,createFloat32MatrixTexture:()=>qT,createIndexBuffer:()=>HT,createPackedMatrixTexture:()=>XT,createUnsignedBytesMatrixTexture:()=>jT,createVertexBuffer:()=>UT,createVertexShader:()=>WT,downloadByteEncodedFloatMatrixFromOutputTexture:()=>r1,downloadFloat32MatrixFromBuffer:()=>e1,downloadMatrixFromPackedOutputTexture:()=>o1,downloadPackedMatrixFromBuffer:()=>n1,getInternalFormatForFloat16MatrixTexture:()=>Bw,getInternalFormatForFloat16PackedMatrixTexture:()=>Ww,getInternalFormatForFloat32MatrixTexture:()=>zw,getInternalFormatForPackedMatrixTexture:()=>Gw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Vw,uploadDenseMatrixToTexture:()=>JT,uploadPixelDataToTexture:()=>QT});function WT(r){let t=We(),e=`${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 vT(r,e)}function UT(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 TT(r,t)}function HT(r){let t=new Uint16Array([0,1,2,2,1,3]);return _T(r,t)}function ag(r,t,e,n,o,s){AT(t,e);let i=ET(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 zw(r){return r.internalFormatFloat}function qT(r,t,e,n){let[o,s]=mp(t,e);return ag(r,o,s,zw(n),n.textureFormatFloat,r.FLOAT)}function Bw(r){return r.internalFormatHalfFloat}function KT(r,t,e,n){let[o,s]=mp(t,e);return ag(r,o,s,Bw(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Vw(r){return r.downloadTextureFormat}function jT(r,t,e,n){let[o,s]=mp(t,e);return ag(r,o,s,Vw(n),r.RGBA,r.UNSIGNED_BYTE)}function Gw(r){return r.internalFormatPackedFloat}function XT(r,t,e,n){let[o,s]=wa(t,e);return ag(r,o,s,Gw(n),r.RGBA,r.FLOAT)}function Ww(r){return r.internalFormatPackedHalfFloat}function YT(r,t,e,n){let[o,s]=wa(t,e);return ag(r,o,s,Ww(n),r.RGBA,n.textureTypeHalfFloat)}function ZT(r,t,e){return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),Ew(r,t,"clipSpacePos",e,3,20,0)&&Ew(r,t,"uv",e,2,20,12)}function JT(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 QT(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 t1(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 e1(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 r1(r,t,e,n){let[o,s]=mp(t,e),i=4,a=new Uint8Array(ZM(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function n1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array(JM(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 o1(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,bT(e,t)):this.gl=qn(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=vd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=vd(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),Kn(this.gl,o))this.colorBufferHalfFloatExtension=vd(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",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=UT(this.gl),this.indexBuffer=HT(this.gl),this.framebuffer=DT(this.gl),this.textureConfig=ng(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(),qT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),KT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),jT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),QT(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),JT(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),YT(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),XT(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Aw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>r1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return n1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return e1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=t1(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,()=>o1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=WT(e));let n=NT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),kT(e,n);let o;return o=Object.assign(n,{vao:this.createVertexArray()}),this.bindVertexArray(o.vao),ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),console.assert(ZT(e,o,this.vertexBuffer),"gpgpu_util.bindVertexProgramAttributeStreams not fully successful."),this.debug&&og(e,o),this.setProgram(o),o}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.bindVertexArray(this.program.vao),this.debug&&og(this.gl,this.program)),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?$T(this.gl,t,e):RT(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(),FT(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&&og(this.gl,this.program),Sd(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=vd(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=Tnt(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(),sg(this.gl,t,this.framebuffer),this.debug&&Sd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(sg(this.gl,this.outputTexture,this.framebuffer),this.debug&&Sd(this.gl)):Aw(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;sg(o,t,this.framebuffer),this.debug&&Sd(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 Tnt(r){let t=0;for(;t<r.length&&r[t]();++t);return t-1}var{addImpl:mL,bincountImpl:Uw,bincountReduceImpl:fL,castImpl:dL,ceilImpl:hL,concatImpl:gL,equalImpl:xL,expImpl:yL,expm1Impl:bL,floorImpl:wL,gatherNdImpl:IL,gatherV2Impl:CL,greaterImpl:vL,greaterEqualImpl:SL,lessImpl:NL,lessEqualImpl:kL,linSpaceImpl:TL,logImpl:_L,maxImpl:EL,maximumImpl:AL,minimumImpl:DL,multiplyImpl:$L,negImpl:RL,notEqualImpl:FL,prodImpl:OL,raggedGatherImpl:PL,raggedRangeImpl:ML,raggedTensorToTensorImpl:LL,rangeImpl:zL,rsqrtImpl:BL,scatterImpl:VL,sigmoidImpl:GL,simpleAbsImpl:Hw,sliceImpl:WL,sparseFillEmptyRowsImpl:UL,sparseReshapeImpl:HL,sparseSegmentReductionImpl:qw,sqrtImpl:qL,staticRegexReplaceImpl:KL,stridedSliceImpl:jL,stringNGramsImpl:XL,stringSplitImpl:YL,stringToHashBucketFastImpl:ZL,subImpl:JL,tileImpl:QL,topKImpl:tz,transposeImpl:gp,uniqueImpl:ez}=bw;function i1(r,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${r}.${e}`)}function er(r,t){return t===1?[r]:i1(r,t)}function rz(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 Kw=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 Fd=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=`
|
|
${_nt(e,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?_d():Td(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 _nt(r,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?nL(["r","c","d"],"inputShape"):Ii(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var jw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=oz(e,n),s=sz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=nz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=oz(n,o),i=sz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=nz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 Ent(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 nz(r,t,e,n,o){let s=Ant(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=Ent(e,s);return i*a}function Ant(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Gw(t);case Lr.PACKED_2X2_FLOAT16:return Ww(t);case Lr.UNPACKED_FLOAT32:return zw(t);case Lr.UNPACKED_FLOAT16:return Bw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Vw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Dnt(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 oz(r,t){if(r===Zr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Zr.RENDER||r==null)return Dnt(t);if(r===Zr.DOWNLOAD||r===Zr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function sz(r,t,e){return`${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;",iz="return x;",a1="return abs(x);";var az="return (x >= 0.0) ? x : (exp(x) - 1.0);",lz=yr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,uz=yr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Ia="return x;",cz="return 1.0 / (1.0 + exp(-1.0 * x));";var mz="return x;",fz=`
|
|
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;
|
|
`,dz=`
|
|
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;
|
|
`,hz=`
|
|
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;
|
|
`,gz="return 1.0 / (1.0 + exp(-1.0 * x));",Dn=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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}};var Xw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=de(this.outputShape.length);let e=t.length,n=er("rc",e),o=zt(e),s=rz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${s});
|
|
|
|
setOutput(getChannel(packedInput, ${a}));
|
|
}
|
|
`}};var Rnt=Kr.whereImpl,Fnt=1e-7,Ont=1e-4,Yw={};function Pnt(r){return r in Yw||(Yw[r]={}),Yw[r]}var Mnt=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Lnt=600;function znt(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Lnt/1024/1024}var Ku=class extends Go{nextDataId(){return Ku.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=qn(L().getNumber("WEBGL_VERSION"),t);e=new hp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber("WEBGL_VERSION"));e=new hp(n),this.binaryCache=Pnt(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new jw(this.gpgpu),this.numMBBeforeWarning=znt(),this.texData=new Ta(this,Bn())}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=Nd(e),c=new ig(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 Dn(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 Dn(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,...rg(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)&&Bn().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 Dn(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=Bn().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(!CT(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:n,isPacked:o}=this.texData.get(t),s=y.sizeFromShape(e);if(L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(t),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture,...rg(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let i=L().getBool("WEBGL_PACK")&&o===!0,a=i?Nd(e):e,u=i?new Mw(a):new Pw(a),l=this.runWebGLProgram(u,[{shape:a,dtype:n,dataId:t}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${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=Mnt){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 Rnt(t.shape,e)}packedUnaryOp(t,e,n){let o=new Dn(t.shape,e),s=this.compileAndRun(o,[t],n);return Bn().makeTensorFromTensorInfo(s)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let o=Hw(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,a1,t.dtype);let e=new zr(t.shape,a1),n=this.compileAndRun(e,[t]);return Bn().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 Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Xw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Kw(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 Fd(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=Nd(s),u;o?u=new Ow(a):u=new Fw(a);let l=!0,c=[e!=null?e:rg(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===Hu.DENSE){let x=i!=null?i:rg(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&&!qu(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=pL(t,c,p),f=this.getAndSaveBinary(m,()=>uL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||cL(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?Fnt:Ont}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=OT(n,u),e.texShape=p),s!=null){let m=Nd(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 Lw(m,g):f=new ig(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=Bnt(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 Sh(),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?(_w(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)){let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=GT(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=Bn().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 Bn().makeTensorFromDataId(l,e,n,u)}};Ku.nextDataId=0;function Bnt(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 xz="4.3.0";function yz(){L().set("WEBGL_FORCE_F16_TEXTURES",!0)}xu.isBrowser()&&sm("webgl",()=>new Ku,2);var n2e={forceHalfFloat:yz};var Od=`
