mirror of https://github.com/vladmandic/human
5546 lines
1.3 MiB
5546 lines
1.3 MiB
/*
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Human
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,Zs(`Initialization of backend ${e} failed`),Zs(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Zs(`Initialization of backend ${e} failed`),Zs(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return lc.nextTensorId++}nextVariableId(){return lc.nextVariableId++}clone(e){let t=z.runKernel(qa,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return z.runKernel(Da,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!($p(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=tg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(tg(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=$p(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let A=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,y);let x=y.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:k,dtype:S}=b;return this.makeTensorFromDataId(v,k,S)});if(s){let b=this.getTensorsForGradient(h,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,d=tg(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,u,t,d,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Gm(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*zm(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ic||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*zm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of 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a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(M(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(r instanceof qe,()=>"The result y returned by f() must be a tensor.");let a=fC(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Actual: ${r}.
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Expected: ${a}.`);for(let o=0;o<a.length;++o){let i=r[o],l=a[o];if(!n(i,l))throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${l}.
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Actual: ${r}.
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Expected: ${a}.`)}}function U9(e,t){e().then(()=>t.fail(),()=>t())}function H9(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Lr(e)||Lr(e[0])||Lr(t)||Lr(t[0])?xg(e,n,(s,r)=>s==r):xg(e,t,(s,r)=>bg(s,r,0))}function G9(e,t,n){if(n==null&&(n=yg()),!bg(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function bg(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function j9(e,t,n){for(let s=0;s<e.length;s++)if(e[s]<t||e[s]>n)throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`)}function q9(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function D5(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?D5(n):e[t]=sc(n)}return e}var Hp="3.9.0";function F5(){Y().set("PROD",!0)}function X9(){Y().set("DEBUG",!0)}function K9(){Y().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function 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with dtype ${a.dtype}. `)}),n.length===1)return $s(n[0]);let s=n,r={axis:t};return z.runKernel(Oi,s,r)}var gt=W({concat_:NT});function ET(e){let n={x:F(e,"x","sigmoid")};return z.runKernel(go,n)}var Gn=W({sigmoid_:ET});function RT(e,t,n){let s=F(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return z.runKernel(hl,r,a)}var De=W({slice_:RT});function _T(e){let n={x:F(e,"x","tanh")};return z.runKernel(ko,n)}var Ol=W({tanh_:_T});function DT(e,t,n,s,r,a){let o=F(e,"forgetBias","basicLSTMCell"),i=F(t,"lstmKernel","basicLSTMCell"),l=F(n,"lstmBias","basicLSTMCell"),u=F(s,"data","basicLSTMCell"),c=F(r,"c","basicLSTMCell"),d=F(a,"h","basicLSTMCell"),p=gt([u,d],1),h=He(p,i),f=ae(h,l),m=f.shape[0],g=f.shape[1]/4,A=[m,g],y=De(f,[0,0],A),x=De(f,[0,g],A),b=De(f,[0,g*2],A),v=De(f,[0,g*3],A),k=ae(L(Gn(y),Ol(x)),L(c,Gn(ae(o,b)))),S=L(Ol(k),Gn(v));return[k,S]}var FT=W({basicLSTMCell_:DT});function $T(e,t,n){let s=F(e,"x","batchToSpaceND"),r=t.reduce((i,l)=>i*l);M(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),M(s.shape[0]%r==0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return z.runKernel($i,a,o)}var Yp=W({batchToSpaceND_:$T});function OT(e){let t;return e.rank===0||e.rank===1?t=U(e,[1,1,1,e.size]):e.rank===2?t=U(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function PT(e,t,n,s,r,a){a==null&&(a=.001);let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;s!=null&&(c=F(s,"offset","batchNorm")),M(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${u.rank}.`),c!=null&&M(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Pl(o,i,l,c,u,a)}var G5=W({batchNorm2d_:MT});function zT(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(s,"offset","batchNorm")),M(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),M(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Pl(o,i,l,c,u,a)}var j5=W({batchNorm3d_:zT});function LT(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(s,"offset","batchNorm")),M(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),M(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Pl(o,i,l,c,u,a)}var q5=W({batchNorm4d_:LT});function BT(e,t,n){let s=F(e,"x","bincount"),r=F(t,"weights","bincount");M(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),M(n>=0,()=>`size must be non-negative, but got 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Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);let r={s0:n,s1:s};return z.runKernel(Hm,r)}var X5=W({broadcastArgs_:WT});function VT(e,t){let n=F(e,"broadcastTo","x"),s=n.shape;if(t.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let u=n.shape.slice();for(;u.length<t.length;)u.unshift(1);n=U(n,u)}let r=n.shape,a=Array.from(t);for(let u=t.length-1;u>=0;u--)if(r[u]===t[u])a[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return $s(n);let i={x:n},l={reps:a};return z.runKernel(Ur,i,l)}var fc=W({broadcastTo_:VT});function UT(e){let n={x:F(e,"x","ceil")};return z.runKernel(Fa,n)}var K5=W({ceil_:UT});function HT(e,t,n){let s=F(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return z.runKernel(Vr,r,a)}var jn=W({clipByValue_:HT});function GT(e){return gt(e,0)}var Z5=W({concat1d_:GT});function jT(e,t){return gt(e,t)}var Ml=W({concat2d_:jT});function qT(e,t){return gt(e,t)}var Y5=W({concat3d_:qT});function XT(e,t){return gt(e,t)}var J5=W({concat4d_:XT});function KT(e,t,n,s,r="NHWC",a=[1,1],o){let i=F(e,"x","conv2d"),l=F(t,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&M(Qt(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d=r==="NHWC"?u.shape[3]:u.shape[1];M(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),M(tr(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let p={x:u,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=z.runKernel($a,p,h);return c?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Jr=W({conv2d_:KT});function ZT(e,t,n,s,r="NWC",a=1,o){let i=F(e,"x","conv1d"),l=F(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=U(i,[1,i.shape[0],i.shape[1]])),M(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&M(Qt(s),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(tr(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),M(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=U(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=U(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Jr(p,d,[1,n],s,"NHWC",[1,a],o);return c?U(g,[g.shape[2],g.shape[3]]):U(g,[g.shape[0],g.shape[2],g.shape[3]])}var Rg=W({conv1d_:ZT});function YT(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),M(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];M(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),M(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&M(Qt(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let p={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=z.runKernel(Oa,p,h);return u?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var _g=W({conv2DBackpropInput_:YT});function JT(e,t,n,s,r,a){let o=F(e,"x","conv2dTranspose"),i=F(t,"filter","conv2dTranspose");return _g(n,o,i,s,r,"NHWC",a)}var Dg=W({conv2dTranspose_:JT});function QT(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=F(e,"x","conv3d"),i=F(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),M(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),M(tr(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let c={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=z.runKernel(Uu,c,d);return u?U(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Fg=W({conv3d_:QT});function eN(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=U(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],u=o.shape[4];M(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),M(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=z.runKernel(sp,c,d);return i?U(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Q5=W({conv3DBackpropInput_:eN});function tN(e,t,n,s,r){let a=F(e,"x","conv3dTranspose"),o=F(t,"filter","conv3dTranspose");return Q5(n,a,o,s,r)}var eb=W({conv3dTranspose_:tN});function nN(e){let n={x:F(e,"x","cos")};return z.runKernel(Pa,n)}var Jp=W({cos_:nN});function sN(e){let n={x:F(e,"x","cosh")};return z.runKernel(Ma,n)}var $g=W({cosh_:sN});function rN(e,t=0,n=!1,s=!1){let a={x:F(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return z.runKernel(za,a,o)}var Og=W({cumsum_:rN});function aN(e,t,n,s=!1){let r=F(e,"x","denseBincount"),a=F(t,"weights","denseBincount");M(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),M(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return z.runKernel(rp,o,i)}var tb=W({denseBincount_:aN});function oN(e,t,n="NHWC"){let s=F(e,"x","depthToSpace"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];M(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${r} and ${t} for depthToSpace with input shape
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${s.shape}`),M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${a} and ${t} for depthToSpace with input shape
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s=F(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=F(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===kn.NONE)return a;if(n===kn.SUM)return ve(a);if(n===kn.MEAN){if(r==null)return Ft(a);{let o=s.size/r.size,i=de(ve(a),ve(r));return o>1?de(i,Se(o)):i}}if(n===kn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return de(ve(a),Se(s.size));{let o=L(r,Xn(s.shape)),i=ce(ve(Wl(o,Se(0))),"float32");return de(ve(a),i)}}throw Error(`Unknown reduction: ${n}`)}var kr=W({computeWeightedLoss_:oD});function iD(e,t,n,s=kn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","absoluteDifference"),a=F(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=F(n,"weights","absoluteDifference")),xn(r.shape,a.shape,"Error in absoluteDifference: ");let i=jt(ge(r,a));return kr(i,o,s)}var lD=W({absoluteDifference_:iD});function uD(e,t,n,s,r=kn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","cosineDistance"),o=F(t,"predictions","cosineDistance"),i=null;s!=null&&(i=F(s,"weights","cosineDistance")),xn(a.shape,o.shape,"Error in cosineDistance: ");let l=Se(1),u=ge(l,ve(L(a,o),n,!0));return kr(u,i,r)}var cD=W({cosineDistance_:uD});function dD(e,t,n,s=kn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","hingeLoss"),a=F(t,"predictions","hingeLoss"),o=null;n!=null&&(o=F(n,"weights","hingeLoss")),xn(r.shape,a.shape,"Error in hingeLoss: ");let i=Se(1);r=ge(L(Se(2),r),i);let l=sr(ge(i,L(r,a)));return kr(l,o,s)}var pD=W({hingeLoss_:dD});function hD(e,t,n,s=1,r=kn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","huberLoss"),o=F(t,"predictions","huberLoss"),i=null;n!=null&&(i=F(n,"weights","huberLoss")),xn(a.shape,o.shape,"Error in huberLoss: ");let l=Se(s),u=jt(ge(o,a)),c=yc(u,l),d=ge(u,c),p=ae(L(Se(.5),pt(c)),L(l,d));return kr(p,i,r)}var fD=W({huberLoss_:hD});function mD(e,t,n,s=1e-7,r=kn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","logLoss"),o=F(t,"predictions","logLoss"),i=null;n!=null&&(i=F(n,"weights","logLoss")),xn(a.shape,o.shape,"Error in logLoss: ");let l=Se(1),u=Se(s),c=Et(L(a,cs(ae(o,u)))),d=L(ge(l,a),cs(ae(ge(l,o),u))),p=ge(c,d);return kr(p,i,r)}var gD=W({logLoss_:mD});function AD(e,t,n,s=kn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),a=F(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=F(n,"weights","meanSquaredError")),xn(r.shape,a.shape,"Error in meanSquaredError: ");let i=eA(r,a);return kr(i,o,s)}var yD=W({meanSquaredError_:AD});function xD(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),s=F(t,"logits","sigmoidCrossEntropyWithLogits");xn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=sr(s),a=L(s,n),o=th(us(Et(jt(s))));return ae(ge(r,a),o)}function bD(e,t,n,s=0,r=kn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"multiClassLabels","sigmoidCrossEntropy"),o=F(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","sigmoidCrossEntropy")),xn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let u=Se(s),c=Se(1),d=Se(.5);a=ae(L(a,ge(c,u)),L(d,u))}let l=xD(a,o);return kr(l,i,r)}var vD=W({sigmoidCrossEntropy_:bD});function wD(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==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 ${n}`);return nr((r,a,o)=>{let l=mb(a,[n],!0),u=ge(ce(a,"float32"),l);o([r,u]);let c=Et(L(u,r));return{value:ve(c,[n]),gradFunc:(h,f)=>{let[m,g]=f,A=zo(h.shape,[n]);return[L(U(h,A),ge(ce(m,"float32"),us(g))),L(U(h,A),ge(us(g),ce(m,"float32")))]}}})(e,t)}function kD(e,t,n,s=0,r=kn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"onehotLabels","softmaxCrossEntropy"),o=F(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","softmaxCrossEntropy")),xn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let u=Se(s),c=Se(1),d=Se(a.shape[1]);a=ae(L(a,ge(c,u)),de(u,d))}let l=wD(a,o);return kr(l,i,r)}var ID=W({softmaxCrossEntropy_:kD});function SD(e,t,n,s){let r=F(e,"indices","sparseFillEmptyRows"),a=F(t,"values","sparseFillEmptyRows"),o=F(n,"denseShape","sparseFillEmptyRows"),i=F(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
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${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},u=z.runKernel(Ip,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var CD=W({sparseFillEmptyRows_:SD});function TD(e,t,n){let s=F(e,"inputIndices","sparseReshape"),r=F(t,"inputShape","sparseReshape"),a=F(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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|
${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=z.runKernel(Sp,o);return{outputIndices:i[0],outputShape:i[1]}}var ND=W({sparseReshape_:TD});function ED(e,t,n){let s=F(e,"data","sparseSegmentMean"),r=F(t,"indices","sparseSegmentMean"),a=F(n,"segmentIds","sparseSegmentMean");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
|
|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return z.runKernel(Cp,o)}var RD=W({sparseSegmentMean_:ED});function _D(e,t,n){let s=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum"),a=F(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return z.runKernel(Tp,o)}var DD=W({sparseSegmentSum_:_D});function FD(e,t,n,s,r,a,o,i){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:u},p=z.runKernel(Ep,d,c);return{nGrams:p[0],nGramsSplits:p[1]}}var $D=W({stringNGrams_:FD});function OD(e,t,n=!0){let s=F(e,"input","stringSplit","string"),r=F(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=z.runKernel(Rp,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var PD=W({stringSplit_:OD});function MD(e,t){let n=F(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return z.runKernel(_p,r,s)}var zD=W({stringToHashBucketFast_:MD}),LD={fft:ph,ifft:vc,rfft:hh,irfft:Qg},BD={hammingWindow:f_,hannWindow:zb,frame:Lb,stft:y_},_e={flipLeftRight:w_,grayscaleToRGB:I_,resizeNearestNeighbor:X_,resizeBilinear:j_,rotateWithOffset:C_,cropAndResize:b_,nonMaxSuppression:N_,nonMaxSuppressionAsync:P_,nonMaxSuppressionWithScore:z_,nonMaxSuppressionWithScoreAsync:B_,nonMaxSuppressionPadded:V_,nonMaxSuppressionPaddedAsync:H_,threshold:Y_,transform:Q_},Gb={bandPart:tD,gramSchmidt:sD,qr:aD},WD={absoluteDifference:lD,computeWeightedLoss:kr,cosineDistance:cD,hingeLoss:pD,huberLoss:fD,logLoss:gD,meanSquaredError:yD,sigmoidCrossEntropy:vD,softmaxCrossEntropy:ID},kc={sparseFillEmptyRows:CD,sparseReshape:ND,sparseSegmentMean:RD,sparseSegmentSum:DD},xh={stringNGrams:$D,stringSplit:PD,stringToHashBucketFast:zD},Ir=class extends E5{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return Z(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return db(e,t)}dispose(){this.iterations_!=null&&Z(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ir,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var bh=class extends Ir{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:H(()=>Je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:H(()=>Je(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;H(()=>{let u=ae(L(i,this.rho),L(pt(o),1-this.rho)),c=L(de(mn(ae(l,this.epsilon)),mn(ae(i,this.epsilon))),o),d=ae(L(l,this.rho),L(pt(c),1-this.rho));i.assign(u),l.assign(d);let p=ae(L(c,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Z(this.accumulatedGrads.map(e=>e.variable)),Z(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};bh.className="Adadelta";Zr(bh);var vh=class extends Ir{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=z.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:H(()=>zl(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;H(()=>{let i=ae(o,pt(a));o.assign(i);let l=ae(L(de(a,mn(ae(i,z.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Z(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};vh.className="Adagrad";Zr(vh);var wh=class extends Ir{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],H(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(n).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let o=z.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:H(()=>Je(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:H(()=>Je(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,d=ae(L(u,this.beta1),L(l,1-this.beta1)),p=ae(L(c,this.beta2),L(pt(l),1-this.beta2)),h=de(d,n),f=de(p,s);u.assign(d),c.assign(p);let m=ae(L(de(h,ae(mn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Z(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Z(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),H(()=>{this.accBeta1.assign(ea(this.beta1,this.iterations_+1)),this.accBeta2.assign(ea(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};wh.className="Adam";Zr(wh);var kh=class extends Ir{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],H(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);H(()=>{let n=ge(1,this.accBeta1),s=de(-this.learningRate,ae(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},AP=0,Qe=class extends oe.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=AP++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Cr(n)+"_"+Lh(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),u=r.axes[i],c=l>=0?o[l]:o[o.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=bt(e),s=!0;for(let a of n)if(!(a instanceof Ws)){s=!1;break}let r=!0;for(let a of n)if(a instanceof Ws){r=!1;break}if(s===r)throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ho(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of bt(e))a.push(o.shape);this.build($n(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=bt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=$n(i),this.activityRegularizer!=null)throw new Me("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=yP(e),o=this.computeOutputShape(a),i,l=xP(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,c)=>new Ws(l,u,this,bt(e),t,this.name,c)):i=new Ws(l,o,this,bt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Me("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Sr(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Sr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new zs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Wh(this.weights)}build(e){this.built=!0}getWeights(e=!1){return _A(e?this.trainableWeights:this.weights)}setWeights(e){H(()=>{let t=this.weights;if(t.length!==e.length)throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;ar(x===0,"input layer has >1 nodes"),ar(b===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let y=this.inputLayers[A];if(!(y instanceof ql))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},s={},r={},a={},o=[],i=(A,y,x,b,v,k)=>{(b==null||v==null||k==null)&&(b=A.sourceLayer,v=A.nodeIndex,k=A.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new zs(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(y.indexOf(S)!==-1)return;this.containerNodes.add(ir.nodeKey(b,v)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let C=S.inboundLayers.length;for(let _=0;_<C;_++){let O=S.inputTensors[_],E=S.inboundLayers[_],R=S.nodeIndices[_],T=S.tensorIndices[_];i(O,y,x,E,R,T)}for(y.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],u=[];for(let A of this.outputs)i(A,l,u);let c=o.slice().reverse();for(let A of c){n[A.id]=A,A.id in t||(t[A.id]=0);let y=t[A.id],x=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];y=Math.max(y,x),s[A.outboundLayer.id]=y,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=y;for(let b=0;b<A.inboundLayers.length;b++){let v=A.inboundLayers[b],k=A.nodeIndices[b],S=v.inboundNodes[k],C=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(y+1,C),n[S.id]=S}}let d={};for(let A in t){let y=t[A];y in d||(d[y]=[]),d[y].push(n[A])}let p={};for(let A in s){let y=s[A];y in p||(p[y]=[]),p[y].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(Th);this.layers=[];for(let A of h){let y=p[A];y.sort((x,b)=>{let v=a[x.id],k=a[b.id];return v<k?-1:v>k?1:0});for(let x of y)x instanceof ir&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(Th);let f=this.inputs.slice(),m=[];for(let A of h)for(let y of d[A]){let x=y.outboundLayer;if(x!=null){for(let b of y.inputTensors)if(f.indexOf(b)===-1)throw new zs(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of y.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let y=g.filter(x=>x===A).length;if(y!