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|
if (isnan(a)) return a;
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|
if (isnan(b)) return b;
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|
`;var po=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 jn=`
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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 Lo=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.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;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.;
|
|
result.w = 0.;
|
|
`:i+=`
|
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result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let u=er("coords",s);this.enableShapeUniforms?i+=`
|
|
bool nextRowOutOfBounds =
|
|
(${u[s-2]} + 1) >= outShape[${s} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${u[s-1]} + 1) >= outShape[${s} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:i+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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 bz={kernelName:go,backendName:"webgl",kernelFunc:rr};function $n(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=rr({inputs:{x:n},backend:e}),u=rr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var wz={kernelName:Op,backendName:"webgl",kernelFunc:$n};var l1="return (a < 0.) ? b * a : a;",u1=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function Vnt(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 Lo(u1,o.shape,i.shape):new po(l1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var Iz={kernelName:ws,backendName:"webgl",kernelFunc:Vnt};var c1="return (a < 0.) ? b * a : a;",p1=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function Gnt(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lo(p1,n.shape,o.shape):new po(c1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var Cz={kernelName:Os,backendName:"webgl",kernelFunc:Gnt};var zo="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 Dn(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 po(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],lr(I.dtype,N.dtype))}),b=$n({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||lr(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 Lo(t,u.shape,l.shape,e):f=new po(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Vl(r,t=!1){if(r==="linear")return t?mz:iz;if(r==="relu")return t?dz:lz;if(r==="elu")return t?fz:az;if(r==="relu6")return t?hz:uz;if(r==="prelu")return t?p1:c1;if(r==="leakyrelu")return t?u1:l1;if(r==="sigmoid")return t?gz:cz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Pd=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 m1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},lg=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) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}};var vz="return a * b;";function ug(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new lg(m1.REAL,n.shape,o.shape),c=new lg(m1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=$n({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]=$L(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Lo(vz,n.shape,o.shape):i=new po(vz,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var Sz={kernelName:Ds,backendName:"webgl",kernelFunc:ug};function Nz(r,t,e){let n=[zl(r.shape),...Bl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[zl(t),...Bl(t)],i=new Fd(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${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&&!qu(o.shape,u)&&!(c.texture!==null&&qu(c.shape,u))?Nz(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var kz={kernelName:zi,backendName:"webgl",kernelFunc:rt};var cg=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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}};var Zw=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${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 Unt(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 Xn(r,t,e,n){let o=Unt(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 cg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},a):new cg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l}):c=new Zw({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 Jw=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=Hnt(e);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function Hnt(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 Qw=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 ${this.rank} is not yet supported.`);let o=zt(this.rank),s=i1("rc",this.rank),i=new Array(this.rank);for(let c=0;c<e.length;c++)i[e[c]]=s[c];let a=`vec2(${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] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function ju(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Qw(r.shape,t):new Jw(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function Tz(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=ju(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=pc(r.dtype),w=Xn(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function xp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return Tz(o,s,i,e)}var _z={kernelName:Zs,backendName:"webgl",kernelFunc:xp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c<u.length;c++)u[c]=o.shape[s[c]];let l;if(i.shouldExecuteOnCPU([o])){let p=i.texData.get(o.dataId).values,m=gp(p,o.shape,o.dtype,s,u);l=i.makeTensorInfo(u,o.dtype);let f=i.texData.get(l.dataId);f.values=m}else l=ju(o,s,i);return l}var Ez={kernelName:oo,backendName:"webgl",kernelFunc:Pe};var f1=1e3;function yp({a:r,b:t,transposeA:e,transposeB:n,backend:o,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:u=null}){let l=r.shape.length,c=t.shape.length,p=e?r.shape[l-2]:r.shape[l-1],m=n?t.shape[c-1]:t.shape[c-2],f=e?r.shape[l-1]:r.shape[l-2],d=n?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),I=Ur.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${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>f1&&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=ug({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=lr(r.dtype,t.dtype),nt=new Pd(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 qnt(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 Az={kernelName:qi,backendName:"webgl",kernelFunc:qnt};var Dz="return abs(x);";function Knt(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=Hw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,Dz):o=new zr(n.shape,Dz),e.runWebGLProgram(o,[n],n.dtype)}var $z={kernelName:Ti,backendName:"webgl",kernelFunc:Knt};var jnt=yr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,Xnt=wt({opSnippet:jnt}),Rz={kernelName:Uo,backendName:"webgl",kernelFunc:Xnt};var Ynt=yr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,Znt=wt({opSnippet:Ynt}),Fz={kernelName:Ho,backendName:"webgl",kernelFunc:Znt};var Oz="return a + b;",Jnt=ue({opSnippet:Oz,packedOpSnippet:Oz,supportsComplex:!0,cpuKernelImpl:mL}),Pz={kernelName:ro,backendName:"webgl",kernelFunc:Jnt};var tI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${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 = ${o};
|
|
setOutput(result);
|
|
}
|
|
`}};var eI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${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 rI(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=rI({inputs:n.slice(0,u),backend:e}),c=rI({inputs:n.slice(u),backend:e});return rI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>lr(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new eI(n[0].shape,s):new tI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var Mz={kernelName:qo,backendName:"webgl",kernelFunc:rI};function Qnt(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=Xn(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 Lz={kernelName:Ea,backendName:"webgl",kernelFunc:Qnt};function tot(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=Xn(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 zz={kernelName:Aa,backendName:"webgl",kernelFunc:tot};var nI=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 ${a} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}};var oI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${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 Bz(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 nI(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=Bz(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function Vz(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new oI(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=Vz(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function sI(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=Bz(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 Vz(r,t,n)}function eot(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=sI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var Gz={kernelName:_i,backendName:"webgl",kernelFunc:eot};function rot(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=sI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var Wz={kernelName:Ei,backendName:"webgl",kernelFunc:rot};var not=yr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,oot=wt({opSnippet:not}),Uz={kernelName:Ko,backendName:"webgl",kernelFunc:oot};var sot=yr+"return log(x + sqrt(x * x + 1.0));",iot=wt({opSnippet:sot}),Hz={kernelName:jo,backendName:"webgl",kernelFunc:iot};var aot=yr+`
|
|
return atan(x);
|
|
`,lot=wt({opSnippet:aot}),qz={kernelName:Xo,backendName:"webgl",kernelFunc:lot};var uot=Od+`
|
|
return atan(a, b);
|
|
`,cot=`
|
|
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);
|
|
`+jn+`
|
|
return result;
|
|
`,pot=ue({opSnippet:uot,packedOpSnippet:cot}),Kz={kernelName:Zo,backendName:"webgl",kernelFunc:pot};var mot=yr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fot=wt({opSnippet:mot}),jz={kernelName:Yo,backendName:"webgl",kernelFunc:fot};var Ci=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});
|
|
}
|
|
`}},Xu=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 dot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;wi(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 Ci(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var Xz={kernelName:Jo,backendName:"webgl",kernelFunc:dot};function hot(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 Xu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var Yz={kernelName:Ai,backendName:"webgl",kernelFunc:hot};var iI=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 >= ${t.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},aI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 got(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 aI(m);return e.runWebGLProgram(f,[o],i.dtype)}var Zz={kernelName:Xl,backendName:"webgl",kernelFunc:got};function xot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;wi([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new iI(c);return e.runWebGLProgram(p,[o],i.dtype)}var Jz={kernelName:Fp,backendName:"webgl",kernelFunc:xot};function yot(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 Qz={kernelName:Qo,backendName:"webgl",kernelFunc:yot};var lI=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 uI=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 bot=({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 uI(n.shape,o.shape,s.shape,c,p,u):new lI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},t3={kernelName:hs,backendName:"webgl",kernelFunc:bot};var cI=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=wot(this.rank),o,s=t.map((i,a)=>`sourceLoc.${d1[a]} = start[${a}] + coords.${d1[a]};`);o=`