==1)throw new zs(`The name "${A}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Vh({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new G("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new G(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}DA(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${BA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=LA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return H(()=>{e=bt(e);let n=new qo;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Fc(this.outputs,n,t)})}computeMask(e,t){return H(()=>{e=bt(e);let n;return t==null?n=Wo(null,e.length):n=bt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Bh(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Th);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],y=`${m.name}_${g}_${A}`,x=n[y];c.push(x)}let d=u.computeOutputShape($n(c)),p=Bh(d),h=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];ar(i in n),r.push(n[i])}return $n(r)}runInternalGraph(e,t){t==null&&(t=Wo(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Th);for(let i of s){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,d=u.inputTensors,p=u.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,A,y;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),A=bt(c.call(x,f)),y=bt(c.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),A=bt(c.call(m,f)),y=bt(c.computeMask(m,g));if(c.activityRegularizer)throw new Me("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=A[x],k=y[x];n[b.id]=[v,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){ar(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),r.push(l),a.push(u)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof ir?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=ir.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return H(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=ir.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let c=0;c<a.inboundNodes.length;c++){let d=a.inboundNodes[c],p=ir.nodeKey(a,c),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],A=d.nodeIndices[m],y=d.tensorIndices[m],x=ir.nodeKey(g,A),b=t[x];b==null&&(b=0),f.push([g.name,b,y,h])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=ir.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,u,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=ir.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,u,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let A=[],y;for(let x of g){let b=x[0],v=x[1],k=x[2];if(y=x[3]==null?{}:x[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=v){o(m,g);return}let C=S.inboundNodes[v];A.push(C.outputTensors[k])}A.length>0&&m.apply($n(A),y)}function l(m){let g=m.name,A=Vs(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${x}`);o(A,x)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!TO(a);)for(let m of c){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let y of A)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],y=m[2];ar(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[y])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],y=m[2];ar(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[y])}return new e({inputs:d,outputs:p,name:u})}get stateful(){if(this._stateful)throw new G("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){H(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function YP(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function j3(e,t){return YP(e,t,"classWeight")}async function q3(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=H(()=>{if(e.shape.length===1)return $s(e);if(e.shape.length===2){if(e.shape[1]>1)return er(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Z(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Lt(o,"float32")}else return null}function JP(e,t){return L(e,t)}var QP=32;function X3(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=K3("input",e.inputNames,n),o=K3("output",e.outputNames,s),i=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)w.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)w.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function K3(e,t,n){if(n instanceof qe)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function eM(e){if(e.length===3)throw new Me("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function tM(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(Z3(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=eM(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=$3(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=O3(c,d,n.epochs,null,null,nM(t,n),null,r,u);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let A=0,y=0;for(s||(m=await t.iterator());s?A<n.batchesPerEpoch:!0;){let x=await m.next();if(s&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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t={};return t.className="linear",t.config={},qA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},qA(t)}else return e instanceof Pn?e:qA(e)}function XA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var gv=class extends oe.Serializable{},Oc=class extends gv{constructor(e){super();XA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return H(()=>{let t=$t([1]);return this.hasL1&&(t=ae(t,ve(L(this.l1,jt(e))))),this.hasL2&&(t=ae(t,ve(L(this.l2,Ec(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Oc.className="L1L2";oe.registerClass(Oc);function bM(e){return XA(e),new Oc({l1:e!=null?e.l1:null,l2:0})}function vM(e){return XA(e),new Oc({l2:e!=null?e.l2:null,l1:0})}var Av={l1l2:"L1L2"};function ht(e){return dA(e)}function 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};QA.className="ThresholdedReLU";oe.registerClass(QA);var e1=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new jA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Be(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};e1.className="Softmax";oe.registerClass(e1);function Yl(e,t,n){if(typeof e=="number")return Wo(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Us(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function lr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+ra([n-t,0]);else if(s==="same")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function t1(e,t){return H(()=>(Mt(t),t==="channelsFirst"?Ye(e,[0,2,3,1]):e))}function xv(e,t){return H(()=>(Mt(t),t==="channelsFirst"?Ye(e,[0,2,3,4,1]):e))}function wM(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=Ms()),Mt(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ye(e,[0,2,1])),r==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Rg(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Bs(i,n)),i})}function bv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=Ms()),Mt(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=t1(e,a);if(r==="causal")throw new Me("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ta.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Ye(l,[0,3,1,2])),l})}function kM(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=Ms()),Mt(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=xv(e,a);if(r==="causal")throw new Me("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Fg(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Bs(i,n)),a==="channelsFirst"&&(i=Ye(i,[0,4,1,2,3])),i})}var n1=class extends Qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",n1.verifyArgs(t),this.rank=e,sn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Me(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yl(t.kernelSize,e,"kernelSize"),this.strides=Yl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ms(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=ia(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=It(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Yt(t.biasConstraint),this.biasRegularizer=St(t.biasRegularizer),this.activityRegularizer=St(t.activityRegularizer),this.dilationRate=Yl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`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 G(`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 G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(ar("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!hA(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:oa(this.activation),useBias:this.useBias,biasInitializer:Rt(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:Zt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Pc=class extends n1{constructor(e,t){super(e,t);this.kernel=null,Pc.verifyArgs(t),this.filters=t.filters,sn(this.filters,"filters"),this.kernelInitializer=It(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Yt(t.kernelConstraint),this.kernelRegularizer=St(t.kernelRegularizer)}build(e){e=it(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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:{[t]:n}}],this.built=!0}call(e,t){return H(()=>{e=Be(e);let n,s=this.bias==null?null:this.bias.read(),r=u3(this.activation.getClassName());if(r!=null&&this.rank===2)n=bv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=wM(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=bv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=kM(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Me("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=it(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=Us(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Rt(this.kernelInitializer),kernelRegularizer:ht(this.kernelRegularizer),kernelConstraint:Zt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Mc=class extends Pc{constructor(e){super(2,e);Mc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Mc.className="Conv2D";oe.registerClass(Mc);var zc=class extends Pc{constructor(e){super(3,e);zc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};zc.className="Conv3D";oe.registerClass(zc);var s1=class extends Mc{constructor(e){super(e);if(this.inputSpec=[new Bt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=it(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=Be(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=lr(i,d,u,this.padding),f=lr(l,p,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,1]));let g=Dg(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ye(g,[0,3,1,2])),this.bias!=null&&(g=Bs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=it(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=lr(t[s],i,a,this.padding),t[r]=lr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};s1.className="Conv2DTranspose";oe.registerClass(s1);var r1=class extends zc{constructor(e){super(e);if(this.inputSpec=[new Bt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=it(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Bt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=Be(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=lr(l,f,d,this.padding),y=lr(u,m,p,this.padding),x=lr(c,g,h,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ye(n,[0,2,3,4,1]));let v=eb(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ye(v,[0,4,1,2,3])),this.bias!==null&&(v=Bs(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=it(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=lr(t[s],u,o,this.padding),t[r]=lr(t[r],c,i,this.padding),t[a]=lr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};r1.className="Conv3DTranspose";oe.registerClass(r1);var vv=class extends Pc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=It(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=St(t.depthwiseRegularizer),this.depthwiseConstraint=Yt(t.depthwiseConstraint),this.pointwiseInitializer=It(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=St(t.pointwiseRegularizer),this.pointwiseConstraint=Yt(t.pointwiseConstraint)}build(e){if(e=it(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Bt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{e=Be(e);let n;if(this.rank===1)throw new Me("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ye(e,[0,2,3,1])),n=wb(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Bs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ye(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Rt(this.depthwiseInitializer),e.pointwiseInitializer=Rt(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=Zt(this.depthwiseConstraint),e.pointwiseConstraint=Zt(this.pointwiseConstraint),e}};vv.className="SeparableConv";var a1=class extends vv{constructor(e){super(2,e)}};a1.className="SeparableConv2D";oe.registerClass(a1);var Yh=class extends Pc{constructor(e){super(1,e);Yh.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!hA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Yh.className="Conv1D";oe.registerClass(Yh);var o1=class extends Qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=Be(e),this.dataFormat==="channelsLast"){let n=Eh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Eh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Eh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Eh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};o1.className="Cropping2D";oe.registerClass(o1);var i1=class extends Qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,zO(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return H(()=>{let n=Be(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ye(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?_e.resizeNearestNeighbor(n,[r,a]):_e.resizeBilinear(n,[r,a]);return Ye(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?_e.resizeNearestNeighbor(n,[r,a]):_e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};i1.className="UpSampling2D";oe.registerClass(i1);function IM(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=Ms()),Mt(r);let o=t1(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=mc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ye(o,[0,3,1,2])),o})}var l1=class extends n1{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=It(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Yt(e.depthwiseConstraint),this.depthwiseRegularizer=St(e.depthwiseRegularizer)}build(e){if(e=it(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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(e,t){return H(()=>{e=Be(e);let n=IM(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Bs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Us(t,this.kernelSize[0],this.padding,this.strides[0]),a=Us(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Rt(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=Zt(this.depthwiseRegularizer),e}};l1.className="DepthwiseConv2D";oe.registerClass(l1);function wv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function kv(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Ls(2,l));if(t=Ye(t,u),a!=null)throw new Me("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===l-1&&(r=zt(r,-1)),r=Ye(r,u)),s&&(t=hs(t,0),r!=null&&(r=hs(r,0)));let c=[],d,p=n,h=t.shape[0],f=fs(t),m;r!=null&&(m=fs(r));for(let A=0;A<h;++A){let y=f[A],x=H(()=>e(y,p));if(r==null)d=x[0],p=x[1];else{let b=H(()=>{let v=m[A],k=ge(ps(v),v),S=ae(L(x[0],v),L(p[0],k)),C=p.map((_,O)=>ae(L(x[1][O],v),L(_,k)));return{output:S,newStates:C}});d=b.output,p=b.newStates}i&&c.push(d)}let g;return i&&(g=gn(c,1)),[d,g,p]})}var ur=class extends Qe{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new ef({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Bt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Ls(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){RA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Me("Constants support is not implemented in RNN yet.");RA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Bt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Me("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Bt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Sr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(r.shape,o))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>nn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=wv(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Bt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Ws){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Be(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=kv((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?c:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return H(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Nc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?vA(t,[1,n]):t):this.cell.stateSize>1?[vA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===ur.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Vs(s,n);return new e(Object.assign(t,{cell:r}))}};ur.className="RNN";oe.registerClass(ur);var Lc=class extends Qe{},Jh=class extends Lc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,sn(this.units,"units"),this.activation=ia(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=It(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=St(e.kernelRegularizer),this.recurrentRegularizer=St(e.recurrentRegularizer),this.biasRegularizer=St(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=jl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=it(e),this.kernel=this.addWeight("kernel",[e[e.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(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=la({ones:()=>ps(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=la({ones:()=>ps(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=or(L(e,a),this.kernel.read()):r=or(e,this.kernel.read()),this.bias!=null&&(r=Bs(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ae(r,or(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:oa(this.activation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),recurrentInitializer:Rt(this.recurrentInitializer),biasInitializer:Rt(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Zt(this.kernelConstraint),recurrentConstraint:Zt(this.recurrentConstraint),biasConstraint:Zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Jh.className="SimpleRNNCell";oe.registerClass(Jh);var u1=class extends ur{constructor(e){e.cell=new Jh(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};u1.className="SimpleRNN";oe.registerClass(u1);var Qh=class extends Lc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,sn(this.units,"units"),this.activation=ia(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ia(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=It(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=St(e.kernelRegularizer),this.recurrentRegularizer=St(e.recurrentRegularizer),this.biasRegularizer=St(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=jl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=it(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,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(e,t){return H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=la({ones:()=>ps(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=la({ones:()=>ps(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let u=or(e,this.kernel.read());this.useBias&&(u=Bs(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let c=this.recurrentKernel.read(),[d,p]=Xt(c,[2*this.units,this.units],c.rank-1),h=or(s,d),[f,m,g]=Xt(u,3,u.rank-1),[A,y]=Xt(h,2,h.rank-1);o=this.recurrentActivation.apply(ae(f,A)),i=this.recurrentActivation.apply(ae(m,y));let x=or(L(i,s),p);l=this.activation.apply(ae(g,x));let b=ae(L(o,s),L(ae(1,Et(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:oa(this.activation),recurrentActivation:oa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),recurrentInitializer:Rt(this.recurrentInitializer),biasInitializer:Rt(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Zt(this.kernelConstraint),recurrentConstraint:Zt(this.recurrentConstraint),biasConstraint:Zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Qh.className="GRUCell";oe.registerClass(Qh);var c1=class extends ur{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Qh(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};c1.className="GRU";oe.registerClass(c1);var Bc=class extends Lc{constructor(e){super(e);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=e.units,sn(this.units,"units"),this.activation=ia(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ia(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=It(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=St(e.kernelRegularizer),this.recurrentRegularizer=St(e.recurrentRegularizer),this.biasRegularizer=St(e.biasRegularizer),this.kernelConstraint=Yt(e.kernelConstraint),this.recurrentConstraint=Yt(e.recurrentConstraint),this.biasConstraint=Yt(e.biasConstraint),this.dropout=jl([1,ra([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=jl([1,ra([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=it(e);let n=e[e.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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Es{apply(i,l){let u=r.apply([a]),c=new _h().apply([a]),d=r.apply([a*2]);return y3(y3(u,c),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=la({ones:()=>ps(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=la({ones:()=>ps(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let d=or(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=ae(d,or(s,this.recurrentKernel.read())),this.useBias&&(d=Bs(d,this.bias.read()));let[p,h,f,m]=Xt(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),u=ae(L(l,r),L(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=L(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:oa(this.activation),recurrentActivation:oa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),recurrentInitializer:Rt(this.recurrentInitializer),biasInitializer:Rt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Zt(this.kernelConstraint),recurrentConstraint:Zt(this.recurrentConstraint),biasConstraint:Zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Bc.className="LSTMCell";oe.registerClass(Bc);var d1=class extends ur{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bc(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};d1.className="LSTM";oe.registerClass(d1);var ef=class extends Lc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){RA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Ho(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Vs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return _A(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}DA(t)}};ef.className="StackedRNNCells";oe.registerClass(ef);function la(e){let{ones:t,rate:n,training:s=!1,count:r=1}=e,a=()=>b3(t(),n),o=()=>Rc(a,t,s);return!r||r<=1?nn(o().clone()):Array(r).fill(void 0).map(o).map(l=>nn(l.clone()))}var SM=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},Iv=class extends ur{constructor(e){if(e.unroll)throw new Me("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Me("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Bt({ndim:5})]}call(e,t){return H(()=>{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Sr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!w.arraysEqual(i.shape,l))throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>nn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=Us(l,s[0],r,a[0],o[0]),d=Us(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};Iv.className="ConvRNN2D";var tf=class extends Bc{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,sn(this.filters,"filters"),this.kernelSize=Yl(n,2,"kernelSize"),this.kernelSize.forEach(i=>sn(i,"kernelSize")),this.strides=Yl(s||1,2,"strides"),this.strides.forEach(i=>sn(i,"strides")),this.padding=r||"valid",ms(this.padding),this.dataFormat=a||"channelsLast",Mt(this.dataFormat),this.dilationRate=Yl(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>sn(i,"dilationRate"))}build(e){var t;e=it(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends Es{apply(d,p){let h=l.apply([u]),f=Xn([u]),m=l.apply([u*2]);return bA([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=la({ones:()=>ps(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,te,ne)=>!te||!te[ne]?ee:L(te[ne],ee),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=la({ones:()=>ps(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),y=3,[x,b,v,k]=Xt(this.kernel.read(),o,y),[S,C,_,O]=this.useBias?Xt(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,_,this.padding),p=this.inputConv(p,k,O,this.padding);let[E,R,T,P]=Xt(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,E),m=this.recurrentConv(m,R),g=this.recurrentConv(g,T),A=this.recurrentConv(A,P);let V=this.recurrentActivation.apply(ae(u,f)),j=this.recurrentActivation.apply(ae(c,m)),q=ae(L(j,a),L(V,this.activation.apply(ae(d,g)))),X=L(this.recurrentActivation.apply(ae(p,A)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=SM(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Jr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Bs(r,n,this.dataFormat):r}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};tf.className="ConvLSTM2DCell";oe.registerClass(tf);var p1=class extends Iv{constructor(e){let t=new tf(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};p1.className="ConvLSTM2D";oe.registerClass(p1);var nf=class extends Qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Rc(()=>b3(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};nf.className="Dropout";oe.registerClass(nf);var h1=class extends nf{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};h1.className="SpatialDropout1D";oe.registerClass(h1);var f1=class extends Qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,sn(this.units,"units"),this.activation=ia(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=It(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=It(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Yt(e.kernelConstraint),this.biasConstraint=Yt(e.biasConstraint),this.kernelRegularizer=St(e.kernelRegularizer),this.biasRegularizer=St(e.biasRegularizer),this.activityRegularizer=St(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=it(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,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]:t}}],this.built=!0}computeOutputShape(e){e=it(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e),s=u3(this.activation.getClassName()),r;return s!=null?r=or(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=or(n,this.kernel.read()),this.bias!=null&&(r=Bs(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:oa(this.activation),useBias:this.useBias,kernelInitializer:Rt(this.kernelInitializer),biasInitializer:Rt(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Zt(this.kernelConstraint),biasConstraint:Zt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};f1.className="Dense";oe.registerClass(f1);var m1=class extends Qe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=it(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],sa(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ye(n,s)}return HO(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};m1.className="Flatten";oe.registerClass(m1);var g1=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=ia(e.activation)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.activation.apply(n)})}getConfig(){let e={activation:oa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};g1.className="Activation";oe.registerClass(g1);var A1=class extends Qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return H(()=>(e=Be(e),VO(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};A1.className="RepeatVector";oe.registerClass(A1);var y1=class extends Qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new G("Can only specifiy one unknown dimension.");else r*=l}let o=sa(e);if(a!==null){if(r===0||o%r!=0)throw new G(n);s[a]=o/r}else if(o!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};y1.className="Reshape";oe.registerClass(y1);var x1=class extends Qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Ls(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Bt({ndim:this.dims.length+1})]}computeOutputShape(e){e=it(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ye(Be(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};x1.className="Permute";oe.registerClass(x1);var b1=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Be(e),s=-1;return Xp(Wl(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e),s=-1,r=!0,a=Xp(Wl(n,this.maskValue),s,r);return L(n,ce(a,n.dtype))})}};b1.className="Masking";oe.registerClass(b1);var v1=class extends Qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(bt(e.inputLength))}this.inputDim=e.inputDim,sn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,sn(this.outputDim,"outputDim"),this.embeddingsInitializer=It(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=St(e.embeddingsRegularizer),this.activityRegularizer=St(e.activityRegularizer),this.embeddingsConstraint=Yt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return H(()=>this.maskZero?(e=Be(e),Wl(e,Je(e))):null)}computeOutputShape(e){if(e=it(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e);n.dtype!=="int32"&&(n=Nh(n,"int32"));let s=x3(this.embeddings.read(),U(n,[n.size]));return U(s,it(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Rt(this.embeddingsInitializer),embeddingsRegularizer:ht(this.embeddingsRegularizer),activityRegularizer:ht(this.activityRegularizer),embeddingsConstraint:Zt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};v1.className="Embedding";oe.registerClass(v1);var Ko=class extends Qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Me}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[it(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=na(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&na(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return H(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=ra(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Nc(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let u=i.shape,c=u[0],d=u.slice(1).concat([c]),p=U(i,[c].concat(sa(u.slice(1))));p=Ye(p,[1,0]),p=U(p,d),n.push(p),r=!0}else if(l>1){let u=Ls(1,l).concat([0]);n.push(Ye(i,u)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=U(Ye(U(a,[-1,u]),[1,0]),c)}else if(o>1){let i=[o-1].concat(Ls(0,o-1));a=Ye(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=na(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return H(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:zt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Os(n,t[s]);return n})}},w1=class extends Ko{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ae(t,e[n]);return t})}};w1.className="Add";oe.registerClass(w1);var k1=class extends Ko{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};k1.className="Multiply";oe.registerClass(k1);var I1=class extends Ko{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ae(t,e[n]);return L(1/e.length,t)})}};I1.className="Average";oe.registerClass(I1);var S1=class extends Ko{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=wr(t,e[n]);return t})}};S1.className="Maximum";oe.registerClass(S1);var C1=class extends Ko{constructor(e){super(e)}mergeFunction(e){return H(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=yc(t,e[n]);return t})}};C1.className="Minimum";oe.registerClass(C1);var T1=class extends Ko{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return H(()=>bA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new G("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new G("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new G(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return H(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(ps(e[a]),"bool")):t[a].rank<e[a].rank?s.push(zt(t[a],-1)):s.push(t[a]);let r=gt(s,this.axis);return Sg(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};T1.className="Concatenate";oe.registerClass(T1);function Wc(e,t){for(;e<0;)e+=t;return e}function CM(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Me("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Me("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);t=U(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);e=U(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(L(e,t),a[0]):i=ve(L(Ye(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=He(e,t,l,u)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=ct(i,u)}return i.shape.length===1&&(i=zt(i,1)),i})}var N1=class extends Ko{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Wc(r,e[a].shape.length)):s=[Wc(this.axes,t.shape.length),Wc(this.axes,n.shape.length)],this.normalize&&(t=Uh(t,s[0]),n=Uh(n,s[1])),CM(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Wc(this.axes,e.length),Wc(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Me("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};N1.className="Dot";oe.registerClass(N1);var E1=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e);return Rc(()=>ae(Rh(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};E1.className="GaussianNoise";oe.registerClass(E1);var R1=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.rate>0&&this.rate<1?Rc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Rh(n.shape,1,r))},()=>n,t.training||!1):n})}};R1.className="GaussianDropout";oe.registerClass(R1);var _1=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Be(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Rc(()=>{let r=Be(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Po(Vl(n),this.rate);l=Nh(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=ae(L(r,l),L(ae(l,-1),i));return ae(L(d,u),c)},()=>Be(e),t.training||!1)}return e})}};_1.className="AlphaDropout";oe.registerClass(_1);function Vc(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=G5(e,t,n,s,r,a);else if(e.rank===3)o=j5(e,t,n,s,r,a);else if(e.rank===4)o=q5(e,t,n,s,r,a);else throw new Me(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function TM(e,t,n,s,r=.001){return H(()=>{let a=oh(e,s),o=a.mean,i=a.variance;return[Vc(e,o,i,n,t,r),o,i]})}function NM(e,t,n,s,r=.001){return H(()=>{let a=oh(e,s),o=a.mean,i=a.variance,l=[];for(let f of Ls(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=U(o,l),c=U(i,l),d=t==null?null:U(t,l),p=n==null?null:U(n,l);return[Vc(e,u,c,p,d,r),o,i]})}function EM(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),Ls(0,e.rank-1))?TM(e,t,n,s,r):NM(e,t,n,s,r)}var D1=class extends Qe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=It(e.betaInitializer||"zeros"),this.gammaInitializer=It(e.gammaInitializer||"ones"),this.movingMeanInitializer=It(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=It(e.movingVarianceInitializer||"ones"),this.betaConstraint=Yt(e.betaConstraint),this.gammaConstraint=Yt(e.gammaConstraint),this.betaRegularizer=St(e.betaRegularizer),this.gammaRegularizer=St(e.gammaRegularizer)}build(e){e=it(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Bt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,s=Be(e),r=s.shape,a=r.length,o=Ls(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Wo(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,Ls(0,a).slice(0,a-1)),d=()=>{if(c){let A=U(this.movingMean.read(),l),y=U(this.movingVariance.read(),l),x=this.center?U(this.beta.read(),l):null,b=this.scale?U(this.gamma.read(),l):null;return Vc(s,A,y,x,b,this.epsilon)}else return Vc(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=EM(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,y,x)=>{H(()=>{let b=1-x,v=A.read(),k=L(ge(v,y),b);A.write(ge(v,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Rt(this.betaInitializer),gammaInitializer:Rt(this.gammaInitializer),movingMeanInitializer:Rt(this.movingMeanInitializer),movingVarianceInitializer:Rt(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:Zt(this.betaConstraint),gammaConstraint:Zt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};D1.className="BatchNormalization";oe.registerClass(D1);var F1=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.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 t of this.axis)if(!Number.isInteger(t))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=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=It(e.betaInitializer||"zeros"),this.gammaInitializer=It(e.gammaInitializer||"ones"),this.betaRegularizer=St(e.betaRegularizer),this.gammaRegularizer=St(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=it(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==na(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Be(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=oh(n,this.axis,a),l=Wo(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?U(f,l):f,c=u(this.gamma.read()),d=u(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=Ts(o,p),i=Ts(i,p),c=Ts(c,h),d=Ts(d,h),Vc(n,o,i,d,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Rt(this.betaInitializer),gammaInitializer:Rt(this.gammaInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};F1.className="LayerNormalization";oe.registerClass(F1);function RM(e,t,n){return H(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ms()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Qr(e,s)})}var $1=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Ms():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=it(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return H(()=>RM(Be(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$1.className="ZeroPadding2D";oe.registerClass($1);function sf(e,t,n,s,r,a){return H(()=>{Mt(r),h3(a),ms(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=Ms()),a==null&&(a="max"),e=t1(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=rh(e,t,n,i):o=Zp(e,t,n,i),r==="channelsFirst"&&(o=Ye(o,[0,3,1,2])),o})}function Sv(e,t,n,s,r,a){return H(()=>{Mt(r),h3(a),ms(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Ms()),a==null&&(a="max"),e=xv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Vg(e,t,n,i):o=Ng(e,t,n,i),r==="channelsFirst"&&(o=Ye(o,[0,4,1,2,3])),o})}var Cv=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(sn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);sn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ms(this.padding),this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){e=it(e);let t=Us(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return H(()=>{this.invokeCallHook(e,t),e=Nc(Be(e),2);let n=this.poolingFunction(Be(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ct(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},O1=class extends Cv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Mt(r),ms(s),sf(e,t,n,s,r,"max")}};O1.className="MaxPooling1D";oe.registerClass(O1);var P1=class extends Cv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Mt(r),ms(s),sf(e,t,n,s,r,"avg")}};P1.className="AveragePooling1D";oe.registerClass(P1);var Tv=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];sn(this.poolSize,"poolSize"),sn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),ms(this.padding),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Us(t,this.poolSize[0],this.padding,this.strides[0]),n=Us(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},M1=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Mt(r),ms(s),sf(e,t,n,s,r,"max")}};M1.className="MaxPooling2D";oe.registerClass(M1);var z1=class extends Tv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Mt(r),ms(s),sf(e,t,n,s,r,"avg")}};z1.className="AveragePooling2D";oe.registerClass(z1);var Nv=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];sn(this.poolSize,"poolSize"),sn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),ms(this.padding),this.inputSpec=[new Bt({ndim:5})]}computeOutputShape(e){e=it(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Us(t,this.poolSize[0],this.padding,this.strides[0]),n=Us(n,this.poolSize[1],this.padding,this.strides[1]),s=Us(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},L1=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Mt(r),ms(s),Sv(e,t,n,s,r,"max")}};L1.className="MaxPooling3D";oe.registerClass(L1);var B1=class extends Nv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Mt(r),ms(s),Sv(e,t,n,s,r,"avg")}};B1.className="AveragePooling3D";oe.registerClass(B1);var Ev=class extends Qe{constructor(e){super(e);this.inputSpec=[new Bt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Me}},W1=class extends Ev{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=Be(e);return Ft(n,1)})}};W1.className="GlobalAveragePooling1D";oe.registerClass(W1);var V1=class extends Ev{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=Be(e);return ds(n,1)})}};V1.className="GlobalMaxPooling1D";oe.registerClass(V1);var Rv=class extends Qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.inputSpec=[new Bt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Me}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},U1=class extends Rv{call(e,t){return H(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?Ft(n,[1,2]):Ft(n,[2,3])})}};U1.className="GlobalAveragePooling2D";oe.registerClass(U1);var H1=class extends Rv{call(e,t){return H(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?ds(n,[1,2]):ds(n,[2,3])})}};H1.className="GlobalMaxPooling2D";oe.registerClass(H1);var _v=class extends Qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}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(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=Vs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},G1=class extends _v{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=it(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=it(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return H(()=>(e=Be(e),kv((a,o)=>[Be(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};G1.className="TimeDistributed";oe.registerClass(G1);function _M(e){Uo(MO,"BidirectionalMergeMode",e)}var DM="concat",j1=class extends _v{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Vs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Vs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?DM:e.mergeMode,_M(this.mergeMode),e.weights)throw new Me("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):$n(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=wv(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new Bt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(s!=null)throw new Me("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof Ws;for(let l of a)if(l instanceof Ws!==i)throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=hs(r,1));let o;return this.mergeMode==="concat"?o=bA([s,r]):this.mergeMode==="sum"?o=ae(s,r):this.mergeMode==="ave"?o=L(.5,ae(s,r)):this.mergeMode==="mul"?o=L(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ho(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ho(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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t}},FL=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[ae(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[qp(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[yb(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[L(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[de(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[rb(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Ig(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[ge(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[yc(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[wr(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[ea(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[eA(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},$L=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[jt(I("x",e,t,n))];case"Acos":return[$5(I("x",e,t,n))];case"Acosh":return[O5(I("x",e,t,n))];case"Asin":return[M5(I("x",e,t,n))];case"Asinh":return[z5(I("x",e,t,n))];case"Atan":return[L5(I("x",e,t,n))];case"Atan2":return[B5(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[W5(I("x",e,t,n))];case"Ceil":return[K5(I("x",e,t,n))];case"Complex":return[qr(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[Jp(I("x",e,t,n))];case"Cosh":return[$g(I("x",e,t,n))];case"Elu":return[gc(I("x",e,t,n))];case"Erf":return[ob(I("x",e,t,n))];case"Exp":return[us(I("x",e,t,n))];case"Expm1":return[ib(I("x",e,t,n))];case"Floor":return[Ac(I("x",e,t,n))];case"Log":return[cs(I("x",e,t,n))];case"Log1p":return[th(I("x",e,t,n))];case"Imag":return[Qp(I("x",e,t,n))];case"Neg":return[Et(I("x",e,t,n))];case"Reciprocal":return[vb(I("x",e,t,n))];case"Real":return[xc(I("x",e,t,n))];case"Relu":return[sr(I("x",e,t,n))];case"Round":return[qg(I("x",e,t,n))];case"Selu":return[Kg(I("x",e,t,n))];case"Sigmoid":return[Gn(I("x",e,t,n))];case"Sin":return[Zg(I("x",e,t,n))];case"Sign":return[Ib(I("x",e,t,n))];case"Sinh":return[Yg(I("x",e,t,n))];case"Softplus":return[Bl(I("x",e,t,n))];case"Sqrt":return[mn(I("x",e,t,n))];case"Square":return[pt(I("x",e,t,n))];case"Tanh":return[Ol(I("x",e,t,n))];case"Tan":return[Cb(I("x",e,t,n))];case"ClipByValue":return[jn(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[jg(I("x",e,t,n))];case"Rsqrt":return[Xg(In(e.inputNames[0],t,n))];case"Prod":return[Ug(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[eh(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[lh(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[lb(In(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function _s(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){w.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];w.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function h7(e){return!(typeof e=="number"||e.some(t=>t<0))}function Uc(e,t,n){let s=l2(e,n),r=!h7(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=l2(a.shape,s)}),!h7(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function l2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var OL=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Se(0),nn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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|