|
|
${e} sourceLoc;
|
|
${e} coords = getOutputCoords();
|
|
${s.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${o}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},d1=["x","y","z","w","u","v"];function wot(r){if(r===1)return"sourceLoc";if(r<=6)return d1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var pI=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=er("coords",this.rank),o=er("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${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 Iot(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 vi(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=WL(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 pI(u):new cI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Iot(o,a,u,e)}var e3={kernelName:Vi,backendName:"webgl",kernelFunc:vi};var Cot=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=vi({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},r3={kernelName:Di,backendName:"webgl",kernelFunc:Cot};function vot(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=Uw(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var n3={kernelName:Da,backendName:"webgl",kernelFunc:vot};function Sot(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 o3={kernelName:Yl,backendName:"webgl",kernelFunc:Sot};var Not="return float(a != b);",h1=ue({opSnippet:Not,cpuKernelImpl:FL,dtype:"bool"}),s3={kernelName:Ja,backendName:"webgl",kernelFunc:h1};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 i3={kernelName:jp,backendName:"webgl",kernelFunc:Gl};var kot="return float(int(x));";function a3(r,t){let e=new zr(r.shape,kot),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function g1(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=g1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=$n({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=g1({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]=dL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return a3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=h1({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 l3={kernelName:fo,backendName:"webgl",kernelFunc:g1};var u3="return ceil(x);",Tot=wt({opSnippet:u3,packedOpSnippet:u3,cpuKernelImpl:hL}),c3={kernelName:ts,backendName:"webgl",kernelFunc:Tot};var mI=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 fI=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 _ot(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 fI(o.shape):a=new mI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var p3={kernelName:ho,backendName:"webgl",kernelFunc:_ot};var dI=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 m3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Eot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new dI(n.shape),i=[m3(n,o.complexTensorInfos.real),m3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var f3={kernelName:Zl,backendName:"webgl",kernelFunc:Eot};var hI=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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}};var xI=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=zt(o),i=er("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${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}(${gI(a,l,g)}),
|
|
vec2(${gI(c,l,g)}));
|
|
}`}let f=u.length,d=u[u.length-1];m+=`
|
|
return getChannel(
|
|
getT${f}(${gI(a,l,d)}),
|
|
vec2(${gI(c,l,d)}));`,this.userCode=`
|
|
float getValue(${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 gI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function bp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return rr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var d3={kernelName:Gp,backendName:"webgl",kernelFunc:bp};function Md(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Gl({inputs:{input:b},backend:e})),d=r.map(b=>bp({inputs:{input:b},backend:e})),h=Md(f,t,e),g=Md(d,t,e),x=$n({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=gL(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 Dn(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(Md(g,t,e))}let d=Md(f,t,e);for(let h of f)e.disposeIntermediateTensorInfo(h);return d}if(i){let f=new xI(s.map(d=>d.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Aot(s,t,e),c=new hI(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 Aot(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 x1(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}):Md(u,s,e)}var h3={kernelName:$i,backendName:"webgl",kernelFunc:x1};var Ld=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}
|
|
${N}
|
|
setOutput(result);
|
|
}
|
|
`}},yI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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, ${d} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};var zd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)m+=`
|
|
vec4 xTexelC${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 bI=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 wI(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 II({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=wI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=wI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>f1)&&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(qu(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 CI({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=wI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=wI(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 bI(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 Pd(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 Dot(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=II({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new zd(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=CI({x:o,filter:s,convInfo:m,backend:e});else{let h=new Ld(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 g3={kernelName:es,backendName:"webgl",kernelFunc:Dot};var vI=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;
|
|
}
|
|
|
|
${i?`float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);`}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},SI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${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);
|
|
}
|
|
`}},NI=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 >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},kI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 $ot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new vI(m);return e.runWebGLProgram(f,[o,s],"float32")}var x3={kernelName:Pp,backendName:"webgl",kernelFunc:$ot};var TI=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 < ${t.outChannels}; d2 += 2) {
|
|
vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
vec4 dySample = getDy(batch, idyR, idyC2, d2);
|
|
vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
|
|
dySample.xy : dySample.zw;
|
|
result.zw += vec2(dot(dyValue, wValue.xy),
|
|
dot(dyValue, wValue.zw));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Rot(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new TI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new SI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var y3={kernelName:rs,backendName:"webgl",kernelFunc:Rot};function Fot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new yI(l);return e.runWebGLProgram(c,[o,s],"float32")}var b3={kernelName:ns,backendName:"webgl",kernelFunc:Fot};function Oot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new NI(l);return e.runWebGLProgram(c,[o,s],"float32")}var w3={kernelName:$a,backendName:"webgl",kernelFunc:Oot};function Pot(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 kI(l);return e.runWebGLProgram(c,[o,s],"float32")}var I3={kernelName:Ra,backendName:"webgl",kernelFunc:Pot};var Mot=zo+`
|
|
return cos(x);
|
|
`,Lot=`
|
|
vec4 result = cos(x);
|
|
bvec4 isNaN = isnan(x);
|
|
${jn}
|
|
return result;
|
|
`,zot=wt({opSnippet:Mot,packedOpSnippet:Lot}),C3={kernelName:os,backendName:"webgl",kernelFunc:zot};var Bot=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Vot=wt({opSnippet:Bot}),v3={kernelName:ss,backendName:"webgl",kernelFunc:Vot};var _I=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${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(${f} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}};var Got=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new _I(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},S3={kernelName:Oa,backendName:"webgl",kernelFunc:Got};var wp;(function(r){r.Prod="*",r.Sum="+"})(wp||(wp={}));var pg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===wp.Prod?"1.0":"0.0",a=n?i:`getX(${N3(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 = ${k3(s,"coords",this.op)};
|
|
float val = ${a};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${l}) {
|
|
int idx = ${c};
|
|
${k3(s,"coords",this.op)} = idx;
|
|
val ${this.op}= getX(${N3(s,"coords",this.op)});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function N3(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 ${r} is not yet supported`)}function k3(r,t,e){if(r===1)return`${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 ${r} is not yet supported`)}function EI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=rr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new pg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new pg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Wot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return EI(wp.Prod,o,e,s,i,a)}var T3={kernelName:Fa,backendName:"webgl",kernelFunc:Wot};function Uot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return EI(wp.Sum,o,e,s,i,a)}var _3={kernelName:is,backendName:"webgl",kernelFunc:Uot};function Hot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Uw(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=fL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var E3={kernelName:Jl,backendName:"webgl",kernelFunc:Hot};var AI=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 qot(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 AI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var A3={kernelName:Pa,backendName:"webgl",kernelFunc:qot};var Bd=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}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}};var Vd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=de(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<p;x++)f+=`
|
|
vec4 xTexelC${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 Kot(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 Vd(p):m=new Bd(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 D3={kernelName:as,backendName:"webgl",kernelFunc:Kot};var DI=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 >= ${t.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},$I=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 jot(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 DI(p);return e.runWebGLProgram(m,[o,s],"float32")}var $3={kernelName:Mp,backendName:"webgl",kernelFunc:jot};function Xot(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 $I(p);return e.runWebGLProgram(m,[o,s],"float32")}var R3={kernelName:Lp,backendName:"webgl",kernelFunc:Xot};var RI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function Yot(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new RI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var F3={kernelName:Ql,backendName:"webgl",kernelFunc:Yot};var FI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 Zot(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 FI(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 O3={kernelName:ls,backendName:"webgl",kernelFunc:Zot};function Jot(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=ug({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 P3={kernelName:zp,backendName:"webgl",kernelFunc:Jot};var Qot="return (x >= 0.0) ? x : (exp(x) - 1.0);",tst=`
|
|
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;
|
|
`,est=wt({opSnippet:Qot,packedOpSnippet:tst}),M3={kernelName:cs,backendName:"webgl",kernelFunc:est};var rst="return (b >= 0.0) ? a : a * (b + 1.0);",nst=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,ost=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lo(nst,n.shape,o.shape):new po(rst,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},L3={kernelName:Ma,backendName:"webgl",kernelFunc:ost};var sst=`
|
|
return vec4(equal(a, b));
|
|
`,ist="return float(a == b);",ast=ue({opSnippet:ist,packedOpSnippet:sst,dtype:"bool",cpuKernelImpl:xL}),z3={kernelName:za,backendName:"webgl",kernelFunc:ast};var lst=`
|
|
// 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 = ${S.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,ust=wt({opSnippet:lst}),B3={kernelName:La,backendName:"webgl",kernelFunc:ust};var cst=zo+`
|
|
return exp(x);
|
|
`,pst=`
|
|
vec4 result = exp(x);
|
|
bvec4 isNaN = isnan(x);
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,y1=wt({opSnippet:cst,packedOpSnippet:pst,cpuKernelImpl:yL,dtype:"float32"}),V3={kernelName:ps,backendName:"webgl",kernelFunc:y1};function OI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var G3={kernelName:Ri,backendName:"webgl",kernelFunc:OI};var W3="return exp(x) - 1.0;",mst=wt({opSnippet:W3,packedOpSnippet:W3,cpuKernelImpl:bL}),U3={kernelName:ms,backendName:"webgl",kernelFunc:mst};var mg=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) / ${i};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function PI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new mg("real",u,t),c=new mg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=$n({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 fst(r){let{inputs:t,backend:e}=r,{input:n}=t;return PI(n,!1,e)}var H3={kernelName:Bp,backendName:"webgl",kernelFunc:fst};var MI=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 MI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var q3={kernelName:ru,backendName:"webgl",kernelFunc:Wl};var LI=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 < ${e}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var K3={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new LI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var j3="return floor(x);",dst=wt({opSnippet:j3,packedOpSnippet:j3,cpuKernelImpl:wL}),X3={kernelName:fs,backendName:"webgl",kernelFunc:dst};var hst=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,gst=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,xst=ue({opSnippet:hst,packedOpSnippet:gst,dtype:"int32"}),Y3={kernelName:ds,backendName:"webgl",kernelFunc:xst};var zI=class{constructor(t){this.variableNames=["A"];let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
|
|
|
|
vec4 values = ${e.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}};var BI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=We(),[n,o]=t;this.outputShape=t,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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 Z3={kernelName:rh,backendName:"webgl",kernelFunc:yst},Gd,b1=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function yst(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");(Gd==null||h!==b1)&&(b1=h,Gd=document.createElement("canvas").getContext("2d",{willReadFrequently:b1})),Gd.canvas.width=u,Gd.canvas.height=l,Gd.drawImage(o,0,0,u,l),o=Gd.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 BI(p):new zI(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function bst(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=II({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 zd(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=CI({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 Ld(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 J3={kernelName:Ki,backendName:"webgl",kernelFunc:bst};function wst(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 Vd(g,I,b,N,E):A=new Bd(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 Q3={kernelName:ji,backendName:"webgl",kernelFunc:wst};var VI=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 Ist(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=IL(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new VI(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 tB={kernelName:Va,backendName:"webgl",kernelFunc:Ist};var GI=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Cst(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 Cst(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 w1(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=CL(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new GI(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 eB={kernelName:Fi,backendName:"webgl",kernelFunc:w1};var vst="return float(a > b);",Sst=`
|
|
return vec4(greaterThan(a, b));
|
|
`,Nst=ue({opSnippet:vst,packedOpSnippet:Sst,cpuKernelImpl:vL,dtype:"bool"}),rB={kernelName:Ga,backendName:"webgl",kernelFunc:Nst};var kst="return float(a >= b);",Tst=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,_st=ue({opSnippet:kst,packedOpSnippet:Tst,dtype:"bool",cpuKernelImpl:SL}),nB={kernelName:gs,backendName:"webgl",kernelFunc:_st};function Est(r){let{inputs:t,backend:e}=r,{input:n}=t;return PI(n,!0,e)}var oB={kernelName:Vp,backendName:"webgl",kernelFunc:Est};var Ast="return float(!isnan(x) && !isinf(x));",Dst=wt({opSnippet:Ast,dtype:"bool"}),sB={kernelName:xs,backendName:"webgl",kernelFunc:Dst};var $st="return float(isinf(x));",Rst=wt({opSnippet:$st,dtype:"bool"}),iB={kernelName:ys,backendName:"webgl",kernelFunc:Rst};var Fst="return float(isnan(x));",Ost=wt({opSnippet:Fst,dtype:"bool"}),aB={kernelName:bs,backendName:"webgl",kernelFunc:Ost};var Pst="return float(a < b);",Mst=`
|
|
return vec4(lessThan(a, b));
|
|
`,Lst=ue({opSnippet:Pst,packedOpSnippet:Mst,cpuKernelImpl:NL,dtype:"bool"}),lB={kernelName:Wa,backendName:"webgl",kernelFunc:Lst};var zst="return float(a <= b);",Bst=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,Vst=ue({opSnippet:zst,packedOpSnippet:Bst,cpuKernelImpl:kL,dtype:"bool"}),uB={kernelName:Ua,backendName:"webgl",kernelFunc:Vst};function Gst(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=TL(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var cB={kernelName:Ha,backendName:"webgl",kernelFunc:Gst};var Wst=zo+`
|
|
return x < 0.0 ? 0./0. : log(x);
|
|
`,Ust=`
|
|
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;
|
|
`,Hst=wt({opSnippet:Wst,packedOpSnippet:Ust,cpuKernelImpl:_L}),pB={kernelName:Is,backendName:"webgl",kernelFunc:Hst};var qst=zo+`
|
|
return log(1.0 + x);
|
|
`,Kst=wt({opSnippet:qst}),mB={kernelName:Cs,backendName:"webgl",kernelFunc:Kst};var jst="return float(a >= 1.0 && b >= 1.0);",Xst=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Yst=ue({opSnippet:jst,packedOpSnippet:Xst,dtype:"bool"}),fB={kernelName:qa,backendName:"webgl",kernelFunc:Yst};var Zst="return float(!(x >= 1.0));",Jst=wt({opSnippet:Zst}),dB={kernelName:Ka,backendName:"webgl",kernelFunc:Jst};var Qst="return float(a >= 1.0 || b >= 1.0);",tit=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,eit=ue({opSnippet:Qst,packedOpSnippet:tit,dtype:"bool"}),hB={kernelName:ja,backendName:"webgl",kernelFunc:eit};var WI=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 * ${u};
|
|
setOutput(val);
|
|
}
|
|
`}};var UI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${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 rit=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 UI(o.shape,s,i,a,u):new WI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},gB={kernelName:vs,backendName:"webgl",kernelFunc:rit};var HI=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 nit=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 HI(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},xB={kernelName:Xa,backendName:"webgl",kernelFunc:nit};function yB(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=Xn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function I1(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=ju(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=EL(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=yB(f,h,g,e);return p&&e.disposeIntermediateTensorInfo(f),x}var bB={kernelName:Ss,backendName:"webgl",kernelFunc:I1};var oit=Od+`
|
|
return max(a, b);
|
|
`,sit=`
|
|
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);
|
|
`+jn+`
|
|
return result;
|
|
`,iit=ue({opSnippet:oit,packedOpSnippet:sit,cpuKernelImpl:AL}),wB={kernelName:Ns,backendName:"webgl",kernelFunc:iit};function ait(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;wi(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 Ci(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var IB={kernelName:ks,backendName:"webgl",kernelFunc:ait};function lit(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 Xu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var CB={kernelName:Oi,backendName:"webgl",kernelFunc:lit};var qI=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 * ${i} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},KI=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 uit(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 Xu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new KI(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var vB={kernelName:nu,backendName:"webgl",kernelFunc:uit};function cit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;wi([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 Ci(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new qI(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var SB={kernelName:Wp,backendName:"webgl",kernelFunc:cit};function NB(r,t,e,n){let o=new Ci(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Ci(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var kB={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]=NB(n,a,c,u);return[p,m]}};function TB(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=Xn(a,"float32","mean",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var _B={kernelName:Ts,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=ju(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=TB(d,g,x,i);for(let w of f)i.disposeIntermediateTensorInfo(w);return b}};function pit(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=Xn(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 EB={kernelName:_s,backendName:"webgl",kernelFunc:pit};var mit=Od+`
|
|
return min(a, b);
|
|
`,fit=`
|
|
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);
|
|
`+jn+`
|
|
return result;
|
|
`,dit=ue({opSnippet:mit,packedOpSnippet:fit,cpuKernelImpl:DL}),AB={kernelName:Es,backendName:"webgl",kernelFunc:dit};var jI=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 XI=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.);
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};var hit=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XI(n.shape,o,s):new jI(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},DB={kernelName:As,backendName:"webgl",kernelFunc:hit};var git=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,xit=`
|
|
vec4 result = mod(a, b);
|
|
bvec4 isNaN = equal(b, vec4(0.0));
|
|
`+jn+`
|
|
return result;
|
|