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),_s(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,nn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return tn([],[0].concat(this.elementShape));let n=this.readMany(e);return _s(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),gn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return tn([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return _s(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),gt(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,fs(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
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|
tensor.shape[0], but sum of lengths is
|
|
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];H(()=>{t=U(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],u=[0,l,0],c=[1,e[i],r];a[i]=U(De(t,u,c),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},Hc=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);_s(t,r.shape,"TensorList shape mismatch: "),nn(r)}),this.idTensor=Se(0),this.maxNumElements=s,nn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Hc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, 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.`);_s(e,this.elementShape,"TensorList shape mismatch: ");let s=Uc(this.elementShape,this.tensors,e);return H(()=>{let r=this.tensors.map(a=>U(a,s));return gn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Uc(this.elementShape,this.tensors,e),s=this.tensors.pop();return _s(s.shape,e,"TensorList shape mismatch: "),U(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(_s(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");nn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);_s(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=Uc(this.elementShape,this.tensors,t);return U(this.tensors[e],s)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);_s(this.elementShape,t.shape,"TensorList shape mismatch: "),nn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);_s(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=Uc(this.elementShape,this.tensors,n);return e.length===0?tn([],[0].concat(s)):H(()=>{let r=e.map(a=>U(this.tensors[a],s));return gn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);_s(this.elementShape,t,"TensorList shape mismatch: ");let n=Uc(this.elementShape,this.tensors,t);return this.size()===0?tn([],[0].concat(n)):H(()=>{let s=this.tensors.map(r=>U(r,n));return gt(s,0)})}};function PL(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);_s(r,t,"TensorList shape mismatch: ");let a=fs(e);return new Hc(a,t,s)}function ML(e,t,n){return new Hc([],e,t,n)}function zL(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r} vs. ${s})`);let a=new Hc([],n,e.dtype,s),o=fs(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function LL(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=l2(a,n),i=s===0?0:e.size/s,l=H(()=>{let c=[];e=U(e,[1,s,i]);for(let d=0;d<t.length;++d){let p=d===0?0:r[d-1],h=[0,p,0],f=[1,t[d],i];c[d]=U(De(e,h,f),o)}return e.dispose(),c}),u=new Hc([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var BL=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),a=I("cond",e,t,n),o=I("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=I("body",e,t,n),r=I("cond",e,t,n),a=I("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(c=>c.id),l=await o[0].data();o.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=a;for(;l[0];){let c=u;u=await n.functionMap[s].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let d=u.map(h=>h.id);c.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let p=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await p[0].data(),p.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let s=I("pred",e,t,n);return[Er(s)]}case"Switch":{let s=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Er(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let s=e.inputNames.find(r=>In(r,t,n)!==void 0);if(s){let r=In(s,t,n);return[Er(r)]}return}case"Enter":{let s=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(s),[Er(r)]}case"Exit":{let s=I("tensor",e,t,n);return n.exitFrame(),[Er(s)]}case"NextIteration":{let s=I("tensor",e,t,n);return n.nextIteration(),[Er(s)]}case"TensorArrayV3":{let s=I("size",e,t,n),r=I("dtype",e,t,n),a=I("elementShape",e,t,n),o=I("dynamicSize",e,t,n),i=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),c=new OL(u,r,s,a,l,o,i);return n.addTensorArray(c),[c.idTensor,Se(1)]}case"TensorArrayWriteV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(s.id);return o.write(r,a),[o.idTensor]}case"TensorArrayReadV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(s.id);return o.scatter(r,a),[o.idTensor]}case"TensorArrayConcatV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=I("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),a=I("lengths",e,t,n),o=n.getTensorArray(s.id);return o.split(a,r),[o.idTensor]}case"TensorArraySizeV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[Se(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorList(s.id);return o.setItem(r,a),[o.idTensor]}case"TensorListGetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let s=I("indices",e,t,n),r=I("tensor",e,t,n),a=I("elementShape",e,t,n),o=I("numElements",e,t,n),i=zL(r,s,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=ML(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=PL(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let 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g=I("leakyreluAlpha",e,t,n);return{stride:c,pad:d,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var WL=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=I("stride",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilation",e,t,n);return[Rg(I("x",e,t,n),I("filter",e,t,n),s,r,a,o)]}case"Conv2D":{let 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implemented`)}},nB=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=xh.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=xh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[xh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},sB=(e,t,n)=>{switch(e.op){case"Cast":return[ce(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[zt(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[ct(I("x",e,t,n),s)]}case"Reshape":return[U(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[Ab(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Qr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[ih(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Yp(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[nb(I("x",e,t,n),s,r)]}case"BroadcastTo":return[fc(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[X5(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function m7(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return H(()=>FL(a,o,i));case"basic_math":return H(()=>$L(a,o,i));case"control":return BL(a,o,i);case"convolution":return H(()=>WL(a,o,i));case"creation":return H(()=>VL(a,o,i));case"dynamic":return UL(a,o,i);case"evaluation":return H(()=>HL(a,o,i));case"image":return H(()=>XL(a,o,i));case"graph":return H(()=>GL(a,o,i));case"logical":return H(()=>KL(a,o,i));case"matrices":return H(()=>ZL(a,o,i));case"normalization":return H(()=>YL(a,o,i));case"reduction":return H(()=>JL(a,o,i));case"slice_join":return H(()=>QL(a,o,i));case"sparse":return H(()=>eB(a,o,i));case"spectral":return H(()=>tB(a,o,i));case"string":return H(()=>nB(a,o,i));case"transformation":return H(()=>sB(a,o,i));case"hash_table":return qL(a,o,i,s);case"custom":let l=Uv(a.op);if(l&&l.customExecutor)return l.customExecutor(new DL(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var g7=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),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 e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function A7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(p=>Kn(p)[0]),c=[];s!=null&&(c=s.map(p=>Kn(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((y7(p)||lB(p)||uB(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&u.indexOf(p.name)===-1&&c.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function rB(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>Kn(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return u}var aB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],oB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],iB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function y7(e){return aB.indexOf(e.op)>=0}function lB(e){return oB.indexOf(e.op)>=0}function uB(e){return iB.indexOf(e.op)>=0}var c2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new c2(e.functions[n],this)})}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(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=A7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return rB(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[Kn(c)[0]]),r=t.map(c=>Kn(c)[0]),a=r.map(c=>this.graph.nodes[c]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return H(()=>{let c=new g7(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=Kn(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=m7(m,d,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let A=i.filter(y=>!y7(y)&&!In(y.name,h,t)).map(y=>y.name);if(A.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. 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u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Nr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!In(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!In(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=Kn(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);w.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=Kn(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Kn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},cB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},dB="?tfjs-format=file",pB="model.json",x7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new cB}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Fn.browserHTTPRequest(e,this.loadOptions);else{let t=Fn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Fn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=Fn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new c2(u7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=u7.Instance.transformGraph(e.modelInitializer);this.initializer=new c2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Fn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof qe)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function At(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},uf='"',Gc=Symbol("out"),R7=Symbol("field"),cf=Symbol("quote"),f2=Symbol("quoteafterquote"),_7=Symbol("quoteinquote"),D7=class extends Ql{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new E7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.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 ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Gc;for(let o=0;o<r;o++)switch(a){case Gc:switch(e.charAt(o)){case uf:s=o+1,a=cf;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Gc;break;default:a=R7,s=o;break}break;case R7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=Gc,s=o+1;break;default:}break;case cf:switch(e.charAt(o)){case uf:a=f2;break;default:}break;case f2:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=Gc,s=o+1;break;case uf:a=cf;break;default:a=_7;break}break;case _7:switch(e.charAt(o)){case uf:a=cf;break;default:}break;default:}if(a===f2?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&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}},F7=class extends rn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))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=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.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(e={}){if(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new F7(e);return await t.start(),t}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 e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!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 t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.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 e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},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(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),tn(n,t)}},$7=class extends rn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Lt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=Ps([a,r,i,o],[1,4])}else this.cropBox=Ps([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new $7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=is.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=zt(ce(e,"float32"),0),n;n=_e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},O7=class{},P7=class extends rn{split(e){return new BB(this,e)}},BB=class extends P7{constructor(e,t){super();this.upstream=e,this.impl=new WB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},WB=class extends h2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},VB=class extends rn{decodeUTF8(){return new UB(this)}},UB=class extends P7{constructor(e){super();this.upstream=e,this.impl=new HB(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},HB=class extends h2{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=bx();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},M7=class extends VB{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.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((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function GB(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=jB(e));let r=await w.fetch(n,s);if(r.ok){let a=new Uint8Array(await r.arrayBuffer());return new M7(a,t)}else throw new Error(r.statusText)}var jB=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function z7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var L7=class extends O7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(z7(this.input)&&Y().get("IS_NODE")){let e=vi("fs");this.input=e.readFileSync(this.input.substr(7))}return new M7(this.input,this.options)}},B7=class extends O7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return z7(this.url)?new L7(this.url,this.fileOptions).iterator():GB(this.url,this.fileOptions)}};function qB(e,t={}){return new D7(new B7(e),t)}function XB(e){let t=p2(e);return Zn(async()=>t)}function KB(e){return Zn(async()=>{let t=await e();return p2(()=>t.next())})}async function ZB(e,t){return $7.create(e,t)}async function YB(e){return F7.create(e)}var JB="3.9.0";function Ie(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var QB=rr.whereImpl,df=class extends Mu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new jd(this,Cs())}nextDataId(){return df.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&D.warn(`
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============================
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Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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============================`));let s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return D.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return Cs().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}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(e){Ie([e],"where");let t=this.readSync(e.dataId);return QB(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};df.nextDataId=0;var m2={};ze(m2,{addImpl:()=>V7,bincountImpl:()=>A2,bincountReduceImpl:()=>U7,ceilImpl:()=>H7,concatImpl:()=>y2,equalImpl:()=>G7,expImpl:()=>q7,expm1Impl:()=>K7,floorImpl:()=>Z7,gatherNdImpl:()=>Y7,gatherV2Impl:()=>J7,greaterEqualImpl:()=>ew,greaterImpl:()=>Q7,lessEqualImpl:()=>nw,lessImpl:()=>tw,linSpaceImpl:()=>sw,logImpl:()=>rw,maxImpl:()=>aw,maximumImpl:()=>ow,minimumImpl:()=>iw,multiplyImpl:()=>x2,negImpl:()=>lw,notEqualImpl:()=>uw,prodImpl:()=>cw,rangeImpl:()=>v2,rsqrtImpl:()=>dw,sigmoidImpl:()=>WW,simpleAbsImpl:()=>W7,sliceImpl:()=>ff,sparseFillEmptyRowsImpl:()=>hw,sparseReshapeImpl:()=>fw,sparseSegmentReductionImpl:()=>w2,sqrtImpl:()=>HW,squaredDifferenceImpl:()=>mw,stridedSliceImpl:()=>gw,stringNGramsImpl:()=>Aw,stringSplitImpl:()=>yw,stringToHashBucketFastImpl:()=>xw,subImpl:()=>bw,tileImpl:()=>vw,topKImpl:()=>kw,transposeImpl:()=>b2,uniqueImpl:()=>Iw});function W7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var eW=e=>{let{x:t}=e.inputs,n=e.backend;Ie(t,"abs");let s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=W7(r),n.makeOutput(s,t.shape,"float32")},tW={kernelName:Ii,backendName:"cpu",kernelFunc:eW};function Wt(e){return(t,n,s,r,a)=>{let o=D.assertAndGetBroadcastShape(t,n),i=o.length,l=w.computeStrides(o),u=w.sizeFromShape(o),c=w.getTypedArrayFromDType(a,u),d=t.length,p=n.length,h=w.computeStrides(t),f=w.computeStrides(n),m=D.getBroadcastDims(t,o),g=D.getBroadcastDims(n,o);if(m.length+g.length===0)for(let A=0;A<c.length;++A)c[A]=e(s[A%s.length],r[A%r.length]);else for(let A=0;A<c.length;++A){let y=w.indexToLoc(A,i,l),x=y.slice(-d);m.forEach(S=>x[S]=0);let b=w.locToIndex(x,d,h),v=y.slice(-p);g.forEach(S=>v[S]=0);let k=w.locToIndex(v,p,f);c[A]=e(s[b],r[k])}return[c,o]}}function Yn(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=n.makeTensorInfo(s.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",a),imag:n.makeTensorInfo(r.shape,"float32",o)},i}var nW={kernelName:ep,backendName:"cpu",kernelFunc:Yn};function pf(e,t,n="float32"){if(n==="complex64"){let r=pf(e,t,"float32"),a=pf(e,t,"float32");return Yn({inputs:{real:r,imag:a},backend:e})}let s=w.makeZerosTypedArray(w.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function cr(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var sW={kernelName:qa,backendName:"cpu",kernelFunc:cr};function Zo(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var rW={kernelName:vp,backendName:"cpu",kernelFunc:Zo};function ca(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return cr({inputs:{x:r},backend:n});let o=pf(n,r.shape,r.dtype),i=ca({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Yn({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Zo({inputs:{input:r},backend:n}),i=ca({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=cr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(r.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(r.shape,"int32",i)}if(a==="bool"){let o=n.data.get(r.dataId).values,i=w.toTypedArray([0],r.dtype),[l,u]=Wt((c,d)=>c!==d?1:0)(r.shape,[],o,i,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var aW={kernelName:Da,backendName:"cpu",kernelFunc:ca};function an(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Ie([o,i],e);let 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G7=Wt((e,t)=>e===t?1:0),j7=an(Li,G7,null,"bool"),cW={kernelName:Li,backendName:"cpu",kernelFunc:j7},q7=da(e=>Math.exp(e)),X7=eu(Va,q7),dW={kernelName:Va,backendName:"cpu",kernelFunc:X7},K7=da(e=>Math.expm1(e)),pW=eu(Wi,K7),hW={kernelName:Wi,backendName:"cpu",kernelFunc:pW},Z7=da(e=>Math.floor(e)),fW=eu(Ua,Z7),mW={kernelName:Ua,backendName:"cpu",kernelFunc:fW};function Y7(e,t,n,s,r,a,o,i,l){let u=Ue([s,a],n);for(let c=0;c<s;c++){let d=[],p=0;for(let h=0;h<r;h++){let f=e[c*r+h];p+=f*o[h],d.push(f)}if(p<0||p>=l/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let h=0;h<a;h++)u.values[c*a+h]=t.get(...t.indexToLoc(p*a+h))}return u}function J7(e,t,n){let s=Ue(n,e.dtype);for(let r=0;r<s.size;++r){let o=s.indexToLoc(r).slice(),i=o[0],l=o[2],u=t.locToIndex([i,l]);o[2]=t.values[u];let c=e.locToIndex(o);s.values[r]=e.values[c]}return s}var Q7=Wt((e,t)=>e>t?1:0),gW=an(Gi,Q7,null,"bool"),AW={kernelName:Gi,backendName:"cpu",kernelFunc:gW},ew=Wt((e,t)=>e>=t?1:0),yW=an(ja,ew,null,"bool"),xW={kernelName:ja,backendName:"cpu",kernelFunc:yW},tw=Wt((e,t)=>e<t?1:0),bW=an(Ki,tw,null,"bool"),vW={kernelName:Ki,backendName:"cpu",kernelFunc:bW},nw=Wt((e,t)=>e<=t?1:0),wW=an(Zi,nw,null,"bool"),kW={kernelName:Zi,backendName:"cpu",kernelFunc:wW};function sw(e,t,n){let s=(t-e)/(n-1),r=w.makeZerosTypedArray(n,"float32");r[0]=e;for(let a=1;a<r.length;a++)r[a]=r[a-1]+s;return r}var rw=da(e=>Math.log(e)),IW=eu(Ka,rw),SW={kernelName:Ka,backendName:"cpu",kernelFunc:IW};function aw(e,t,n,s){let r=w.getTypedArrayFromDType(s,w.sizeFromShape(n));for(let a=0;a<r.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let u=e[o+l];(Number.isNaN(u)||u>i)&&(i=u)}r[a]=i}return r}var ow=Wt((e,t)=>Math.max(e,t)),CW=an(Ya,ow),TW={kernelName:Ya,backendName:"cpu",kernelFunc:CW},iw=Wt((e,t)=>Math.min(e,t)),NW=an(to,iw),EW={kernelName:to,backendName:"cpu",kernelFunc:NW},x2=Wt((e,t)=>e*t),RW=g2((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),hf=an(so,x2,RW),_W={kernelName:so,backendName:"cpu",kernelFunc:hf};function lw(e,t,n){let s=w.createScalarValue(-1,n);return x2([],t,s,e,n)}function DW(e){let{inputs:t,backend:n}=e,{x:s}=t;Ie(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=lw(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var FW={kernelName:el,backendName:"cpu",kernelFunc:DW},uw=Wt((e,t)=>e!==t?1:0),$W=an(tl,uw,null,"bool"),OW={kernelName:tl,backendName:"cpu",kernelFunc:$W};function b2(e,t,n,s,r){let a=t.length,o=w.sizeFromShape(t),i=w.computeStrides(t),l=w.computeStrides(r),u=w.getTypedArrayFromDType(n,w.sizeFromShape(r));for(let c=0;c<o;++c){let d=w.indexToLoc(c,a,i),p=new Array(d.length);for(let f=0;f<p.length;f++)p[f]=d[s[f]];let 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p=c.indexToLoc(d),h=p.map((f,m)=>f+t[m]);c.set(u.get(...h),...p)}return r==="string"?D.fromStringArrayToUint8(c.values):c.values}function Yo(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;Ie(r,"slice");let[i,l]=vn.parseSliceParams(r,a,o);vn.assertParamsValid(r,i,l);let u=n.data.get(r.dataId).values,c=ff(u,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}var UW={kernelName:hl,backendName:"cpu",kernelFunc:Yo};function hw(e,t,n,s,r,a,o){let i=t[0],l=a[0],u=new Array(l),c=new Array(i),d=t[1];if(l===0){if(i!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