`,yit=ue({opSnippet:git,packedOpSnippet:xit}),$B={kernelName:Ya,backendName:"webgl",kernelFunc:yit};var YI=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 bit=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,wit=`
|
|
// 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;
|
|
`,C1=ue({opSnippet:bit,packedOpSnippet:wit,checkOutOfBounds:!0}),RB={kernelName:us,backendName:"webgl",kernelFunc:C1};var FB="return a - b;",v1=ue({opSnippet:FB,packedOpSnippet:FB,supportsComplex:!0,cpuKernelImpl:JL}),OB={kernelName:ti,backendName:"webgl",kernelFunc:v1};function S1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=I1({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=v1({inputs:{a:o,b:l},backend:e}),p=y1({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=C1({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 PB={kernelName:Js,backendName:"webgl",kernelFunc:S1};function Iit(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:S1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new YI(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var MB={kernelName:Za,backendName:"webgl",kernelFunc:Iit};var Cit=yr+`
|
|
return -x;
|
|
`,vit=`
|
|
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 Sit(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=RL(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,vit):o=new zr(n.shape,Cit),e.runWebGLProgram(o,[n],n.dtype)}var LB={kernelName:Pi,backendName:"webgl",kernelFunc:Sit};var Nit=Kr.nonMaxSuppressionV3Impl;function kit(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}=Nit(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var zB={kernelName:Qa,backendName:"webgl",kernelFunc:kit};var Tit=Kr.nonMaxSuppressionV4Impl;function _it(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}=Tit(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var BB={kernelName:tl,backendName:"webgl",kernelFunc:_it};var Eit=Kr.nonMaxSuppressionV5Impl;function Ait(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}=Eit(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var VB={kernelName:el,backendName:"webgl",kernelFunc:Ait};var ZI=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 Dit=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 ZI(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},GB={kernelName:$s,backendName:"webgl",kernelFunc:Dit};function fg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Gl({inputs:{input:n},backend:e}),s=fg({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=fg({inputs:{x:i},backend:e}),u=$n({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 WB={kernelName:Hi,backendName:"webgl",kernelFunc:fg};function UB(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=UB({inputs:{x:o},backend:e}),i=bp({inputs:{input:n},backend:e}),a=fg({inputs:{x:i},backend:e}),u=$n({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 HB={kernelName:Mi,backendName:"webgl",kernelFunc:UB};function $it(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return OI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=OI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=x1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var qB={kernelName:Li,backendName:"webgl",kernelFunc:$it};var JI=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 QI=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 N1=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 QI(o.shape,s,i):new JI(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},KB={kernelName:Rs,backendName:"webgl",kernelFunc:N1};var Rit=`
|
|
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);
|
|
`,Fit=`
|
|
// 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);
|
|
`+jn+`
|
|
return result;
|
|
`,Oit=ue({opSnippet:Rit,packedOpSnippet:Fit}),jB={kernelName:Fs,backendName:"webgl",kernelFunc:Oit};function Pit(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}=OL(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=pc(o.dtype),w=Xn(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 XB={kernelName:Ps,backendName:"webgl",kernelFunc:Pit};function Mit(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]=PL(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 YB={kernelName:Hp,backendName:"webgl",kernelFunc:Mit};function Lit(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]=ML(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 ZB={kernelName:qp,backendName:"webgl",kernelFunc:Lit};function zit(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]=LL(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var JB={kernelName:Kp,backendName:"webgl",kernelFunc:zit};var k1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=zL(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},QB={kernelName:ou,backendName:"webgl",kernelFunc:k1};var Bit="return 1.0 / x;",Vit=wt({opSnippet:Bit}),tV={kernelName:Ms,backendName:"webgl",kernelFunc:Vit};var Git=yr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Wit=`
|
|
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;
|
|
`,Uit=wt({opSnippet:Git,packedOpSnippet:Wit}),eV={kernelName:Ls,backendName:"webgl",kernelFunc:Uit};var Hit=yr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,qit=`
|
|
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;
|
|
`,Kit=wt({opSnippet:Hit,packedOpSnippet:qit}),rV={kernelName:Vs,backendName:"webgl",kernelFunc:Kit};var tC=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 eC=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 jit(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 eC(o.shape,u,l,s,i):new tC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var nV={kernelName:Bs,backendName:"webgl",kernelFunc:jit};var rC=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 Xit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new rC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var oV={kernelName:nl,backendName:"webgl",kernelFunc:Xit};var nC=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 oC=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 < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Yit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new oC(o.shape,u,l,s,i):new nC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var sV={kernelName:zs,backendName:"webgl",kernelFunc:Yit};var sC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Zit(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new sC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var iV={kernelName:rl,backendName:"webgl",kernelFunc:Zit};var iC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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 aC=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 Jit(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 aC(o.shape,a):new iC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var aV={kernelName:Gs,backendName:"webgl",kernelFunc:Jit};var lC=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var lV={kernelName:fl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new lC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var Qit=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,tat=wt({opSnippet:Qit}),uV={kernelName:Ws,backendName:"webgl",kernelFunc:tat};var eat="return inversesqrt(x);",rat=wt({opSnippet:eat,cpuKernelImpl:BL}),cV={kernelName:Us,backendName:"webgl",kernelFunc:rat};var Yu=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${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 uC=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 nat(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 uC(u,a,f.shape.length,d.shape.length,c,m):g=new Yu(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 pV={kernelName:ol,backendName:"webgl",kernelFunc:nat};var cC=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 oat(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new cC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var mV={kernelName:il,backendName:"webgl",kernelFunc:oat};var pC=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 sat(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new pC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],lr(o.dtype,s.dtype))}var fV={kernelName:Bi,backendName:"webgl",kernelFunc:sat};var iat=`
|
|
// 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);
|
|
`,aat=wt({opSnippet:iat}),dV={kernelName:Hs,backendName:"webgl",kernelFunc:aat};var lat=zo+`
|
|
return 1.0 / (1.0 + exp(-1.0 * x));
|
|
`,uat=`
|
|
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;
|
|
`,cat=wt({opSnippet:lat,packedOpSnippet:uat,cpuKernelImpl:GL}),hV={kernelName:js,backendName:"webgl",kernelFunc:cat};var pat=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,mat=wt({opSnippet:pat}),gV={kernelName:Ks,backendName:"webgl",kernelFunc:mat};var fat=zo+`
|
|
return sin(x);
|
|
`,dat=`
|
|
vec4 result = sin(x);
|
|
bvec4 isNaN = isnan(x);
|
|
${jn}
|
|
return result;
|
|
`,hat=wt({opSnippet:fat,packedOpSnippet:dat}),xV={kernelName:qs,backendName:"webgl",kernelFunc:hat};var gat=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,xat=wt({opSnippet:gat}),yV={kernelName:al,backendName:"webgl",kernelFunc:xat};var yat=`
|
|
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;
|
|
`,bat=wt({opSnippet:yat}),bV={kernelName:Xs,backendName:"webgl",kernelFunc:bat};var wat=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=N1({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},wV={kernelName:Gi,backendName:"webgl",kernelFunc:wat};function Iat(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]=UL(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 IV={kernelName:su,backendName:"webgl",kernelFunc:Iat};function Cat(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]=HL(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var CV={kernelName:ll,backendName:"webgl",kernelFunc:Cat};function vat(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]=qw(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var vV={kernelName:iu,backendName:"webgl",kernelFunc:vat};function Sat(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]=qw(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var SV={kernelName:au,backendName:"webgl",kernelFunc:Sat};function Nat(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=VL(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Yu(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 NV={kernelName:ul,backendName:"webgl",kernelFunc:Nat};function kat(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=vi({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var kV={kernelName:Wi,backendName:"webgl",kernelFunc:kat};var TV="return sqrt(x);",Tat=wt({opSnippet:TV,packedOpSnippet:TV,cpuKernelImpl:qL}),_V={kernelName:Ys,backendName:"webgl",kernelFunc:Tat};var _at="return x * x;",Eat=wt({opSnippet:_at}),EV={kernelName:lu,backendName:"webgl",kernelFunc:Eat};var AV="return (a - b) * (a - b);",Aat=ue({opSnippet:AV,packedOpSnippet:AV}),DV={kernelName:Qs,backendName:"webgl",kernelFunc:Aat};function Dat(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=KL(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var $V={kernelName:oc,backendName:"webgl",kernelFunc:Dat};function $at({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=yr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var RV={kernelName:xo,backendName:"webgl",kernelFunc:$at};var mC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${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 Rat(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=vi({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=jL(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new mC(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 FV={kernelName:cl,backendName:"webgl",kernelFunc:Rat};function Fat(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]=XL(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var OV={kernelName:uu,backendName:"webgl",kernelFunc:Fat};function Oat(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]=YL(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 PV={kernelName:cu,backendName:"webgl",kernelFunc:Oat};function Pat(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=ZL(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var MV={kernelName:pu,backendName:"webgl",kernelFunc:Pat};var Mat="return tan(x);",Lat=wt({opSnippet:Mat}),LV={kernelName:ei,backendName:"webgl",kernelFunc:Lat};var zat=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Bat=wt({opSnippet:zat}),zV={kernelName:ri,backendName:"webgl",kernelFunc:Bat};function Vat(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 Yu(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 BV={kernelName:sl,backendName:"webgl",kernelFunc:Vat};var fC=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=Gat(t);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function Gat(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 T1(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=QL(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new fC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var VV={kernelName:no,backendName:"webgl",kernelFunc:T1};var dC=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));
|
|
}
|
|
}
|
|
`}},hC=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 GV(r){let t=1;for(;t<r;)t*=2;return t}function Wat(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]=tz(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=GV(s),b=GV(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(P,V,G)=>{let W=I(),q=new dC(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 hC([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=vi({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),Ip(e,E);let A=w1({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 WV={kernelName:pl,backendName:"webgl",kernelFunc:Wat};var gC=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 Uat(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 gC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],"float32")}var UV={kernelName:ml,backendName:"webgl",kernelFunc:Uat};function Hat(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;wi(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}=ez(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var HV={kernelName:mu,backendName:"webgl",kernelFunc:Hat};function qat(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=vi({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 qV={kernelName:Ui,backendName:"webgl",kernelFunc:qat};var xC=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 Kat(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=pc(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 xC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=k1({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),K=T1({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 KV={kernelName:fu,backendName:"webgl",kernelFunc:Kat};var jat=[Az,$z,Rz,Fz,Pz,Mz,Lz,zz,Gz,Wz,Uz,Hz,qz,Kz,jz,Xz,Yz,Zz,Jz,Qz,t3,r3,n3,o3,l3,c3,p3,wz,f3,h3,g3,x3,y3,b3,w3,I3,C3,v3,S3,T3,_3,E3,A3,D3,$3,R3,F3,O3,P3,M3,L3,z3,B3,V3,G3,U3,H3,q3,K3,X3,Y3,Z3,J3,Q3,tB,eB,rB,nB,bz,oB,d3,sB,iB,aB,Iz,lB,uB,cB,pB,mB,fB,dB,hB,gB,xB,bB,wB,IB,CB,vB,SB,kB,_B,EB,AB,DB,$B,MB,Sz,LB,zB,BB,VB,s3,GB,HB,qB,KB,jB,Cz,XB,YB,ZB,JB,QB,i3,RB,tV,eV,rV,kz,nV,oV,sV,iV,aV,lV,uV,cV,pV,mV,fV,dV,hV,gV,xV,yV,e3,PB,bV,wV,IV,CV,vV,SV,NV,kV,_V,EV,DV,$V,RV,FV,OV,PV,MV,OB,_z,LV,zV,BV,VV,WV,UV,Ez,HV,qV,KV,WB];for(let r of jat)sc(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 Zu;(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"})(Zu||(Zu={}));var jV;function Xat(r){jV=r.wasm.cwrap(qi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Yat(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 ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Zu[c];if(g==null)throw new Error(`${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 jV(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var XV={kernelName:qi,backendName:"wasm",setupFunc:Xat,kernelFunc:Yat};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 YV=Ct(Ti);var ZV=Ct(Uo);var JV=Ct(Ho);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 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and transposeB=${a} must match.`);let I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=pr({inputs:{x:o},backend:e,attrs:{shape:I}}),A=pr({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 vG(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 SG={kernelName:Qo,backendName:"wasm",setupFunc:flt,kernelFunc:dlt};function Bo(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)hlt(u,c[0],m,s,i);else if(f===3)glt(u,c[0],c[1],m,s,i);else if(f===4)xlt(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 hlt(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 glt(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 xlt(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 NG={kernelName:Vi,backendName:"wasm",kernelFunc:Bo};function ylt(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=pr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=mo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=pr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Bo({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 kG={kernelName:Di,backendName:"wasm",kernelFunc:ylt};var TG;function blt(r){TG=r.wasm.cwrap(Da,null,["number","number","boolean","number","number","number"])}function wlt(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 TG(c(o),i,a,c(s),kt[s.dtype],c(l)),l}var _G={kernelName:Da,backendName:"wasm",setupFunc:blt,kernelFunc:wlt};function Ilt(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 EG={kernelName:Yl,backendName:"wasm",kernelFunc:Ilt};function Rn(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 AG={kernelName:fo,backendName:"wasm",kernelFunc:Rn};var DG=Ct(ts);var $G;function Clt(r){$G=r.wasm.cwrap(ho,null,["number","number","number","number"])}function vlt(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 $G(a,s,i,l),u}var RG={kernelName:ho,backendName:"wasm",setupFunc:Clt,kernelFunc:vlt};function _1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return Cp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return pr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=rp(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f<u;f++){let d=f*l;for(let h=0;h<p.length;h++){let g=c[h],x=f*g,b=p[h].subarray(x,x+g);m.set(b,d),d+=g}}return a}var FG={kernelName:$i,backendName:"wasm",kernelFunc:_1};var OG;function Slt(r){OG=r.wasm.cwrap(es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nlt(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 OG(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 PG={kernelName:es,backendName:"wasm",setupFunc:Slt,kernelFunc:Nlt};var MG;function klt(r){MG=r.wasm.cwrap(rs,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 Tlt(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 MG(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 LG={kernelName:rs,backendName:"wasm",setupFunc:klt,kernelFunc:Tlt};var zG;function _lt(r){zG=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"])}function Elt(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 ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=e.makeOutput(l.outShape,o.dtype);return zG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var BG={kernelName:ns,backendName:"wasm",setupFunc:_lt,kernelFunc:Elt};var VG;function Alt(r){VG=r.wasm.cwrap($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 Dlt(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 ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,u,i,1,a),c=e.makeOutput(l.filterShape,s.dtype);return VG(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var GG={kernelName:$a,backendName:"wasm",setupFunc:Alt,kernelFunc:Dlt};var WG;function $lt(r){WG=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 Rlt(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 ${s.dtype}`);let l=S.computeConv3DInfo(u,s.shape,a,1,i),c=e.makeOutput(l.inShape,o.dtype);return WG(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var UG={kernelName:Ra,backendName:"wasm",setupFunc:$lt,kernelFunc:Rlt};var HG=Ct(os);var qG=Ct(ss);var E1;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(E1||(E1={}));var KG;function Flt(r){KG=r.wasm.cwrap(Oa,null,["number","number","number","number","array","number","number","number","number","number"])}function Olt(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=Rn({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 KG(g,x,b,c,N,p,m,E1[o],s,I),h!=null&&t.disposeData(h.dataId),w}var jG={kernelName:Oa,backendName:"wasm",setupFunc:Flt,kernelFunc:Olt};var XG;function Plt(r){XG=r.wasm.cwrap(Fa,null,["number","number","number","number","number","number"])}function Mlt(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=mo({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;XG(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var YG={kernelName:Fa,backendName:"wasm",setupFunc:Plt,kernelFunc:Mlt};var ZG;function Llt(r){ZG=r.wasm.cwrap(is,null,["number","number","number","number","number","number"])}function zlt(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=mo({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;ZG(d,i?1:0,a?1:0,f,h,kt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var JG={kernelName:is,backendName:"wasm",setupFunc:Llt,kernelFunc:zlt};var QG;function Blt(r){QG=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function Vlt(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 QG(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),kt[s.dtype],a,p(c)),c}var tW={kernelName:Jl,backendName:"wasm",setupFunc:Blt,kernelFunc:Vlt};var eW;function Glt(r){eW=r.wasm.cwrap(Pa,null,["number","number","number","array","number","array","array","number","number"])}function Wlt(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 eW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var rW={kernelName:Pa,backendName:"wasm",setupFunc:Glt,kernelFunc:Wlt};var nW;function Ult(r){nW=r.wasm.cwrap(as,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}=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 nW(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 oW={kernelName:as,backendName:"wasm",setupFunc:Ult,kernelFunc:Hlt};var sW;function qlt(r){sW=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function Klt(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 sW(e.dataIdMap.get(n.dataId).id,kt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var iW={kernelName:Ql,backendName:"wasm",setupFunc:qlt,kernelFunc:Klt};var aW;function jlt(r){aW=r.wasm.cwrap(ls,null,["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!