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indices.shape[0] = ${i}`);let g=w.getArrayFromDType(n,0),A=w.getArrayFromDType(r,0);return[g,[0,d],A,u,c]}let p=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let A=e[g*d];if(A<0)throw new Error(`indices(${g}, 0) is invalid: ${A} < 0`);if(A>=l)throw new Error(`indices(${g}, 0) is invalid: ${A} >= ${l}`);++f[A],p=p&&A>=h,h=A}let m=!0;for(let g=0;g<l;++g){let A=f[g]===0;u[g]=A,m=m&&!A,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,A=s;for(let y=0;y<i;++y)c[y]=y;return[g,[i,d],A,u,c]}else{let g=f[l-1],A=w.getArrayFromDType(n,g*d),y=w.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let b=0;b<i;++b){let v=e[b*d],k=x[v],S=(v===0?0:f[v-1])+k;x[v]++;for(let C=0;C<d;++C)A[S*d+C]=e[b*d+C];y[S]=s[b],c[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let k=b===0?0:f[b-1];A[k*d+0]=b;for(let S=1;S<d;++S)A[k*d+S]=0;y[k]=o}return[A,[g,d],y,u,c]}}function fw(e,t,n,s,r){let a=w.sizeFromShape(s),o=t[0],i=r.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let A=r[g];if(A===-1){if(c!==-1)throw new Error(`only one output dimension may be -1, not both ${c} and ${g}`);c=g,l.push(1)}else{if(A<0)throw new Error(`size ${g} must be non-negative, not ${A}`);u*=A,l.push(A)}}if(c!==-1){if(u<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/u);if(u*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
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dense values, but the requested shape requires a multiple of ${u}. inputShape=${s} outputShape= ${l}`);l[c]=g}let d=w.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${s} outputShape=${l}`);let p=s.length,h=[];if(p>0){h[p-1]=1;for(let g=p-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=w.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let A=0;for(let y=0;y<p;++y)A+=e[g*p+y]*h[y];for(let y=0;y<i;++y)m[g*i+y]=Math.trunc(A/f[y]),A%=f[y]}return[m,[o,i],l]}function w2(e,t,n,s,r,a=!1,o=0){let i=s.length;if(i!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],u=l[1],d=i>0?r[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let h=p.reduce((x,b)=>x*b,1),f=w.getArrayFromDType(n,h);if(i===0)return d>0&&f.fill(o),[f,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,A=0,y=r[m];for(;;){let x=0;if(g<i){if(x=r[g],y===x){++g;continue}if(y>=x)throw new Error("segment ids are not increasing")}if(y<0||y>=d)throw new Error(`Segment id ${y} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);y>A&&f.fill(o,A*u,y*u);for(let b=m;b<g;++b){let v=s[b];if(v<0||v>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<u;k++)f[y*u+k]+=e[v*u+k]}if(a)for(let b=0;b<u;b++)f[y*u+b]/=g-m;if(m=g,++g,A=y+1,y=x,g>i)break}return A<d&&f.fill(o,A*u,d*u),[f,p]}var HW=da(e=>Math.sqrt(e)),GW=lt(Ao,e=>Math.sqrt(e)),jW={kernelName:Ao,backendName:"cpu",kernelFunc:GW},mw=Wt((e,t)=>{let n=e-t;return n*n}),qW=an(bo,mw),XW={kernelName:bo,backendName:"cpu",kernelFunc:qW};function gw(e,t,n,s){let r=Ue(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var KW=class{constructor(e,t,n,s,r,a){this.separator=w.encodeString(e),this.nGramWidths=t,this.leftPad=w.encodeString(n),this.rightPad=w.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),u=Math.max(0,i-(r-(o+1))),c=a-(l+u),d=t+(l>0?0:o-i),p=0;p+=l*this.leftPad.length;for(let A=0;A<c;++A)p+=e[d+A].length;p+=u*this.rightPad.length,p+=(l+u+c-1)*this.separator.length,n[s+o]=new Uint8Array(p);let f=n[s+o],m=0,g=A=>A.forEach(y=>f[m++]=y);for(let A=0;A<l;++A)g(this.leftPad),g(this.separator);for(let A=0;A<c-1;++A)g(e[d+A]),g(this.separator);if(c>0){g(e[d+c-1]);for(let A=0;A<u;++A)g(this.separator),g(this.rightPad)}else{for(let A=0;A<u-1;++A)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let u=t[l]>=i;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. 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a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(r,a);(!n||o.length!==0)&&s.push(o),r=a+1}}function yw(e,t,n){let s=e.length,r=[],a=0,o=0,i=new Array(s);for(let p=0;p<s;++p){let h=r.length;ZW(e[p],t,n,r);let f=r.length-h;i[p]=f,a+=f,o=Math.max(o,f)}let l=w.getArrayFromDType("int32",a*2),u=new Array(a),c=[s,o],d=0;for(let p=0;p<s;++p)for(let h=0;h<i[p];++h)l[d*2]=p,l[d*2+1]=h,u[d]=r[d],++d;return[l,u,c]}function xw(e,t){let n=w.getArrayFromDType("int32",e.length);for(let s=0;s<e.length;++s)n[s]=w.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();return n}var bw=Wt((e,t)=>e-t),YW=g2((e,t,n,s)=>({real:e-n,imag:t-s})),k2=an(vo,bw,YW),JW={kernelName:vo,backendName:"cpu",kernelFunc:k2};function vw(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let s=Ue(n,e.dtype);for(let r=0;r<s.values.length;++r){let a=s.indexToLoc(r),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);s.values[r]=e.values[i]}return s}var qc=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function ww(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let i=s-n+1,l=t-n+1,u=Math.log(i),c=.5*Math.exp(2*u/3),d=.5*Math.sqrt(u*c*(i-c)/i)*Math.sign(l-i/2),p=Math.max(n,Math.floor(t-l*c/i+d)),h=Math.min(s,Math.floor(t+(i-l)*c/i+d));ww(e,t,p,h)}let r=e[t],a=n,o=s;for(w.swap(e,n,t),qc(e[s],r)>0&&w.swap(e,n,s);a<o;){for(w.swap(e,a,o),a++,o--;qc(e[a],r)<0;)a=a+1;for(;qc(e[o],r)>0;)o=o-1}qc(e[n],r)===0?w.swap(e,n,o):(o=o+1,w.swap(e,o,s)),o<=t&&(n=o+1),t<=o&&(s=o-1)}}function kw(e,t,n,s,r){let a=t[t.length-1],[o,i]=[e.length/a,a],l=w.getTypedArrayFromDType(n,o*s),u=w.getTypedArrayFromDType("int32",o*s);for(let d=0;d<o;d++){let p=d*i,h=e.subarray(p,p+i),f=new Array(h.length);h.forEach((y,x)=>f[x]={value:y,index:x}),s<f.length&&(ww(f,s),f=f.slice(0,s)),r&&f.sort(qc);let m=d*s,g=l.subarray(m,m+s),A=u.subarray(m,m+s);for(let y=0;y<s;y++)g[y]=f[y].value,A[y]=f[y].index}let c=t.slice();return c[c.length-1]=s,[Ue(c,n,l),Ue(c,"int32",u)]}function Iw(e,t,n,s){let r=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[r]),l=new Gt(a,s,e),u=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let A=0;A<a[0];A++)for(let y=0;y<a[2];y++)g.push(l.get(A,f,y));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,u.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new Gt(d,s);u.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let A=0;A<a[2];A++)p.set(l.get(g,f,A),g,m,A)});let h=n.slice();return h[r]=d[1],{outputValues:p.values,outputShape:h,indices:i}}var QW="3.9.0";Dl("cpu",()=>new df,1);var Sw=lt(Wa,e=>e>=0?e:Math.exp(e)-1),eV={kernelName:Wa,backendName:"cpu",kernelFunc:Sw};function Cw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ie([r],"leakyRelu");let o=w.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=w.getTypedArrayFromDType("float32",o);for(let u=0;u<i.length;u++)l[u]=i[u]<0?a*i[u]:i[u];return n.makeTensorInfo(r.shape,"float32",l)}var tV={kernelName:Xa,backendName:"cpu",kernelFunc:Cw},nV=Wt((e,t)=>e<0?t*e:e);function Tw(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Ie([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=nV(s.shape,r.shape,a,o,s.dtype);return n.makeTensorInfo(l,s.dtype,i)}var sV={kernelName:io,backendName:"cpu",kernelFunc:Tw},Nw=lt(lo,e=>Math.max(0,e)),rV={kernelName:lo,backendName:"cpu",kernelFunc:Nw},Ew=lt(co,e=>Math.min(Math.max(0,e),6)),aV={kernelName:co,backendName:"cpu",kernelFunc:Ew};function I2(e,t,n,s,r){if(n==="linear")return cr({inputs:{x:t},backend:e});if(n==="relu")return Nw({inputs:{x:t},backend:e});if(n==="elu")return Sw({inputs:{x:t},backend:e});if(n==="relu6")return Ew({inputs:{x:t},backend:e});if(n==="prelu")return Tw({inputs:{x:t,alpha:s},backend:e});if(n==="leakyrelu")return Cw({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return pw({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function vt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=w.sizeFromShape(r.shape),i=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(i);w.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. 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nt=0;for(let ut=Fe;ut<dt;ut++){let je=Math.min(Ae,g-1)*q,Rn=Math.min(Ae,A-1)*se,Tt=T[je+rt*X+ut*ee],Vn=P[ut*te+at*ne+Rn];nt+=Tt*Vn}le[Ae*Q+(rt*E+at)]+=nt}}return n.disposeIntermediateTensorInfo(S),n.disposeIntermediateTensorInfo(C),n.makeTensorInfo(b,ie.dtype,ie.values)}var iV={kernelName:_a,backendName:"cpu",kernelFunc:Rw};function lV(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,p,h,f,m=[];p=Rw({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:u},backend:n}),o&&(h=jc({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),c&&(f=I2(n,p,c,i,d),m.push(p),p=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return p}var uV={kernelName:So,backendName:"cpu",kernelFunc:lV},cV=lt(Si,e=>Math.acos(e)),dV={kernelName:Si,backendName:"cpu",kernelFunc:cV},pV=lt(Ci,e=>Math.acosh(e)),hV={kernelName:Ci,backendName:"cpu",kernelFunc:pV};function fV(e){let{inputs:t,backend:n}=e,s=t;Ie(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=Ue(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let u=0;u<o.length;u++)o[u]+=l[u]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var mV={kernelName:Na,backendName:"cpu",kernelFunc:fV};function gV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ie(r,"all");let i=w.parseAxisParam(a,r.shape),l=i,u=D.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=gs({inputs:{x:r},backend:n,attrs:{perm:u}}),l=D.getInnerMostAxes(l.length,r.shape.length)),D.assertAxesAreInnerMostDims("all",l,c.shape.length);let[d,p]=D.computeOutAndReduceShapes(c.shape,l),h=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let A=0;A<f.length;++A){let y=A*h,x=m[y];for(let b=0;b<h;++b){let v=m[y+b];x=x&&v}f[A]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let A=D.expandShapeToKeepDim(d,i),y=vt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),y}return g}var AV={kernelName:Ti,backendName:"cpu",kernelFunc:gV};function yV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ie(r,"any");let i=w.parseAxisParam(a,r.shape),l=i,u=D.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=gs({inputs:{x:r},backend:n,attrs:{perm:u}}),l=D.getInnerMostAxes(l.length,r.shape.length)),D.assertAxesAreInnerMostDims("any",l,c.shape.length);let[d,p]=D.computeOutAndReduceShapes(c.shape,l),h=w.sizeFromShape(p),f=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),m=n.data.get(c.dataId).values;for(let A=0;A<f.length;++A){let y=A*h,x=m[y];for(let b=0;b<h;++b){let v=m[y+b];x=x||v}f[A]=x}u!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(d,c.dtype,f);if(o){let A=D.expandShapeToKeepDim(d,i),y=vt({inputs:{x:g},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(g),y}return g}var xV={kernelName:Ni,backendName:"cpu",kernelFunc:yV};function bV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ie(r,"argMax");let o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=gs({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],D.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[c,d]=D.computeOutAndReduceShapes(l.shape,o),p=w.sizeFromShape(c),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,y=m[A],x=0;for(let b=0;b<f;++b){let v=m[A+b];v>y&&(y=v,x=b)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var vV={kernelName:Ea,backendName:"cpu",kernelFunc:bV};function wV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ie(r,"argMin");let o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=gs({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],D.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[c,d]=D.computeOutAndReduceShapes(l.shape,o),p=w.sizeFromShape(c),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,y=m[A],x=0;for(let b=0;b<f;++b){let v=m[A+b];v<y&&(y=v,x=b)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var kV={kernelName:Bu,backendName:"cpu",kernelFunc:wV},IV=lt(Ei,e=>Math.asin(e)),SV={kernelName:Ei,backendName:"cpu",kernelFunc:IV},CV=lt(Ri,e=>Math.asinh(e)),TV={kernelName:Ri,backendName:"cpu",kernelFunc:CV},NV=lt(_i,e=>Math.atan(e)),EV={kernelName:_i,backendName:"cpu",kernelFunc:NV},RV=Wt((e,t)=>Math.atan2(e,t)),_V=an(Fi,RV),DV={kernelName:Fi,backendName:"cpu",kernelFunc:_V},FV=lt(Di,e=>Math.atanh(e)),$V={kernelName:Di,backendName:"cpu",kernelFunc:FV};function S2(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ue(r.outShape,n),g=m.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;b<r.batchSize;++b){let v=b*A,k=b*s[0];for(let S=0;S<r.inChannels;++S)for(let C=0;C<r.outHeight;++C){let _=C*o-p,O=Math.max(0,_),E=Math.min(r.inHeight,c+_),R=v+C*y;for(let T=0;T<r.outWidth;++T){let P=T*i-h,V=Math.max(0,P),j=Math.min(r.inWidth,d+P),q=f,X=0,ee=0;for(let ne=O;ne<E;ne+=l){let se=k+ne*s[1];for(let Q=V;Q<j;Q+=u){let ie=se+Q*s[2],le=e[ie+S];a==="max"&&le>q?q=le:a==="avg"&&(X+=le,ee++)}if(isNaN(q))break}let te=R+T*x+S;g[te]=a==="avg"?X/ee:q}}}return m}function _w(e,t,n,s,r=!1,a=!1){let o=Ue(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,u=s.dilationHeight,c=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=Ue(t,n,e);for(let g=0;g<s.batchSize;++g)for(let A=0;A<s.inChannels;++A)for(let y=0;y<s.outHeight;++y){let x=y*i-h,b=x;for(;b<0;)b+=u;let v=Math.min(s.inHeight,d+x);for(let k=0;k<s.outWidth;++k){let S=k*l-f,C=S;for(;C<0;)C+=c;let _=Math.min(s.inWidth,p+S),O=Number.NEGATIVE_INFINITY,E=-1;for(let R=b;R<v;R+=u){let T=R-x;for(let P=C;P<_;P+=c){let V=P-S,j=m.get(g,R,P,A);j>O&&(O=j,r?E=a?((g*s.inHeight+R)*s.inWidth+P)*s.inChannels+A:(R*s.inWidth+P)*s.inChannels+A:E=T*p+V)}}o.set(E,g,y,k,A)}}return o}function Dw(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,c=r.dilationHeight,d=r.dilationWidth,p=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,A=r.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Ue(r.outShape,n),b=x.values,v=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],S=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let _=0;_<r.batchSize;++_){let O=_*v,E=_*s[0];for(let R=0;R<r.inChannels;++R)for(let T=0;T<r.outDepth;++T){let P=T*o-m,V=P;for(;V<0;)V+=u;let j=Math.min(r.inDepth,p+P),q=O+T*k;for(let X=0;X<r.outHeight;++X){let ee=X*i-g,te=ee;for(;te<0;)te+=c;let ne=Math.min(r.inHeight,h+ee),se=q+X*S;for(let Q=0;Q<r.outWidth;++Q){let ie=Q*l-A,le=ie;for(;le<0;)le+=d;let pe=Math.min(r.inWidth,f+ie),Ae=se+Q*C,Te=y,Ne=0,Fe=0;for(let Pe=V;Pe<j;Pe+=u){let dt=E+Pe*s[1];for(let rt=te;rt<ne;rt+=c){let at=dt+rt*s[2];for(let nt=le;nt<pe;nt+=d){let ut=at+nt*s[3],je=e[ut+R];if(a==="max"&&je>Te?Te=je:a==="avg"&&(Ne+=je,Fe++),isNaN(Te))break}if(isNaN(Te))break}if(isNaN(Te))break}let Le=Ae+R;b[Le]=a==="avg"?Ne/Fe:Te}}}}return x}function OV(e,t){let n=Ue(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let A=0;A<t.outDepth;++A){let y=A*s-p,x=y;for(;x<0;)x+=o;let b=Math.min(t.inDepth,u+y);for(let v=0;v<t.outHeight;++v){let k=v*r-h,S=k;for(;S<0;)S+=i;let C=Math.min(t.inHeight,c+k);for(let _=0;_<t.outWidth;++_){let O=_*a-f,E=O;for(;E<0;)E+=l;let R=Math.min(t.inWidth,d+O),T=Number.NEGATIVE_INFINITY,P=-1;for(let V=x;V<b;V+=o){let j=V-y;for(let q=S;q<C;q+=i){let X=q-k;for(let ee=E;ee<R;ee+=l){let te=ee-O,ne=e.get(m,V,q,ee,g);ne>=T&&(T=ne,P=j*c*d+X*c+te)}}}n.set(P,m,A,v,_,g)}}}return n}function PV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ie(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=cr({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),f=S2(p,r.shape,r.dtype,h,c,"avg");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var MV={kernelName:Ra,backendName:"cpu",kernelFunc:PV};function zV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;Ie(r,"avgPool3d");let c=D.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,p=Dw(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var LV={kernelName:Wu,backendName:"cpu",kernelFunc:zV};function BV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;Ie([r,a],"avgPool3DGrad");let c=D.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,p=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,A=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,S=b-1-c.padInfo.front,C=k-1-c.padInfo.left,_=v-1-c.padInfo.top,O=Ue(a.shape,"float32"),E=1/(f*m*g),R=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let P=0;P<c.inChannels;++P)for(let V=0;V<c.inDepth;++V)for(let j=0;j<c.inHeight;++j)for(let q=0;q<c.inWidth;++q){let X=V-S,ee=j-_,te=q-C,ne=0;for(let se=0;se<b;se+=A){let Q=(X+se)/d;if(!(Q<0||Q>=c.outDepth||Math.floor(Q)!==Q))for(let ie=0;ie<v;ie+=y){let le=(ee+ie)/p;if(!(le<0||le>=c.outHeight||Math.floor(le)!==le))for(let pe=0;pe<k;pe+=x){let Ae=(te+pe)/h;if(Ae<0||Ae>=c.outWidth||Math.floor(Ae)!==Ae)continue;ne+=R.get(T,Q,le,Ae,P)}}}O.set(ne*E,T,V,j,q,P)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var WV={kernelName:Jd,backendName:"cpu",kernelFunc:BV};function VV(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ie([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=D.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,A=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=A-1-c.padInfo.top,v=Ue(o.shape,"float32"),k=1/(h*f),S=n.data.get(r.dataId).values,C=Ue(r.shape,"float32",S);for(let _=0;_<c.batchSize;++_)for(let O=0;O<c.inChannels;++O)for(let E=0;E<c.inHeight;++E)for(let R=0;R<c.inWidth;++R){let T=E-b,P=R-x,V=0;for(let j=0;j<A;j+=m){let q=(T+j)/d;if(!(q<0||q>=c.outHeight||Math.floor(q)!==q))for(let X=0;X<y;X+=g){let ee=(P+X)/p;if(ee<0||ee>=c.outWidth||Math.floor(ee)!==ee)continue;V+=C.get(_,q,ee,O)}}v.set(V*k,_,E,R,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var UV={kernelName:Yd,backendName:"cpu",kernelFunc:VV};function HV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;w.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ie([r,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=s;u==null&&(u=.001);let c=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(l.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,A=h.length,y=p.length,x=d.length,b=0,v=0,k=0,S=0;for(let C=0;C<c.length;++C)m[C]=f[b++]+(c[C]-d[v++])*h[k++]/Math.sqrt(p[S++]+u),b>=g&&(b=0),v>=x&&(v=0),k>=A&&(k=0),S>=y&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var GV={kernelName:Ga,backendName:"cpu",kernelFunc:HV};function jV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ie([r],"batchToSpaceND");let i=a.reduce((A,y)=>A*y),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),p=D.getSliceSize(c,o,a.length),h=vt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=gs({inputs:{x:h},backend:n,attrs:{perm:u}}),m=vt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=Yo({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var qV={kernelName:$i,backendName:"cpu",kernelFunc:jV};function XV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,u=A2(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var KV={kernelName:Qd,backendName:"cpu",kernelFunc:XV};function ZV(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=D.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var YV={kernelName:Hm,backendName:"cpu",kernelFunc:ZV},JV=lt(Vr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),QV={kernelName:Vr,backendName:"cpu",kernelFunc:JV},eU=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],d=l[u];s[u]=Math.hypot(c,d)}return n.makeOutput(s,t.shape,"float32")},tU={kernelName:Vu,backendName:"cpu",kernelFunc:eU};function tu(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var nU={kernelName:fp,backendName:"cpu",kernelFunc:tu};function nu(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=D.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return cr({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(D.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>Zo({inputs:{input:b},backend:n})),g=i.map(b=>tu({inputs:{input:b},backend:n})),A=nu({inputs:m,backend:n,attrs:{axis:a}}),y=nu({inputs:g,backend:n,attrs:{axis:a}}),x=Yn({inputs:{real:A,imag:y},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),x}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return vt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=D.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,p=y2(c,o,t[0].dtype,d),h=D.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var sU={kernelName:Oi,backendName:"cpu",kernelFunc:nu};function Fw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;Ie([r,a],"conv2d");let d=D.convertConv2DDataFormat(l),p=D.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,y=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new Gt(p.outShape,r.dtype),v=w.computeStrides(r.shape),k=w.computeStrides(a.shape),S=v[0],C=x?v[1]:v[2],_=x?v[2]:1,O=x?1:v[1],E=b.strides[0],R=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,P=x?1:b.strides[1],V=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X<p.batchSize;++X){let ee=X*S,te=X*E;for(let ne=0;ne<p.outHeight;++ne){let se=te+ne*R,Q=ne*p.strideHeight-y;for(let ie=0;ie<h;++ie){let le=Q+ie*m;if(le<0||le>=p.inHeight)continue;let pe=ie*k[0],Ae=ee+le*C;for(let Te=0;Te<p.outWidth;++Te){let Ne=se+Te*T,Fe=Te*p.strideWidth-A;for(let Le=0;Le<f;++Le){let Pe=Fe+Le*g;if(Pe<0||Pe>=p.inWidth)continue;let dt=pe+Le*k[1],rt=Ae+Pe*_,at=dt;for(let nt=0;nt<p.inChannels;++nt){let ut=V[rt+nt*O];for(let je=0;je<p.outChannels;++je)q[Ne+je*P]+=ut*j[at+je];at+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,q)}var rU={kernelName:$a,backendName:"cpu",kernelFunc:Fw};function aU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s;Ie([r,a],"conv2dBackpropFilter");let d=D.convertConv2DDataFormat(l),p=D.