==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 aW(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 lW={kernelName:ls,backendName:"wasm",setupFunc:jlt,kernelFunc:Xlt};var uW;function Ylt(r){uW=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 Zlt(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 uW(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 cW={kernelName:eu,backendName:"wasm",setupFunc:Ylt,kernelFunc:Zlt};var pW;function Jlt(r){pW=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 Qlt(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 ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return pW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,kt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var mW={kernelName:tu,backendName:"wasm",setupFunc:Jlt,kernelFunc:Qlt};var fW=Ct(cs);var dW;function tut(r){dW=r.wasm.cwrap(Ma,null,["number","number","number"])}function eut(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return dW(i(o),i(n),i(s)),s}var hW={kernelName:Ma,backendName:"wasm",setupFunc:tut,kernelFunc:eut};var rut=!1,gW=ae(za,rut,"bool");var xW=Ct(ps,"float32");function bC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),pr({inputs:{x:o},backend:n,attrs:{shape:a}})}var yW={kernelName:Ri,backendName:"wasm",kernelFunc:bC};var bW=Ct(ms,"float32");function A1(r){let{attrs:{shape:t,value:e,dtype:n},backend:o}=r,s=o.makeOutput(t,n);return o.typedArrayFromHeap(s).fill(e),s}var wW={kernelName:ru,backendName:"wasm",kernelFunc:A1};var IW;function nut(r){IW=r.wasm.cwrap(Ba,null,["number","number","number","number","number","number"])}function out(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return IW(s,a,u,l,c,i),o}var CW={kernelName:Ba,backendName:"wasm",kernelFunc:out,setupFunc:nut};var vW=Ct(fs);var sut=!1,SW=ae(ds,sut);var NW;function iut(r){NW=r.wasm.cwrap(hs,null,["number","number","number","number","number","number","number"])}function aut(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return NW(c,p,m,f,d,o,g),h}var kW={kernelName:hs,backendName:"wasm",setupFunc:iut,kernelFunc:aut};var TW;function lut(r){TW=r.wasm.cwrap(Ki,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function uut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=Zu[f];if(g==null)throw new Error(`${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}'. 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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 EW(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 AW={kernelName:ji,backendName:"wasm",setupFunc:cut,kernelFunc:put};var DW;function mut(r){DW=r.wasm.cwrap(Va,null,["number","number","number","number","number","number","array","number"])}function fut(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ny.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 DW(f,kt[n.dtype],h,i,p,a,g,x),l}var $W={kernelName:Va,backendName:"wasm",setupFunc:mut,kernelFunc:fut};var RW;function dut(r){RW=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function hut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F<l.length;++F){let P=l[F];y.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=pr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=pr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return 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Aut(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;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return tU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var eU={kernelName:Xa,backendName:"wasm",setupFunc:Eut,kernelFunc:Aut};var rU;function Dut(r){rU=r.wasm.cwrap(Ss,null,["number","number","number","number"])}function $ut(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=In(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;rU(u,kt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var nU={kernelName:Ss,backendName:"wasm",setupFunc:Dut,kernelFunc:$ut};var Rut=!1,oU=ae(Ns,Rut);var sU;function Fut(r){sU=r.wasm.cwrap(ks,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Out(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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aU(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 lU={kernelName:Oi,backendName:"wasm",setupFunc:Put,kernelFunc:Mut};var uU;function Lut(r){uU=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 zut(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 uU(e.dataIdMap.get(s.dataId).id,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 cU={kernelName:nu,backendName:"wasm",setupFunc:Lut,kernelFunc:zut};var pU;function But(r){pU=r.wasm.cwrap(Ts,null,["number, number, number"])}function Vut(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}=In(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("mean",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!=="float32"&&(b=Rn({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,"float32");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;pU(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!=="float32"&&t.disposeData(b.dataId),w}var mU={kernelName:Ts,backendName:"wasm",setupFunc:But,kernelFunc:Vut};var fU;function Gut(r){fU=r.wasm.cwrap(_s,null,["number","number","number","number"])}function Wut(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}=In(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let d=l.shape.length;S.assertAxesAreInnerMostDims("min",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;fU(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 dU={kernelName:_s,backendName:"wasm",setupFunc:Gut,kernelFunc:Wut};var Uut=!1,hU=ae(Es,Uut);var D1;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(D1||(D1={}));var gU;function Hut(r){gU=r.wasm.cwrap(As,null,["number","array","number","number","array","array","number","number"])}function qut(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new 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Qut(r){kU=r.wasm.cwrap(tl,"number",["number","number","number","number","number","bool"])}function tct(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=kU(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Wd(t,m);t.wasm._free(h);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([],"int32",g);return[x,b]}var TU={kernelName:tl,backendName:"wasm",setupFunc:Qut,kernelFunc:tct};var _U;function ect(r){_U=r.wasm.cwrap(el,"number",["number","number","number","number","number","number"])}function rct(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=_U(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Wd(t,m);t.wasm._free(g);let 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wC={kernelName:Rs,backendName:"wasm",kernelFunc:uct,setupFunc:lct};var cct=!1,PU=ae(Fs,cct);var MU;function pct(r){MU=r.wasm.cwrap(Os,null,["number","number","number"])}function mct(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=Rn({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 MU(a,i,p),u.dtype!=="float32"&&e.disposeData(l.dataId),c}var LU={kernelName:Os,backendName:"wasm",setupFunc:pct,kernelFunc:mct};var zU;function fct(r){zU=r.wasm.cwrap(Ps,null,["number","number","number","number"])}function dct(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}=In(i,o,t),d=p;if(f){let 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Sct(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=e.makeOutput(o.shape,"float32"),u=e.dataIdMap.get(o.dataId),l;return u.dtype!=="float32"&&(l=Rn({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),u=e.dataIdMap.get(l.dataId)),JU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,new Uint8Array(new Int32Array(o.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),l!=null&&e.disposeData(l.dataId),a}var QU={kernelName:rl,backendName:"wasm",setupFunc:vct,kernelFunc:Sct};var t4;function Nct(r){t4=r.wasm.cwrap(Gs,null,["number","array","number","array","number","number"])}function kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=y.parseAxisParam(s,o.shape);if(o.shape.length===0)return Cp({inputs:{x:o},backend:e});let a=e.makeOutput(o.shape,o.dtype),u=e.dataIdMap.get(o.dataId).id,l=e.dataIdMap.get(a.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(o.shape).buffer);t4(u,c,i.length,p,o.shape.length,l);let m=pr({inputs:{x:a},attrs:{shape:o.shape},backend:e});return e.disposeData(a.dataId),m}var e4={kernelName:Gs,backendName:"wasm",kernelFunc:kct,setupFunc:Nct};var r4;function Tct(r){r4=r.wasm.cwrap(fl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function _ct(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{radians:s,fillValue:i,center:a}=n,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(o.dataId).id,c=e.dataIdMap.get(u.dataId).id,[p,m,f,d]=o.shape,[h,g]=S.getImageCenter(a,m,f),x=i===0,b=255,w=typeof i=="number"?[i,i,i,x?0:b]:[...i,b],I=new Uint8Array(new Int32Array(w).buffer);return r4(l,p,m,f,d,s,h,g,I,w.length,c),u}var n4={kernelName:fl,backendName:"wasm",kernelFunc:_ct,setupFunc:Tct};var o4=Ct(Ws);var s4=Ct(Us);var i4;function Ect(r){i4=r.wasm.cwrap(ol,null,["number","number","number","number","number","number","array","number","number"])}function Act(r){let{backend:t,inputs:e,attrs:n}=r,{indices:o,updates:s}=e,{shape:i}=n,a=t.makeOutput(i,s.dtype);if(y.sizeFromShape(i)===0)return a;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=$u.calculateShapes(s,o,i),d=t.dataIdMap.get(o.dataId).id,g=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(p).buffer),b=t.dataIdMap.get(a.dataId).id;return i4(d,g,kt[s.dtype],u,l,c,x,m,b),a}var a4={kernelName:ol,backendName:"wasm",setupFunc:Ect,kernelFunc:Act};var l4;function Dct(r){l4=r.wasm.cwrap(il,null,["number","number","number","number","number","number","bool","number"])}function $ct(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n;if(o.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${o.dtype} and ${s.dtype}`);let a=e.makeOutput(s.shape,"int32");function u(l){return e.dataIdMap.get(l.dataId).id}return l4(u(o),u(s),o.shape[0],o.shape[1],s.shape[1],kt[o.dtype],i==="left",u(a)),a}var u4={kernelName:il,backendName:"wasm",setupFunc:Dct,kernelFunc:$ct};var c4;function Rct(r){c4=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Fct(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=e.dataIdMap.get(n.dataId).id,a=e.dataIdMap.get(o.dataId).id,u=e.dataIdMap.get(s.dataId).id,l=e.makeOutput(o.shape,o.dtype),c=e.dataIdMap.get(l.dataId).id,p=n.shape.length,m=o.shape.length,f=p===0||p>1||m===1?1:y.sizeFromShape(o.shape.slice(1));return c4(i,a,u,f,c),l}var p4={kernelName:Bi,backendName:"wasm",kernelFunc:Fct,setupFunc:Rct};var m4=Ct(Hs);var f4;function Oct(r){f4=r.wasm.cwrap(js,null,["number","number"])}function Pct(r){let{backend:t,inputs:{x:e}}=r,n=t.dataIdMap.get(e.dataId).id,o=t.makeOutput(e.shape,e.dtype),s=t.dataIdMap.get(o.dataId).id;return y.sizeFromShape(o.shape)===0||f4(n,s),o}var d4={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Oct,kernelFunc:Pct};var h4=Ct(Ks);var g4=Ct(qs);var x4=Ct(Xs);function Mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n,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=wC.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=pr({inputs:{x:l},backend:e,attrs:{shape:c}}),b=mo({inputs:{x:h},backend:e,attrs:{perm:p}}),N=pr({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeData(l.dataId),e.disposeData(h.dataId),e.disposeData(b.dataId),N}var y4={kernelName:Gi,backendName:"wasm",kernelFunc:Mct};var b4;function Lct(r){b4=r.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function zct(r){let{backend:t,inputs:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=e,a=n.shape[0],u=n.shape[1],l=t.readSync(s.dataId)[0],c=[a+l,u],p=t.dataIdMap.get(n.dataId).id,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(i.dataId).id,d=t.makeOutput(c,n.dtype),h=t.dataIdMap.get(d.dataId).id,g=t.makeOutput(c.slice(0,1),o.dtype),x=t.dataIdMap.get(g.dataId).id,b=t.makeOutput([l],"bool"),w=t.dataIdMap.get(b.dataId).id,I=t.makeOutput([a],n.dtype),N=t.dataIdMap.get(I.dataId).id,E=t.makeOutput([4],"int32"),A=t.dataIdMap.get(E.dataId).id,D=b4(p,m,kt[o.dtype],a,l,u,f,h,x,w,N,A),F=t.readSync(E.dataId),P;switch(F[0]){case 1:{P=S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(F[1]);break}case 2:{P=S.getSparseFillEmptyRowsNegativeIndexErrorMessage(F[1],F[2]);break}case 3:P=S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(F[1],F[2],F[3]);break;default:P=""}if(t.disposeData(E.dataId),P)throw t.disposeData(d.dataId),t.disposeData(g.dataId),t.disposeData(b.dataId),t.disposeData(I.dataId),new Error(P);let V=d,G=g;return D!