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,A=p.dataFormat==="channelsLast",y=new Gt(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=new Gt(r.shape,r.dtype,v),C=new Gt(a.shape,a.dtype,k);for(let _=0;_<m;++_){let O=Math.max(0,Math.ceil((b-_)/h)),E=Math.min(p.outHeight,(p.inHeight+b-_)/h);for(let R=0;R<g;++R){let T=Math.max(0,Math.ceil((x-R)/f)),P=Math.min(p.outWidth,(p.inWidth+x-R)/f);for(let V=0;V<p.inChannels;++V)for(let j=0;j<p.outChannels;++j){let q=0;for(let X=0;X<p.batchSize;++X)for(let ee=O;ee<E;++ee){let te=_+ee*h-b;for(let ne=T;ne<P;++ne){let se=R+ne*f-x;A?q+=S.get(X,te,se,V)*C.get(X,ee,ne,j):q+=S.get(X,V,te,se)*C.get(X,j,ee,ne)}}y.set(q,_,R,V,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var oU={kernelName:tp,backendName:"cpu",kernelFunc:aU};function iU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s;Ie([r,a],"conv2dBackpropInput");let d=w.computeStrides(a.shape),p=w.computeStrides(r.shape),h=D.convertConv2DDataFormat(u),f=D.computeConv2DInfo(o,a.shape,i,1,l,c,!1,h),m=new Gt(f.inShape,"float32"),g=m.values,A=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[x,b,v]=d,{batchSize:k,filterHeight:S,filterWidth:C,inChannels:_,inHeight:O,inWidth:E,outChannels:R,outHeight:T,outWidth:P,strideHeight:V,strideWidth:j}=f;h=f.dataFormat;let q=S-1-f.padInfo.top,X=C-1-f.padInfo.left,ee=h==="channelsLast",te=m.strides[0],ne=ee?m.strides[1]:m.strides[2],se=ee?m.strides[2]:1,Q=ee?1:m.strides[1],ie=p[0],le=ee?p[1]:p[2],pe=ee?p[2]:1,Ae=ee?1:p[1];for(let Te=0;Te<k;++Te)for(let Ne=0;Ne<_;++Ne)for(let Fe=0;Fe<O;++Fe){let Le=Fe-q,Pe=Math.max(0,Math.ceil(Le/V)),dt=Math.min(T,(S+Le)/V);for(let rt=0;rt<E;++rt){let at=rt-X,nt=Math.max(0,Math.ceil(at/j)),ut=Math.min(P,(C+at)/j),je=0;for(let Tt=Pe;Tt<dt;++Tt){let Vn=Tt*V-Le;for(let on=nt;on<ut;++on){let ws=on*j-at,An=ie*Te+le*Tt+pe*on,ns=x*(S-1-Vn)+b*(C-1-ws)+v*Ne;for(let ss=0;ss<R;++ss){let ln=A[An+Ae*ss],rs=y[ns+ss];je+=ln*rs}}}let Rn=te*Te+ne*Fe+se*rt+Q*Ne;g[Rn]=je}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var lU={kernelName:Oa,backendName:"cpu",kernelFunc:iU};function uU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Ie([r,a],"conv3d");let u=D.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:c,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=u,A=g.front,y=g.left,x=g.top,b=new Gt(u.outShape,r.dtype),v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=b.values,C=w.computeStrides(r.shape),_=w.computeStrides(a.shape);for(let O=0;O<u.batchSize;++O){let E=O*C[0],R=O*b.strides[0];for(let T=0;T<u.outDepth;++T){let P=R+T*b.strides[1],V=T*u.strideDepth-A;for(let j=0;j<c;++j){let q=V+j*h;if(q<0||q>=u.inDepth)continue;let X=j*_[0],ee=E+q*C[1];for(let te=0;te<u.outHeight;++te){let ne=P+te*b.strides[2],se=te*u.strideHeight-x;for(let Q=0;Q<d;++Q){let ie=se+Q*f;if(ie<0||ie>=u.inHeight)continue;let le=X+Q*_[1],pe=ee+ie*C[2];for(let Ae=0;Ae<u.outWidth;++Ae){let Te=ne+Ae*u.outChannels,Ne=Ae*u.strideWidth-y;for(let Fe=0;Fe<p;++Fe){let Le=Ne+Fe*m;if(Le<0||Le>=u.inWidth)continue;let Pe=le+Fe*_[2],dt=pe+Le*u.inChannels,rt=Pe;for(let at=0;at<u.inChannels;++at){let nt=v[dt+at];for(let ut=0;ut<u.outChannels;++ut)S[Te+ut]+=nt*k[rt+ut];rt+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var cU={kernelName:Uu,backendName:"cpu",kernelFunc:uU};function dU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Ie([r,a],"conv3dBackpropFilterV2");let u=w.computeStrides(r.shape),c=w.computeStrides(a.shape),d=D.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,A=d.filterWidth,y=new Gt(d.filterShape,"float32"),x=y.values,[b,v,k,S]=y.strides,C=n.data.get(a.dataId).values,[_,O,E,R]=c,T=n.data.get(r.dataId).values,[P,V,j,q]=u,X=d.padInfo.front,ee=d.padInfo.left,te=d.padInfo.top;for(let ne=0;ne<m;++ne){let se=Math.max(0,Math.ceil((X-ne)/p)),Q=Math.min(d.outDepth,(d.inDepth+X-ne)/p),ie=ne*b;for(let le=0;le<g;++le){let pe=Math.max(0,Math.ceil((te-le)/h)),Ae=Math.min(d.outHeight,(d.inHeight+te-le)/h),Te=le*v+ie;for(let Ne=0;Ne<A;++Ne){let Fe=Math.max(0,Math.ceil((ee-Ne)/f)),Le=Math.min(d.outWidth,(d.inWidth+ee-Ne)/f),Pe=Ne*k+Te;for(let dt=0;dt<d.inChannels;++dt){let rt=dt*S+Pe;for(let at=0;at<d.outChannels;++at){let nt=0;for(let ut=0;ut<d.batchSize;++ut){let je=ut*P,Rn=ut*_;for(let Tt=se;Tt<Q;++Tt){let on=(ne+Tt*p-X)*V+je,ws=Tt*O+Rn;for(let An=pe;An<Ae;++An){let ss=(le+An*h-te)*j+on,ln=An*E+ws;for(let rs=Fe;rs<Le;++rs){let Un=(Ne+rs*f-ee)*q+ss,Xs=rs*R+ln;nt+=T[Un+dt]*C[Xs+at]}}}}x[rt+at]=nt}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var pU={kernelName:np,backendName:"cpu",kernelFunc:dU};function hU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ie([r],"conv3dBackpropInputV2");let u=w.computeStrides(r.shape),c=w.computeStrides(a.shape),d=D.computeConv3DInfo(l,a.shape,i,1,o),p=new Gt(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,y=n.data.get(r.dataId).values,[x,b,v,k]=u,S=n.data.get(a.dataId).values,[C,_,O,E]=c,{batchSize:R,filterDepth:T,filterHeight:P,filterWidth:V,inChannels:j,inDepth:q,inHeight:X,inWidth:ee,outChannels:te,outDepth:ne,outHeight:se,outWidth:Q,strideDepth:ie,strideHeight:le,strideWidth:pe}=d,Ae=T-1-d.padInfo.front,Te=P-1-d.padInfo.top,Ne=V-1-d.padInfo.left;for(let Fe=0;Fe<R;++Fe)for(let Le=0;Le<j;++Le)for(let Pe=0;Pe<q;++Pe){let dt=Pe-Ae,rt=Math.max(0,Math.ceil(dt/ie)),at=Math.min(ne,(T+dt)/ie);for(let nt=0;nt<X;++nt){let ut=nt-Te,je=Math.max(0,Math.ceil(ut/le)),Rn=Math.min(se,(P+ut)/le);for(let Tt=0;Tt<ee;++Tt){let Vn=Tt-Ne,on=Math.max(0,Math.ceil(Vn/pe)),ws=Math.min(Q,(V+Vn)/pe),An=0;for(let ns=rt;ns<at;++ns){let ss=ns*ie-dt;for(let ln=je;ln<Rn;++ln){let rs=ln*le-ut;for(let as=on;as<ws;++as){let Un=as*pe-Vn,Xs=x*Fe+b*ns+v*ln+k*as,mr=C*(T-1-ss)+_*(P-1-rs)+O*(V-1-Un)+E*Le;for(let Fr=0;Fr<te;++Fr){let ui=y[Xs+Fr],Ks=S[mr+Fr];An+=ui*Ks}}}}h[f*Fe+m*Pe+g*nt+A*Tt+Le]=An}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var fU={kernelName:sp,backendName:"cpu",kernelFunc:hU},mU=lt(Pa,e=>Math.cos(e)),gU={kernelName:Pa,backendName:"cpu",kernelFunc:mU},AU=lt(Ma,e=>Math.cosh(e)),yU={kernelName:Ma,backendName:"cpu",kernelFunc:AU};function xU(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,A=Ue([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=w.computeStrides(r.shape),k=w.computeStrides(A.shape);for(let S=0;S<f;S++){let C=S*4,_=y[C],O=y[C+1],E=y[C+2],R=y[C+3],T=x[S];if(T>=c)continue;let P=m>1?(E-_)*(d-1)/(m-1):0,V=g>1?(R-O)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let q=m>1?_*(d-1)+j*P:.5*(_+E)*(d-1);if(q<0||q>d-1){for(let X=0;X<g;X++)for(let ee=0;ee<h;ee++){let te=ee+X*k[2]+j*k[1]+S*k[0];A.values[te]=u}continue}if(l==="bilinear"){let X=Math.floor(q),ee=Math.ceil(q),te=q-X;for(let ne=0;ne<g;ne++){let se=g>1?O*(p-1)+ne*V:.5*(O+R)*(p-1);if(se<0||se>p-1){for(let pe=0;pe<h;pe++){let Ae=pe+ne*k[2]+j*k[1]+S*k[0];A.values[Ae]=u}continue}let Q=Math.floor(se),ie=Math.ceil(se),le=se-Q;for(let pe=0;pe<h;pe++){let Ae=pe+Q*v[2]+X*v[1]+T*v[0],Te=b[Ae];Ae=pe+ie*v[2]+X*v[1]+T*v[0];let Ne=b[Ae];Ae=pe+Q*v[2]+ee*v[1]+T*v[0];let Fe=b[Ae];Ae=pe+ie*v[2]+ee*v[1]+T*v[0];let Le=b[Ae],Pe=Te+(Ne-Te)*le,dt=Fe+(Le-Fe)*le;Ae=pe+ne*k[2]+j*k[1]+S*k[0],A.values[Ae]=Pe+(dt-Pe)*te}}}else for(let X=0;X<g;++X){let ee=g>1?O*(p-1)+X*V:.5*(O+R)*(p-1);if(ee<0||ee>p-1){for(let se=0;se<h;se++){let Q=se+X*k[2]+j*k[1]+S*k[0];A.values[Q]=u}continue}let te=Math.round(ee),ne=Math.round(q);for(let se=0;se<h;se++){let Q=se+te*v[2]+ne*v[1]+T*v[0],ie=se+X*k[2]+j*k[1]+S*k[0];A.values[ie]=b[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var bU={kernelName:Pi,backendName:"cpu",kernelFunc:xU};function vU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ie(r,"cumsum");let l=D.getAxesPermutation([a],r.shape.length),u=r;l!=null&&(u=gs({inputs:{x:r},backend:n,attrs:{perm:l}}));let c=D.getInnerMostAxes(1,r.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let d=Ss(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(A,y)=>A+f-y-1:(A,y)=>A+y;for(let A=0;A<h.length;A+=f)for(let y=0;y<f;y++){let x=m(A,y);if(y===0)p[x]=o?0:h[x];else{let b=m(A,y-1);p[x]=o?h[b]+p[b]:h[x]+p[b]}}let g=n.makeTensorInfo(u.shape,d,p);if(l!=null){let A=D.getUndoAxesPermutation(l),y=gs({inputs:{x:g},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),y}return g}var wU={kernelName:za,backendName:"cpu",kernelFunc:vU};function kU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=A2(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=U7(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var IU={kernelName:rp,backendName:"cpu",kernelFunc:kU};function SU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,p=u*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let A=0;A<i;++A)for(let y=0;y<d;++y){let x=Math.floor(y/a),b=y%a;for(let v=0;v<p;++v){let k=Math.floor(v/a),S=v%a,C=(b*a+S)*h;for(let _=0;_<h;++_){let E=_+C+c*(k+u*(x+l*A));m[g++]=f[E]}}}return n.makeTensorInfo([i,d,p,h],r.dtype,m)}var CU={kernelName:Mi,backendName:"cpu",kernelFunc:SU};function $w(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s;Ie([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),d=w.computeStrides(a.shape),p=l;p==null&&(p=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=D.computeConv2DInfo(r.shape,a.shape,o,p,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:y}=h,x=y.left,b=y.top,v=h.outChannels/h.inChannels,k=new Gt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,_=k.values;for(let O=0;O<h.batchSize;++O){let E=O*c[0],R=O*k.strides[0];for(let T=0;T<h.outHeight;++T){let P=R+T*k.strides[1],V=T*h.strideHeight-b;for(let j=0;j<f;++j){let q=V+j*g;if(q<0||q>=h.inHeight)continue;let X=j*d[0],ee=E+q*c[1];for(let te=0;te<h.outWidth;++te){let ne=P+te*k.strides[2],se=te*h.strideWidth-x;for(let Q=0;Q<m;++Q){let ie=se+Q*A;if(ie<0||ie>=h.inWidth)continue;let le=X+Q*d[1],pe=ee+ie*h.inChannels,Ae=ne,Te=le;for(let Ne=0;Ne<h.inChannels;++Ne){let Fe=S[pe+Ne];for(let Le=0;Le<v;++Le)_[Ae+Le]+=Fe*C[Te+Le];Ae+=v,Te+=v}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var TU={kernelName:La,backendName:"cpu",kernelFunc:$w};function NU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s;Ie([r,a],"depthwiseConv2dNativeBackpropFilter");let d=D.computeConv2DInfo(r.shape,c,o,i,l,u,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Gt(d.filterShape,"float32"),A=d.padInfo.left,y=d.padInfo.top,x=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,v=new Gt(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,S=new Gt(a.shape,a.dtype,k);for(let C=0;C<f;++C){let _=Math.max(0,Math.ceil((y-C)/p)),O=Math.min(d.outHeight,(d.inHeight+y-C)/p);for(let E=0;E<m;++E){let R=Math.max(0,Math.ceil((A-E)/h)),T=Math.min(d.outWidth,(d.inWidth+A-E)/h);for(let P=0;P<d.outChannels;++P){let V=Math.trunc(P/x),j=P%x,q=0;for(let X=0;X<d.batchSize;++X)for(let ee=_;ee<O;++ee){let te=C+ee*p-y;for(let ne=R;ne<T;++ne){let se=E+ne*h-A;q+=v.get(X,te,se,V)*S.get(X,ee,ne,P)}}g.set(q,C,E,V,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var EU={kernelName:ap,backendName:"cpu",kernelFunc:NU};function RU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s;Ie([r,a],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(r.shape),p=w.computeStrides(a.shape),h=D.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new Gt(h.inShape,"float32"),m=f.values,[g,A,y]=f.strides,x=n.data.get(r.dataId).values,[b,v,k]=d,S=n.data.get(a.dataId).values,[C,_,O]=p,{batchSize:E,filterHeight:R,filterWidth:T,inChannels:P,inHeight:V,inWidth:j,outChannels:q,outHeight:X,outWidth:ee,strideHeight:te,strideWidth:ne}=h,se=R-1-h.padInfo.top,Q=T-1-h.padInfo.left,ie=q/P;for(let le=0;le<E;++le)for(let pe=0;pe<P;++pe)for(let Ae=0;Ae<V;++Ae){let Te=Ae-se,Ne=Math.max(0,Math.ceil(Te/te)),Fe=Math.min(X,(R+Te)/te);for(let Le=0;Le<j;++Le){let Pe=Le-Q,dt=Math.max(0,Math.ceil(Pe/ne)),rt=Math.min(ee,(T+Pe)/ne),at=0;for(let nt=Ne;nt<Fe;++nt){let ut=nt*te-Te;for(let je=dt;je<rt;++je){let Rn=je*ne-Pe,Tt=b*le+v*nt+k*je,Vn=C*(R-1-ut)+_*(T-1-Rn)+O*pe;for(let on=0;on<ie;++on){let ws=pe*ie+on,An=x[Tt+ws],ns=S[Vn+on];at+=An*ns}}}m[g*le+A*Ae+y*Le+pe]=at}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var _U={kernelName:op,backendName:"cpu",kernelFunc:RU};function DU(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=Ue([r,r],s.dtype),i=o.values;for(let u=0;u<a.length;u++)i[u*r+u]=a[u];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var 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se=w.locToIndex([V,j,X,te],R,w.computeStrides(O));T[se]=ne}}}return{dataId:l.write(w.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},OU={kernelName:up,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:_}=D.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===_.length,()=>`Error in ${up}, dy must have the same rank as output ${_.length}, but got ${a.rank}`);let O=w.toNestedArray(_,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<p;++T)for(let P=0;P<g;++P){let V=P*x-y.top;for(let j=0;j<A;++j){let q=j*b-y.left;for(let X=0;X<m;++X){let 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n.makeTensorInfo(o.shape,r.dtype,g)}var BH={kernelName:gp,backendName:"cpu",kernelFunc:LH};function Pw(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=n,l=r.shape,u=l.length,c=w.parseAxisParam(a,l),d=c,p=D.getAxesPermutation(d,u),h=i.data.get(r.dataId).values;if(p!=null){let b=new Array(u);for(let v=0;v<b.length;v++)b[v]=l[p[v]];h=b2(h,l,r.dtype,p,b),d=D.getInnerMostAxes(d.length,u),l=b}Ie(r,"max"),D.assertAxesAreInnerMostDims("max",d,u);let[f,m]=D.computeOutAndReduceShapes(l,d),g=w.sizeFromShape(m),A=aw(h,g,f,r.dtype),y=i.write(A,f,r.dtype),x=f;return o&&(x=D.expandShapeToKeepDim(f,c)),{dataId:y,shape:x,dtype:r.dtype}}var WH={kernelName:Za,backendName:"cpu",kernelFunc:Pw};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ie(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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c=D.computePool3DInfo(a.shape,o,i,1,l,u),d=n.bufferSync(a),p=OV(d,c),h=c.strideDepth,f=c.strideHeight,m=c.strideWidth,g=c.dilationDepth,A=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterDepth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,k=x-1-c.padInfo.front,S=v-1-c.padInfo.left,C=b-1-c.padInfo.top,_=Ue(a.shape,"float32"),O=n.bufferSync(r);for(let E=0;E<c.batchSize;++E)for(let R=0;R<c.inChannels;++R)for(let T=0;T<c.inDepth;++T)for(let P=0;P<c.inHeight;++P)for(let V=0;V<c.inWidth;++V){let j=T-k,q=P-C,X=V-S,ee=0;for(let te=0;te<x;te+=g){let ne=(j+te)/h;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let se=0;se<b;se+=A){let Q=(q+se)/f;if(!(Q<0||Q>=c.outHeight||Math.floor(Q)!==Q))for(let ie=0;ie<v;ie+=y){let le=(X+ie)/m;if(le<0||le>=c.outWidth||Math.floor(le)!==le)continue;let pe=x*b*v-1-p.get(E,ne,Q,le,R),Ae=te*b*v+se*v+ie,Te=pe===Ae?1:0;if(Te===0)continue;ee+=O.get(E,ne,Q,le,R)*Te}}}_.set(ee,E,T,P,V,R)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var qH={kernelName:yp,backendName:"cpu",kernelFunc:jH};function XH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Ie([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=D.computePool2DInfo(i.shape,l,u,1,c,d),h=n.data.get(i.dataId).values,f=Ue(p.outShape,i.dtype,_w(h,i.shape,i.dtype,p).values),m=p.strideHeight,g=p.strideWidth,A=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterHeight,b=p.effectiveFilterWidth,v=b-1-p.padInfo.left,k=x-1-p.padInfo.top,S=Ue(i.shape,"float32"),C=n.data.get(r.dataId).values,_=Ue(r.shape,"float32",C);for(let O=0;O<p.batchSize;++O)for(let E=0;E<p.inChannels;++E)for(let R=0;R<p.inHeight;++R)for(let T=0;T<p.inWidth;++T){let P=R-k,V=T-v,j=0;for(let q=0;q<x;q+=A){let X=(P+q)/m;if(!(X<0||X>=p.outHeight||Math.floor(X)!==X))for(let ee=0;ee<b;ee+=y){let te=(V+ee)/g;if(te<0||te>=p.outWidth||Math.floor(te)!==te)continue;let 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l=i?r:Mw({inputs:{logits:r},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],d=n.data.get(l.dataId).values,p=[u,a],h=w.makeZerosTypedArray(w.sizeFromShape(p),"int32");for(let f=0;f<u;++f){let m=f*c,g=new Float32Array(c-1);g[0]=d[m];for(let x=1;x<g.length;++x)g[x]=g[x-1]+d[m+x];let A=iG.alea(o.toString()),y=f*a;for(let x=0;x<a;++x){let b=A();h[y+x]=g.length;for(let v=0;v<g.length;v++)if(b<g[v]){h[y+x]=v;break}}}return i||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(p,"int32",h)}var cG={kernelName:bp,backendName:"cpu",kernelFunc:uG},dG=rr.nonMaxSuppressionV3Impl;function pG(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s;Ie(r,"NonMaxSuppression");let u=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,{selectedIndices:d}=dG(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var hG={kernelName:nl,backendName:"cpu",kernelFunc:pG},fG=rr.nonMaxSuppressionV4Impl;function 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t.makeTensorInfo([i.length],a,i)}var RG={kernelName:Zu,backendName:"cpu",kernelFunc:EG},_G=lt(ll,e=>1/e),DG={kernelName:ll,backendName:"cpu",kernelFunc:_G};function FG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ie(r,"resizeBilinear");let l=w.computeStrides(r.shape),[u,c]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(w.sizeFromShape([d,u,c,f])),A=[a&&u>1?p-1:p,a&&c>1?h-1:h],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=0,b=A[0]/y[0],v=A[1]/y[1];for(let k=0;k<d;k++)for(let S=0;S<u;S++){let C;o?C=b*(S+.5)-.5:C=b*S;let _=Math.max(0,Math.floor(C)),O=C-_,E=Math.min(p-1,Math.ceil(C)),R=k*l[0]+_*l[1],T=k*l[0]+E*l[1];for(let P=0;P<c;P++){let V;o?V=v*(P+.5)-.5:V=v*P;let j=Math.max(0,Math.floor(V)),q=V-j,X=Math.min(h-1,Math.ceil(V)),ee=R+j*l[2],te=T+j*l[2],ne=R+X*l[2],se=T+X*l[2];for(let Q=0;Q<f;Q++){let ie=m[ee+Q],le=m[te+Q],pe=m[ne+Q],Ae=m[se+Q],Te=ie+(pe-ie)*q,Ne=le+(Ae-le)*q,Fe=Te+(Ne-Te)*O;g[x++]=Fe}}}return 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MG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ie(r,"resizeNearestNeighbor");let l=w.computeStrides(r.shape),[u,c]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(d*u*c*f),A=[a&&u>1?p-1:p,a&&c>1?h-1:h],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=A[0]/y[0],b=A[1]/y[1],v=0;for(let k=0;k<d;k++){let S=k*l[0];for(let C=0;C<u;C++){let _=o?x*(C+.5):x*C,O=Math.min(p-1,a?Math.round(_):Math.floor(_));o&&(O=Math.max(0,O));let E=S+O*l[1];for(let R=0;R<c;R++){let T=o?b*(R+.5):b*R,P=Math.min(h-1,a?Math.round(T):Math.floor(T));o&&(P=Math.max(0,P));let V=E+P*l[2];for(let j=0;j<f;j++){let q=m[V+j];g[v++]=q}}}}return n.makeTensorInfo([d,u,c,f],r.dtype,g)}var zG={kernelName:Yu,backendName:"cpu",kernelFunc:MG};function LG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ie([a,r],"resizeNearestNeighborGrad");let i=w.computeStrides(r.shape),l=w.computeStrides(a.shape),[u,c,d,p]=r.shape,[,h,f]=a.shape,m=new Float32Array(u*c*d*p),g=n.data.get(a.dataId).values,A=[o&&h>1?c-1:c,o&&f>1?d-1:d],y=[o&&h>1?h-1:h,o&&f>1?f-1:f],x=A[0]/y[0],b=A[1]/y[1],v=1/x,k=1/b,S=Math.ceil(v)*2+2,C=Math.ceil(k)*2+2;for(let _=0;_<u;_++){let O=_*i[0];for(let E=0;E<c;E++){let R=O+E*i[1],T=Math.floor(E*v),P=Math.floor(T-S/2);for(let V=0;V<d;V++){let j=R+V*i[2],q=Math.floor(V*k),X=Math.floor(q-C/2);for(let ee=0;ee<p;ee++){let te=0;for(let ne=0;ne<S;ne++){let se=ne+P;if(se<0||se>=h)continue;let Q=O+se*l[1],ie=se*x,le=Math.min(c-1,o?Math.round(ie):Math.floor(ie));if(E===le)for(let pe=0;pe<C;pe++){let Ae=pe+X;if(Ae<0||Ae>=f)continue;let Te=Q+Ae*l[2],Ne=Ae*b,Fe=Math.min(d-1,o?Math.round(Ne):Math.floor(Ne));V===Fe&&(te+=g[Te+ee])}}m[j+ee]=te}}}}return n.makeTensorInfo(r.shape,r.dtype,m)}var BG={kernelName:wp,backendName:"cpu",kernelFunc:LG};function WG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s;Ie(r,"reverse");let o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return cr({inputs:{x:r},backend:n});let l=new Gt(r.shape,r.dtype),u=n.bufferSync(r);for(let c=0;c<l.size;c++){let d=l.indexToLoc(c),p=d.slice();i.forEach(h=>p[h]=r.shape[h]-1-p[h]),l.set(u.get(...p),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var VG={kernelName:po,backendName:"cpu",kernelFunc:WG},UG={kernelName:Il,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[u,c,d,p]=s.shape,[h,f]=D.getImageCenter(o,c,d),m=255,g=Math.sin(r),A=Math.cos(r),y=i.data.get(s.dataId).values;for(let b=0;b<u;b++){let v=b*d*c*p;for(let k=0;k<c;k++){let S=k*(d*p);for(let C=0;C<d;C++){let _=C*p;for(let O=0;O<p;O++){let E=[u,k,C,O],R=E[2],T=E[1],P=(R-h)*A-(T-f)*g,V=(R-h)*g+(T-f)*A;P=Math.round(P+h),V=Math.round(V+f);let j=a;if(typeof a!="number"&&(O===3?j=m:j=a[O]),P>=0&&P<d&&V>=0&&V<c){let X=V*(d*p),ee=P*p,te=v+X+ee+O;j=y[te]}let q=v+S+_+O;l[q]=j}}}}return{dataId:i.write(l,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},HG=lt(ho,e=>{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),GG={kernelName:ho,backendName:"cpu",kernelFunc:HG};function Ww(e,t,n,s,r,a,o,i,l,u){let c=[s/r,r],d=e.values,p=t.values;if(s===0)return Ue(n,t.dtype);let h=Ue(c,t.dtype);h.values.fill(l);for(let f=0;f<a;f++){let m=[],g=0;for(let A=0;A<o;A++){let y=d[f*o+A];m.push(y),g+=y*i[A]}if(g<0||g>=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let A=0;A<r;A++)u?h.values[g*r+A]+=p[f*r+A]:h.values[g*r+A]=t.rank===0?p[0]:p[f*r+A]}return h}function jG(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=D.calculateShapes(a,r,o),p=!0,h=n.bufferSync(r),f=n.bufferSync(a),m=Ww(h,f,o,d,u,l,i,c,0,p);return n.makeTensorInfo(o,m.dtype,m.values)}var qG={kernelName:cl,backendName:"cpu",kernelFunc:jG};function XG(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Ie([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=Ss(r.dtype,a.dtype),d=w.makeZerosTypedArray(w.sizeFromShape(r.shape),c),p=0,h=o===0||o>1||r.shape.length===1?1:w.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=l[f]:d[p++]=u[f];return n.makeTensorInfo(r.shape,c,d)}var KG={kernelName:dl,backendName:"cpu",kernelFunc:XG},ZG=D.SELU_SCALEALPHA,YG=D.SELU_SCALE,JG=lt(pl,e=>e>=0?YG*e:ZG*(Math.exp(e)-1)),QG={kernelName:pl,backendName:"cpu",kernelFunc:JG},ej=lt(ml,e=>e<0?-1:e>0?1:0),tj={kernelName:ml,backendName:"cpu",kernelFunc:ej},nj=lt(mo,e=>Math.sin(e)),sj={kernelName:mo,backendName:"cpu",kernelFunc:nj},rj=lt(fl,e=>Math.sinh(e)),aj={kernelName:fl,backendName:"cpu",kernelFunc:rj},oj=11920928955078125e-23,Vw=Math.log(oj)+2,ij=lt(gl,e=>{let t=e>-Vw,n=e<Vw,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),lj={kernelName:gl,backendName:"cpu",kernelFunc:ij};function uj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Ie([r],"spaceToBatchND");let i=w.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=Bw.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=D.getReshaped(u.shape,a,i,!1),d=D.getPermuted(c.length,a.length,!1),p=D.getReshapedPermuted(u.shape,a,i,!1),m=vt({inputs:{x:u},backend:n,attrs:{shape:c}}),y=gs({inputs:{x:m},backend:n,attrs:{perm:d}}),v=vt({inputs:{x:y},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),v}var cj={kernelName:Al,backendName:"cpu",kernelFunc:uj};function dj(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