==c[0]&&(V=Bo({inputs:{x:d},attrs:{begin:0,size:[D,u]},backend:t}),G=Bo({inputs:{x:g},attrs:{begin:0,size:D},backend:t}),t.disposeData(d.dataId),t.disposeData(g.dataId)),[V,G,b,I]}var w4={kernelName:su,backendName:"wasm",setupFunc:Lct,kernelFunc:zct};var I4;function Bct(r){I4=r.wasm.cwrap(ll,null,["number","number","number","number","number","number","number"])}function Vct(r){let{backend:t,inputs:e}=r,{inputIndices:n,inputShape:o,newShape:s}=e;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=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;I4(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 C4={kernelName:ll,backendName:"wasm",setupFunc:Bct,kernelFunc:Vct};var v4;function IC(r){v4=r.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function CC(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;v4(m,kt[o.dtype],o.shape[0],f,d,g,b,t,0);let 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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 k4(d,h,s.shape.length,g,kt[i.dtype],l,c,p,x,f,b),u}var T4={kernelName:ul,backendName:"wasm",setupFunc:Uct,kernelFunc:Hct};function qct(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=Bo({inputs:{x:o},attrs:{begin:l,size:m},backend:n});return l[a]+=p,f})}var _4={kernelName:Wi,backendName:"wasm",kernelFunc:qct};var E4=Ct(Ys);var A4=Ct(lu);var Kct=!0,D4=ae(Qs,Kct);var $4;function jct(r){$4=r.wasm.cwrap(xo,null,["number","number","number","number"])}function 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hpt=[XV,YV,ZV,JV,QV,eG,iG,lG,uG,cG,pG,mG,fG,dG,hG,xG,bG,IG,SG,kG,_G,EG,AG,DG,RG,FG,PG,LG,BG,GG,UG,HG,qG,jG,YG,JG,tW,rW,oW,iW,lW,cW,mW,fW,hW,gW,xW,yW,bW,wW,CW,vW,SW,kW,_W,AW,$W,FW,OW,PW,rG,MW,LW,zW,VW,GW,WW,HW,KW,qW,jW,XW,YW,ZW,QW,eU,nU,oU,iU,lU,cU,mU,dU,hU,xU,IU,CU,vU,NU,TU,EU,AU,$U,RU,FU,wC,PU,LU,BU,VU,GU,WU,UU,HU,CG,KU,XU,ZU,QU,e4,n4,o4,s4,a4,u4,p4,m4,d4,h4,g4,NG,bU,x4,y4,w4,C4,S4,N4,T4,_4,E4,A4,D4,R4,O4,P4,M4,L4,z4,V4,G4,W4,H4,K4,X4,Z4,oG,J4,Q4,tH];for(let r of hpt)sc(r);var R1=L();R1.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}});R1.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(R1.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 ${e}`)}}};function xpt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function uH(r,t,e){if(NC!=null)return NC;let n="tfjs-backend-wasm.wasm";return r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),hg!=null&&hg[n]!=null?hg[n]:e+n}async function pH(){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=cH.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?uH(r,t,dg!=null?dg:u):u+a},G1&&(o.instantiateWasm=xpt(uH(r,t,dg!=null?dg:"")));let s=!1;o.onAbort=()=>{if(s||gg)return;gg=!0,n({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}G1=t}var mH=-1,z1=-1;function Cpt(r){mH=r}function vpt(){if(z1===-1)throw new Error("WASM backend not initialized.");return z1}var Spt="4.3.0";var Npt=2;sm("wasm",async()=>{let{wasm:r}=await pH();return new xg(r)},Npt);var fH="4.3.0",kpt="4.3.0",Tpt="4.3.0",_pt="4.3.0",Ept="4.3.0",Apt={tfjs:fH,"tfjs-core":fH,"tfjs-converter":kpt,"tfjs-backend-cpu":Tpt,"tfjs-backend-webgl":_pt,"tfjs-backend-wasm":Ept};export{Ti as Abs,Uo as Acos,Ho as Acosh,kc as AdadeltaOptimizer,Tc as AdagradOptimizer,_c as AdamOptimizer,Ec as AdamaxOptimizer,ro as Add,qo as AddN,Ea as All,Aa as Any,_i as ArgMax,Ei as ArgMin,Ko as Asin,jo as Asinh,Xo as Atan,Zo as Atan2,Yo as Atanh,Jo as AvgPool,Ai as AvgPool3D,Xl as AvgPool3DGrad,Fp as AvgPoolGrad,xg as BackendWasm,Qo as BatchMatMul,Di as BatchToSpaceND,Da as Bincount,Yl as BroadcastArgs,a_ as BroadcastTo,Db as Callback,Uy as CallbackList,fo as Cast,ts as Ceil,ho as ClipByValue,Op as Complex,Zl as ComplexAbs,$i as Concat,es as Conv2D,Pp as Conv2DBackpropFilter,rs as Conv2DBackpropInput,ns as Conv3D,$a as Conv3DBackpropFilterV2,Ra as Conv3DBackpropInputV2,os as Cos,ss as Cosh,Oa as CropAndResize,Fa as Cumprod,is as Cumsum,qy as CustomCallback,Ta as DataStorage,Jl as DenseBincount,Pa as DepthToSpace,as as DepthwiseConv2dNative,Mp as DepthwiseConv2dNativeBackpropFilter,Lp as DepthwiseConv2dNativeBackpropInput,Ql as Diag,ls as Dilation2D,eu as Dilation2DBackpropFilter,tu as Dilation2DBackpropInput,r0 as ENV,$b as EarlyStopping,zp as Einsum,cs as Elu,Ma as EluGrad,th as Environment,za as Equal,La as Erf,ps as Exp,Ri as ExpandDims,ms as Expm1,Bp as FFT,ru as Fill,Ba as FlipLeftRight,fs as Floor,ds as FloorDiv,rh as FromPixels,hs as FusedBatchNorm,Ki as FusedConv2D,ji as FusedDepthwiseConv2D,hp as GPGPUContext,Va as GatherNd,Fi as GatherV2,Uh as GraphModel,Ga as Greater,gs as GreaterEqual,Hy as History,Vp as IFFT,go as Identity,Gp as Imag,Ie as InputSpec,xs as IsFinite,ys as IsInf,bs as IsNan,Go as KernelBackend,vs as LRN,Xa as LRNGrad,Eh as LayerVariable,Un as LayersModel,ws as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,Is as Log,Cs as Log1p,u_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,l_ as LogicalXor,Ppt as LowerBound,Wu as MathBackendCPU,Ku as MathBackendWebGL,Mpt as MatrixBandPart,Ss as Max,ks as MaxPool,Oi as MaxPool3D,nu as MaxPool3DGrad,Wp as MaxPoolGrad,Up as MaxPoolWithArgmax,Ns as Maximum,Ts as Mean,_s as Min,Es as Minimum,As as MirrorPad,Ya as Mod,Ac as MomentumOptimizer,Za as Multinomial,Ds as Multiply,Pi as Neg,Qa as NonMaxSuppressionV3,tl as NonMaxSuppressionV4,el as NonMaxSuppressionV5,Ja as NotEqual,v0 as OP_SCOPE_SUFFIX,$s as OneHot,Mi as OnesLike,qr as Optimizer,vh as OptimizerConstructors,Li as Pack,Rs as PadV2,Lpt as Pool,Fs as Pow,Os as Prelu,Ps as Prod,Dc as RMSPropOptimizer,_n as RNN,Hp as RaggedGather,qp as RaggedRange,Kp as RaggedTensorToTensor,ou as Range,f0 as Rank,jp as Real,us as RealDiv,Ms as Reciprocal,Ze as Reduction,Ls as Relu,Vs as Relu6,zi as Reshape,Bs as ResizeBilinear,nl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,rl as ResizeNearestNeighborGrad,Gs as Reverse,fl as RotateWithOffset,Ws as Round,Us as Rsqrt,Cl as SGDOptimizer,ol as ScatterNd,il as SearchSorted,Bi as Select,Hs as Selu,xa as Sequential,js as Sigmoid,Ks as Sign,qs as Sin,al as Sinh,Vi as Slice,Js as Softmax,Xs as Softplus,Gi as SpaceToBatchND,su as SparseFillEmptyRows,ll as SparseReshape,iu as SparseSegmentMean,au as SparseSegmentSum,ul as SparseToDense,Wi as SplitV,Ys as Sqrt,lu as Square,Qs as SquaredDifference,oc as StaticRegexReplace,xo as Step,cl as StridedSlice,uu as StringNGrams,cu as StringSplit,pu as StringToHashBucketFast,ti as Sub,Zs as Sum,rn as SymbolicTensor,ei as Tan,ri as Tanh,Pt as Tensor,le as TensorBuffer,sl as TensorScatterUpdate,no as Tile,pl as TopK,ml as Transform,oo as Transpose,mu as Unique,Ui as Unpack,fu as UnsortedSegmentSum,zpt as UpperBound,dl as Variable,Hi as ZerosLike,qi as _FusedMatMul,Ee as abs,ux as acos,cx as acosh,Y as add,sE as addN,am as all,fc as any,Qi as argMax,px as argMin,mx as asin,fx as asinh,dx as atan,hx as atan2,gx as atanh,bu as avgPool,xx as avgPool3d,oE as backend,S as backend_util,lE as basicLSTMCell,ra as batchNorm,yx as batchNorm2d,bx as batchNorm3d,wx as batchNorm4d,wu as batchToSpaceND,Ix as bincount,jX as booleanMaskAsync,cE as broadcastArgs,na as broadcastTo,Ur as broadcast_util,Sy as browser,bt as buffer,lQ as callbacks,J as cast,Cx as ceil,vr as clipByValue,ln as clone,vn as complex,oe as concat,vx as concat1d,Sx as concat2d,Nx as concat3d,kx as concat4d,q$ as constraints,um as conv1d,Sn as conv2d,pm as conv2dTranspose,Tx as conv3d,Ex as conv3dTranspose,qpt as copyRegisteredKernels,Iu as cos,mm as cosh,bh as cosineWindow,hc as cumprod,fm as cumsum,pn as customGrad,EF as data,dh as denseBincount,R0 as deprecationWarn,Ax as depthToSpace,oa as depthwiseConv2d,mQ as deregisterOp,xu as device_util,pE as diag,Dx as dilation2d,tdt as disableDeprecationWarnings,Tt as dispose,edt as disposeVariables,ut as div,$x as divNoNan,Rx as dot,Y0 as dropout,fE as einsum,sa as elu,Qft as enableDebugMode,Jft as enableProdMode,Z0 as enclosingPowerOfTwo,Bn as engine,L as env,Rr as equal,Fx as erf,Ox as euclideanNorm,sr as exp,ir as expandDims,Px as expm1,gc as eye,Au as fft,Co as fill,adt as findBackend,ldt as findBackendFactory,ia as floor,im as floorDiv,yz as forceHalfFloat,Ru as fused,aa as gather,o5 as gatherND,Ny as gather_util,sdt as getBackend,s0 as getGradient,oh as getKernel,Kg as getKernelsForBackend,vpt as getThreadsCount,s1 as gpgpu_util,Jj as grad,Qj as grads,Fe as greater,cn as greaterEqual,Il as ifft,Cu as imag,lo as image,a5 as inTopKAsync,K$ as initializers,RN as input,Mr as io,km as irfft,Mx as isFinite,Lx as isInf,zx as isNaN,$e as keep,Kr as kernel_impls,kR as layers,vu as leakyRelu,bl as less,Vn as lessEqual,Q0 as linalg,gE as linspace,l9 as loadGraphModel,u9 as loadGraphModelSync,fR as loadLayersModel,Bx as localResponseNormalization,Nr as log,Su as log1p,Wx as logSigmoid,dm as logSoftmax,hm as logSumExp,Or as logicalAnd,Nu as logicalNot,gm as logicalOr,Ux as logicalXor,l8 as losses,xE as lowerBound,Bt as matMul,a2 as math,Sr as max,ku as maxPool,qx as maxPool3d,yE as maxPoolWithArgmax,Nn as maximum,ke as mean,ph as memory,bE as meshgrid,TR as metrics,xl as min,ao as minimum,Kx as mirrorPad,jx as mod,c7 as model,_R as models,xc as moments,ZX as movingAverage,$ as mul,wE as multiRNNCell,IE as multinomial,Ut as neg,Sh as nextFrame,yl as norm,ai as notEqual,la as oneHot,dr as ones,Ir as onesLike,k as op,CE as outerProduct,mn as pad,vE as pad1d,SE as pad2d,NE as pad3d,kE as pad4d,Xx as pool,un as pow,_u as prelu,lx as print,Yx as prod,rdt as profile,TE as raggedGather,_E as raggedRange,EE as raggedTensorToTensor,AE as rand,ZE as randomGamma,wc as randomNormal,JE as randomStandardNormal,ua as randomUniform,ca as range,odt as ready,wl as real,ry as reciprocal,sm as registerBackend,m7 as registerCallbackConstructor,p_ as registerGradient,sc as registerKernel,pQ as registerOp,ER as regularizers,Pr as relu,xm as relu6,idt as removeBackend,R as reshape,hr as reverse,QE as reverse1d,tA as reverse2d,eA as reverse3d,rA as reverse4d,Du as rfft,ym as round,bm as rsqrt,ft as scalar,QX as scatterND,$u as scatter_util,gh as searchSorted,wm as selu,Im as separableConv2d,p7 as sequential,Q as serialization,iK as setBackend,udt as setPlatform,Cpt as setThreadsCount,wpt as setWasmPath,Ipt as setWasmPaths,bT as setWebGLContext,nA as setdiff1dAsync,bw as shared,tn as sigmoid,ny as sign,a8 as signal,Cm as sin,vm as sinh,Ot as slice,Sm as slice1d,yh as slice2d,Nm as slice3d,Ic as slice4d,ze as slice_util,Eu as softmax,ii as softplus,Tu as spaceToBatchND,u8 as sparse,r5 as sparseToDense,i8 as spectral,gr as split,Ne as sqrt,Gt as square,Tm as squaredDifference,Gn as squeeze,qe as stack,So as step,oy as stridedSlice,c8 as string,lt as sub,pt as sum,pc as sumOutType,sy as tan,ea as tanh,or as tensor,Ke as tensor1d,li as tensor2d,iy as tensor3d,oA as tensor4d,sA as tensor5d,iA as tensor6d,lA as tensorScatterUpdate,Io as tensor_util,YE as test_util,B as tidy,Fr as tile,ndt as time,ay as topk,Rc as train,Vt as transpose,Em as truncatedNormal,ly as unique,Hpt as unregisterGradient,Upt as unregisterKernel,Am as unsortedSegmentSum,xr as unstack,lr as upcastType,uA as upperBound,y as util,t6 as valueAndGrad,e6 as valueAndGrads,uy as variable,Vx as variableGrads,Apt as version,uF as version_converter,b2 as version_core,dO as version_cpu,tf as version_layers,Spt as version_wasm,xz as version_webgl,n2e as webgl,kd as webgl_util,be as where,py as whereAsync,Te as zeros,vt as zerosLike};
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