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|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values[0],[d,p,h,f,m]=hw(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var pj={kernelName:Ip,backendName:"cpu",kernelFunc:dj};function hj(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[u,c,d]=fw(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var fj={kernelName:Sp,backendName:"cpu",kernelFunc:hj};function mj(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=w2(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var gj={kernelName:Cp,backendName:"cpu",kernelFunc:mj};function Aj(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=w2(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var yj={kernelName:Tp,backendName:"cpu",kernelFunc:Aj};function xj(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:p}=D.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],A=Ww(f,m,i,p,c,u,l,d,g,h);return n.makeTensorInfo(i,A.dtype,A.values)}var bj={kernelName:Np,backendName:"cpu",kernelFunc:xj};function vj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=D.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let p=[...c];p[i]=d;let h=Yo({inputs:{x:r},backend:n,attrs:{begin:u,size:p}});return u[i]+=d,h})}var wj={kernelName:yl,backendName:"cpu",kernelFunc:vj},kj={kernelName:Ju,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;Ie(n,"square");let r=s.data.get(n.dataId).values,a=new Float32Array(r.length);for(let i=0;i<r.length;++i){let l=r[i];a[i]=l*l}return{dataId:s.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},Ij=lt(Hr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),Sj={kernelName:Hr,backendName:"cpu",kernelFunc:Ij};function Cj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s;Ie(r,"stridedSlice");let{nonStrided:h,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=vn.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=vt({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=Yo({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=vt({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else{let k=n.bufferSync(x),S=gw(y,k,m,f);b=n.makeTensorInfo(S.shape,S.dtype,S.values)}let v=vt({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var Tj={kernelName:xl,backendName:"cpu",kernelFunc:Cj};function Nj(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,p=n.data.get(c.dataId).values,h=n.data.get(d.dataId).values,[f,m]=Aw(p,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Ej={kernelName:Ep,backendName:"cpu",kernelFunc:Nj};function Rj(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values[0],[u,c,d]=yw(i,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var _j={kernelName:Rp,backendName:"cpu",kernelFunc:Rj};function Dj(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.data.get(a.dataId).values,i=xw(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Fj={kernelName:_p,backendName:"cpu",kernelFunc:Dj},$j=lt(wo,e=>Math.tan(e)),Oj={kernelName:wo,backendName:"cpu",kernelFunc:$j},Pj=lt(ko,e=>Math.tanh(e)),Mj={kernelName:ko,backendName:"cpu",kernelFunc:Pj};function zj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;Ie(r,"tile");let o=vw(n.bufferSync(r),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var 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s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function Aq(e,t){let n=_2(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function f6(e){return e!==2?!1:dr(e).fenceSync!=null}function ru(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Re=Y();Re.registerFlag("HAS_WEBGL",()=>Re.getNumber("WEBGL_VERSION")>0);Re.registerFlag("WEBGL_VERSION",()=>$2(2)?2:$2(1)?1:0);Re.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Re.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Re.get("WEBGL_VERSION")===2);Re.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Re.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Re.registerFlag("WEBGL_PACK",()=>Re.getBool("HAS_WEBGL"));Re.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_CLIP",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_PACK_REDUCE",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_LAZILY_UNPACK",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_CONV_IM2COL",()=>Re.getBool("WEBGL_PACK"));Re.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>u6(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>c6(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Re.getNumber("WEBGL_VERSION");return e===0?0:d6(e)});Re.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Re.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!uc.isMobile());Re.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>p6(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Re.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Re.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Re.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>h6(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_FENCE_API_ENABLED",()=>f6(Re.getNumber("WEBGL_VERSION")));Re.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Re.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Re.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Re.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>uc.isMobile()&&Re.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Re.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Re.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Re.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Re.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Sn(){let e,t,n,s,r,a,o,i,l,u;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
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)
|
|
`,l="",u=`
|
|
#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)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
|
|
#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));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,u=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function ti(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function Sf(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function yq(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function xq(e,t,n="index"){let s=e.map((a,o)=>o),r=yq(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function P2(e){let t=w.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function M2(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var m6=`
|
|
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;
|
|
}
|
|
`,{getBroadcastDims:g6}=D;function bq(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=z2(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>vq(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Sn(),l=Iq(i),u,c,d=Tq(i);return t.isPacked?(u=wq(t.logicalShape,o,n.enableShapeUniforms),c=Cq(i)):(u=kq(t.logicalShape,o,n.enableShapeUniforms),c=Sq(i)),n.packedInputs&&(d+=_q),[d,l,c,r,u,a,n.userCode].join(`
|
|
`)}function au(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return Uq(e,t);case 1:return Gq(e,t);case 2:return qq(e,t);case 3:return Kq(e,t);case 4:return Yq(e,t);case 5:return Jq(e);case 6:return Qq(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function A6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Vq(e);case 1:return Hq(e,t);case 2:return jq(e,t);case 3:return Xq(e,t);default:return Zq(e,t)}}function vq(e,t,n=!1,s){let r="";n?r+=A6(e,s):r+=au(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=eX(e,t):r+=tX(e,t)),r}function wq(e,t,n){switch(e.length){case 0:return y6();case 1:return Dq(e,t,n);case 2:return Bq(e,t,n);case 3:return $q(e,t,n);default:return Pq(e,t,n)}}function kq(e,t,n){switch(e.length){case 0:return y6();case 1:return Fq(e,t,n);case 2:return Wq(e,t,n);case 3:return Oq(e,t,n);case 4:return Mq(e,t,n);case 5:return zq(e,t);case 6:return Lq(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Iq(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function Sq(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function Cq(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function Tq(e){return`${e.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${e.varyingFs} vec2 resultUV;
|
|
${e.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;
|
|
${e.defineSpecialNaN}
|
|
${e.defineSpecialInf}
|
|
${e.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);
|
|
}
|
|
|
|
${Nq}
|
|
${Eq}
|
|
${Rq}
|
|
`}var Nq=`
|
|
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);
|
|
}
|
|
`,Eq=`
|
|
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);
|
|
}
|
|
`,Rq=`
|
|
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);
|
|
}
|
|
`,_q=`
|
|
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 y6(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function Dq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${s[1]}.0);
|
|
}
|
|
`:s[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${s[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
|
|
}
|
|
`}function Fq(e,t,n){return t[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function $q(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function Oq(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Sf(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=ti(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function Pq(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
|
|
int b${u} = index / ${o};
|
|
index -= b${u} * ${o};
|
|
`+i,l=`b${u}, `+l;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec${e.length}(${l});
|
|
}
|
|
`}function Mq(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Sf(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=ti(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function zq(e,t){let n=ti(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function Lq(e,t){let n=ti(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function Bq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Wq(e,t,n){return w.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function ni(e){return`offset${e}`}function Vq(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Sn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function Uq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
|
|
float ${s}() {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let o=ni(n);if(t)return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let[i,l]=e.shapeInfo.texShape;return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Hq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Sn();if(t)return`
|
|
vec4 ${s}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${s}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${o[0]}, ${o[1]}, index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`}function Gq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${ou(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
|
|
float ${s}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=ni(n);return o===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:a===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function jq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Sn();if(a!=null&&w.arraysEqual(n,a))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`}function qq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let p=a[0],h=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}let{newShape:o,keptDims:i}=w.squeezeShape(n),l=o;if(l.length<n.length){let p=iu(e,l),h=["row","col"];return`
|
|
${au(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${lu(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${ou(e)}
|
|
}
|
|
`;let u=a[0],c=a[1],d=ni(s);return c===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s}Shape[1] + col + ${d};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${u}, ${c}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function Xq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=iu(e,p),m=["b","row","col"];return`
|
|
${A6(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${lu(m,h)});
|
|
}
|
|
`}let i=Sn();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`;let l=o[0],u=o[1],c=Math.ceil(n[2]/2),d=c*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${u}, ${d}, ${c}, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`}function Kq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=w.squeezeShape(n),u=i;if(u.length<n.length){let m=iu(e,u),g=["row","col","depth"];return`
|
|
${au(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${lu(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${a}, ${o}, 1)));
|
|
${ou(e)}
|
|
}
|
|
`;let c=e.shapeInfo.texShape,d=c[0],p=c[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${s}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(p===o&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let f=ni(s);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${s}Shape[1] * ${s}Shape[2];
|
|
int stride1 = ${s}Shape[2];
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function Zq(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Sn();if(t)return`
|
|
vec4 ${s}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${n}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}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 ${r.texture2D}(${n}, uv);
|
|
}
|
|
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
|
|
vec4 ${s}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${c};
|
|
int texC = index - texR * ${c};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function Yq(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=w.squeezeShape(n);if(l.length<n.length){let y=iu(e,l),x=["row","col","depth","depth2"];return`
|
|
${au(y,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${lu(x,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, 1)));
|
|
${ou(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${o}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(h===a&&c==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n[1]*n[2]}, ${n[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let A=ni(s);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} +
|
|
depth * ${a} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function Jq(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=iu(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${au(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${lu(g,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, ${r})) +
|
|
depth3;
|
|
${ou(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&c==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${o}, ${a}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&c==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=ni(n);return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} + depth * ${a} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Qq(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=iu(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${au(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${lu(A,a)});
|
|
}
|
|
`}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${o}, 1)));
|
|
${ou(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===c&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${i}, ${o})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===o&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=ni(n);return`
|
|
float ${s}(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 * ${u} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function ou(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function eX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=g6(e.shapeInfo.logicalShape,t.logicalShape),l=ft(o),u=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+u]} = 0;`).join(`
|
|
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,x)=>`coords.${d[x+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,A=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!A)o===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let y=a-2,x=a-1;i.indexOf(y)>-1&&i.indexOf(x)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${s}(${p});
|
|
${h}
|
|
}
|
|
`}function tX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let u=ft(l),c=g6(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&c.length>=1?p="coords = 0;":p=c.map(m=>`coords.${h[m+d]} = 0;`).join(`
|
|
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
|
|
float ${r}() {
|
|
${u} coords = getOutputCoords();
|
|
${p}
|
|
return get${s}(${f});
|
|
}
|
|
`}function ft(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function z2(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function iu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function lu(e,t){return t.map(n=>e[n]).join(", ")}function nX(e,t,n,s){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=bq(r,o,t),l=e.createProgram(i),u=null,c=e.getUniformLocation(l,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let d=!1,p={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];p[b]=e.getUniformLocation(l,b,d),p[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,g,A;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),A=e.getUniformLocation(l,"outShapeStrides",d),g=e.getUniformLocation(l,"outTexShape",d));let y=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:p,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:c,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:A,outTexShapeLocation:g}}function x6(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function sX(e,t,n,s,r){t.program.enableShapeUniforms||(x6(t.inShapeInfos,n),x6([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],d=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=z2(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(c,d);else if(l.type==="vec2")e.gl.uniform2fv(c,d);else if(l.type==="vec3")e.gl.uniform3fv(c,d);else if(l.type==="vec4")e.gl.uniform4fv(c,d);else if(l.type==="int")e.gl.uniform1iv(c,d);else if(l.type==="ivec2")e.gl.uniform2iv(c,d);else if(l.type==="ivec3")e.gl.uniform3iv(c,d);else if(l.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function rX(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:d}=z2(e.packedInputs,o.shape,l),p="",h="",f="";if(c.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let v=w.computeStrides(c);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,l),A=w.sizeFromShape(o.shape)===1,y=D.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${u?d:""}_${c.length}_${A}_${y}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Y().getNumber("WEBGL_VERSION")}`,a}function xs(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var aX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Zc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Sn();this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Sf(["r","c","d"],e):ti(["r","c","d"],e)}
|
|
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);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},oX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Zc.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Sn();this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Sf(["r","c","d"],e):ti(["r","c","d"],e)}
|
|
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));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},iX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=As.DOWNLOAD;let t=Sn();this.outputShape=e,this.userCode=`
|
|
${m6}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},lX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=As.DOWNLOAD;let t=Sn();this.outputShape=e,this.userCode=`
|
|
${m6}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},uX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Sn();this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?M2():P2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int 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]);
|
|
vec4 values = ${n.texture2D}(A, uv);
|
|
|
|
float result;
|
|
|
|
if(offset == 0) {
|
|
result = values[0];
|
|
} else if(offset == 1) {
|
|
result = values[1];
|
|
} else if(offset == 2) {
|
|
result = values[2];
|
|
} else {
|
|
result = values[3];
|
|
}
|
|
|
|
${n.output} = vec4(${s}, 0., 0., 0.);
|
|
}
|
|
`}},cX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Sn();this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
|
|
localCoords[2] += ${o};
|
|
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
|
|
localCoords[1] += ${a};
|
|
|
|
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 = ${n.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${i}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${i}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${i}] = values[2];
|
|
} else {
|
|
result[${i}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?M2():P2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},b6={};ze(b6,{bindVertexProgramAttributeStreams:()=>E6,createBufferFromOutputTexture:()=>D6,createFloat16MatrixTexture:()=>S6,createFloat16PackedMatrixTexture:()=>N6,createFloat32MatrixTexture:()=>I6,createIndexBuffer:()=>k6,createPackedMatrixTexture:()=>T6,createUnsignedBytesMatrixTexture:()=>C6,createVertexBuffer:()=>w6,createVertexShader:()=>v6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>$6,downloadFloat32MatrixFromBuffer:()=>F6,downloadMatrixFromPackedOutputTexture:()=>P6,downloadPackedMatrixFromBuffer:()=>O6,getInternalFormatForFloat16MatrixTexture:()=>B2,getInternalFormatForFloat16PackedMatrixTexture:()=>U2,getInternalFormatForFloat32MatrixTexture:()=>L2,getInternalFormatForPackedMatrixTexture:()=>V2,getInternalFormatForUnsignedBytesMatrixTexture:()=>W2,uploadDenseMatrixToTexture:()=>R6,uploadPixelDataToTexture:()=>_6});function v6(e){let t=Sn(),n=`${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 qw(e,n)}function w6(e){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 Yw(e,t)}function k6(e){let t=new Uint16Array([0,1,2,2,1,3]);return Jw(e,t)}function td(e,t,n,s,r,a){e6(t,n);let o=Qw(e),i=e.TEXTURE_2D;return be(e,()=>e.bindTexture(i,o)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),be(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),be(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),be(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function L2(e){return e.internalFormatFloat}function I6(e,t,n,s){let[r,a]=Yc(t,n);return td(e,r,a,L2(s),s.textureFormatFloat,e.FLOAT)}function B2(e){return e.internalFormatHalfFloat}function S6(e,t,n,s){let[r,a]=Yc(t,n);return td(e,r,a,B2(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function W2(e){return e.downloadTextureFormat}function C6(e,t,n,s){let[r,a]=Yc(t,n);return td(e,r,a,W2(s),e.RGBA,e.UNSIGNED_BYTE)}function V2(e){return e.internalFormatPackedFloat}function T6(e,t,n,s){let[r,a]=su(t,n);return td(e,r,a,V2(s),e.RGBA,e.FLOAT)}function U2(e){return e.internalFormatPackedHalfFloat}function N6(e,t,n,s){let[r,a]=su(t,n);return td(e,r,a,U2(s),e.RGBA,s.textureTypeHalfFloat)}function E6(e,t,n){let s=0,r=3*4,a=3*4+2*4;return be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),D2(e,t,"clipSpacePos",n,3,a,s)&&D2(e,t,"uv",n,2,a,r)}function R6(e,t,n,s,r,a){be(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),be(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function _6(e,t,n){be(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):be(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),be(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function D6(e,t,n,s){let r=e.createBuffer();be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return be(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),be(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function F6(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function $6(e,t,n,s){let[r,a]=Yc(t,n),o=4,i=new Uint8Array(aq(t*n,o));return be(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function O6(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(oq(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function P6(e,t,n){let s=new Float32Array(t*n*4);return be(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Cf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Af(t,e)):this.gl=dr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Jc(this.gl,r),ys(this.gl,a))this.textureHalfFloatExtension=Jc(this.gl,a);else if(Y().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),ys(this.gl,s))this.colorBufferHalfFloatExtension=Jc(this.gl,s);else if(Y().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",ys(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(ys(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=w6(this.gl),this.indexBuffer=k6(this.gl),this.framebuffer=t6(this.gl),this.textureConfig=_2(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().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 e=this.gl;be(e,()=>e.finish()),be(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),be(e,()=>e.deleteFramebuffer(this.framebuffer)),be(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),be(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),be(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),I6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),S6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),C6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),_6(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),R6(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),N6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),T6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(F2(this.gl,this.framebuffer),this.outputTexture=null),be(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>$6(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return O6(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return F6(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=D6(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>P6(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Xw(t,e);this.vertexShader==null&&(this.vertexShader=v6(t));let s=Kw(t);return be(t,()=>t.attachShader(s,this.vertexShader)),be(t,()=>t.attachShader(s,n)),Zw(t,s),this.debug&&xf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=E6(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&be(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&xf(this.gl,this.program),be(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?s6(this.gl,e,t):r6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),be(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),a6(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=su(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&xf(this.gl,this.program),Qc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),be(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),be(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Jc(this.gl,Y().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(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=dX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),bf(this.gl,e,this.framebuffer),this.debug&&Qc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bf(this.gl,this.outputTexture,this.framebuffer),this.debug&&Qc(this.gl)):F2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;bf(s,e,this.framebuffer),this.debug&&Qc(s),this.outputTexture=e,be(s,()=>s.viewport(0,0,t,n)),be(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),be(this.gl,()=>this.gl.scissor(e,t,n,s))}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 dX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:pX,bincountImpl:M6,bincountReduceImpl:hX,ceilImpl:fX,concatImpl:mX,equalImpl:gX,expImpl:AX,expm1Impl:yX,floorImpl:xX,gatherNdImpl:bX,gatherV2Impl:vX,greaterImpl:wX,greaterEqualImpl:kX,lessImpl:IX,lessEqualImpl:SX,linSpaceImpl:CX,logImpl:TX,maxImpl:NX,maximumImpl:EX,minimumImpl:RX,multiplyImpl:_X,negImpl:DX,notEqualImpl:FX,prodImpl:$X,rangeImpl:OX,rsqrtImpl:PX,sigmoidImpl:MX,simpleAbsImpl:z6,sliceImpl:zX,sparseFillEmptyRowsImpl:LX,sparseReshapeImpl:BX,sparseSegmentReductionImpl:L6,sqrtImpl:WX,stridedSliceImpl:VX,stringNGramsImpl:UX,stringSplitImpl:HX,stringToHashBucketFastImpl:GX,subImpl:jX,tileImpl:qX,topKImpl:XX,transposeImpl:H2,uniqueImpl:KX}=m2;function B6(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Cn(e,t){return t===1?[e]:B6(e,t)}function ZX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var YX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let n=Cn("rc",t),s=ft(t),r=QX(t,e,n),a=eK(t,e[e.length-1],e[e.length-2],n),o=tK(e,n);this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${o}));
|
|
}
|
|
}
|
|
`}}};function JX(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function QX(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function eK(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
|
|
int r = ${r[0]};
|
|
int c = ${r[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function tK(e,t){let n=e.length,s=JX(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${s[0]}),
|
|
cEdge ? 0. : getA(${s[1]}),
|
|
rEdge ? 0. : getA(${s[2]}),
|
|
rEdge || cEdge ? 0. : getA(${s[3]})`}var W6=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${s}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${s>0?"}":""}
|
|
`}this.userCode=`
|
|
${nK(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?M2():P2(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function nK(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?xq(["r","c","d"],"inputShape"):ti(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var sK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=U6(t,n),r=H6(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=V6(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===cn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===cn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===cn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===cn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===cn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=U6(n,s),a=H6(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=V6(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}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 e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function rK(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function V6(e,t,n,s,r){let a=aK(t,s),o;if(r){let[l,u]=su(e[0],e[1]);o=l*u}else{let[l,u]=Yc(e[0],e[1]);o=l*u}let i=rK(n,a);return o*i}function aK(e,t){switch(e){case cn.PACKED_2X2_FLOAT32:return V2(t);case cn.PACKED_2X2_FLOAT16:return U2(t);case cn.UNPACKED_FLOAT32:return L2(t);case cn.UNPACKED_FLOAT16:return B2(t);case cn.PACKED_4X1_UNSIGNED_BYTE:return W2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function oK(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?cn.PACKED_2X2_FLOAT32:cn.UNPACKED_FLOAT32:e?cn.PACKED_2X2_FLOAT16:cn.UNPACKED_FLOAT16}function U6(e,t){if(e===As.UPLOAD)return cn.PACKED_2X2_FLOAT32;if(e===As.RENDER||e==null)return oK(t);if(e===As.DOWNLOAD||e===As.PIXELS)return cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function H6(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var pa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},Gs="if (isnan(x)) return x;",iK="return x;",G6="return abs(x);",lK="return (x >= 0.0) ? x : (exp(x) - 1.0);",uK=Gs+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,cK=Gs+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Tf="return x;",dK="return 1.0 / (1.0 + exp(-1.0 * x));",pK="return x;",hK=`
|
|
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;
|
|
`,fK=`
|
|
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;
|
|
`,mK=`
|
|
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;
|
|
`,gK="return 1.0 / (1.0 + exp(-1.0 * x));",uu=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=xs(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},AK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Cn("rc",t),s=ft(t),r=ZX(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},yK=rr.whereImpl,xK=1e-7,bK=1e-4,Nf={};function vK(e){return e in Nf||(Nf[e]={}),Nf[e]}var wK=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),kK=600;function IK(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*kK/1024/1024}var cu=class extends Mu{constructor(e){super();if(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,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=dr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=vK(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new Cf(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new sK(this.gpgpu),this.numMBBeforeWarning=IK(),this.texData=new jd(this,Cs())}nextDataId(){return cu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:As.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:As.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new uu(o,Tf):d=new pa(o,Tf);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);c=D.mergeRealAndImagArrays(d,p)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new uu(s,Tf):h=new pa(s,Tf);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().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,u;if(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...yf(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=D.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;be(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Cs().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Gw(n))throw Y().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...yf(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?vf(t):t,i=a?new lX(o):new iX(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=wK){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){D.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return yK(e.shape,t)}packedUnaryOp(e,t,n){let s=new uu(e.shape,t),r=this.compileAndRun(s,[e],n);return Cs().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=z6(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,G6,e.dtype);let t=new pa(e.shape,G6),n=this.compileAndRun(t,[e]);return Cs().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Cs().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new AK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new YX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Qo(e.shape),...ei(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Qo(t),...ei(t)],a=new W6(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=vf(s),o,i=yf(a);n?o=new oX(a):o=new aX(a);let l=!0,u=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:s,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Zc.DENSE){let m=yf(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&w.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!ed(g.shape,m.shape)){let A=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),A.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=rX(e,l,u),d=this.getAndSaveBinary(c,()=>nX(this.gpgpu,e,l,u)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),sX(this.gpgpu,d,l,u,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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=H(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?xK:bK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let c=t.texShape;if(c==null&&(c=l6(n,i),t.texShape=c),r!=null){let d=vf(n),p,h=c[1],f=c[0],m=r instanceof Uint8Array;i?([h,f]=su(c[0],c[1]),p=new cX(d,m)):p=new uX(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=As.PIXELS:this.texData.get(g.dataId).usage=As.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],y=!0,x=this.runWebGLProgram(p,[g],s,A,y),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(c,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=SK(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}};cu.nextDataId=0;function SK(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var CK="3.9.0";function j6(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}uc.isBrowser()&&Dl("webgl",()=>new cu,2);var TK={forceHalfFloat:j6},q6=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,du=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=D.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=xs(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},Ef=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`,nd=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=D.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=xs(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${ft(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?a+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=Cn("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Jn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var NK={kernelName:qa,backendName:"webgl",kernelFunc:Jn};function ha(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Jn({inputs:{x:s},backend:n}),l=Jn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var EK={kernelName:ep,backendName:"webgl",kernelFunc:ha},X6="return (a < 0.) ? b * a : a;",K6=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function RK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new nd(K6,r.shape,o.shape):new du(X6,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var _K={kernelName:Xa,backendName:"webgl",kernelFunc:RK},Z6="return (a < 0.) ? b * a : a;",Y6=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function DK(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new nd(Y6,s.shape,r.shape):new du(Z6,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var FK={kernelName:io,backendName:"webgl",kernelFunc:DK},J6="if (isnan(x)) return x;",$K=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,OK=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`;function et({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new uu(o.shape,t):c=new pa(o.shape,e),i.runWebGLProgram(c,[o],l)}}function dn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},C=new du(e,l.shape,u.shape);return c.runWebGLProgram(C,[k,S],Ss(b.dtype,v.dtype))}),y=ha({inputs:{real:g,imag:A},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(A),y}let d=a||Ss(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?D.fromUint8ToStringArray(f):f,A=l.dtype==="string"?D.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,u.shape,g,A,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=y,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new nd(t,l.shape,u.shape,n):h=new du(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],d)}}function Rf(e,t=!1){if(e==="linear")return t?pK:iK;if(e==="relu")return t?fK:uK;if(e==="elu")return t?hK:lK;if(e==="relu6")return t?mK:cK;if(e==="prelu")return t?Y6:Z6;if(e==="leakyrelu")return t?K6:X6;if(e==="sigmoid")return t?gK:dK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Q6=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=xs(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${o}
|
|
}`,g="result = activation(result);");let A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${c}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${c}; i++) {
|
|
int batchA = ${y};
|
|
int batchB = ${x};
|
|
vec4 a = getMatrixA(batchA, ${d});
|
|
vec4 b = getMatrixB(batchB, ${p});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${f[0]});
|
|
result += (${h[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${A}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},e4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},t4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=D.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},n4="return a * b;";function G2(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=D.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new t4(e4.REAL,s.shape,r.shape),c=new t4(e4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=ha({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=_X(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),p=n.texData.get(d.dataId);return p.values=u,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new nd(n4,s.shape,r.shape):o=new du(n4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var PK={kernelName:so,backendName:"webgl",kernelFunc:G2};function MK(e,t,n){let s=[Qo(e.shape),...ei(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Qo(t),...ei(t)],o=new W6(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(l);w.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!ed(r.shape,l)&&!(c.texture!==null&&ed(c.shape,l))?MK(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var zK={kernelName:ul,backendName:"webgl",kernelFunc:ye},s4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${u}
|
|
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 < ${o}; 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 + ${o};
|
|
if (${i===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},LK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,d=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,p="vec4";t==="all"?(o="1.0",d=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,p="bvec4"):t==="any"&&(o="0.0",d=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,p="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${o});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${c===1}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===2}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===3}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function BK(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=D.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function si(e,t,n,s){let r=BK(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,d;n==="mean"?c=o===0?new s4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new s4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new LK({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),d=a,a=s.runWebGLProgram(c,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var WK=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ft(this.rank),r=VK(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function VK(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var UK=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ft(this.rank),r=B6("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${i}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${i}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function _f(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UK(e.shape,t):new WK(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function HK(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,l=D.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=_f(e,l,s),i=D.getInnerMostAxes(i.length,a)),D.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=D.computeOutAndReduceShapes(c.shape,i),h=d;n&&(h=D.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,A=ye({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=Lp(e.dtype),x=si(A,y,"sum",s),b=ye({inputs:{x},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(x),u&&s.disposeIntermediateTensorInfo(c),b}function Df(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return HK(r,a,o,n)}var GK={kernelName:yo,backendName:"webgl",kernelFunc:Df};function Tn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=H2(d,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(u.dataId);h.values=p}else u=_f(r,a,o);return u}var jK={kernelName:Io,backendName:"webgl",kernelFunc:Tn},r4=1e3;function Ff({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],p=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=w.sizeFromShape(m),y=w.sizeFromShape(g),x=A===y||A===1||y===1;w.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let v=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,h]:[A,h,d],S=s?[y,f,p]:[y,p,f],C=ye({inputs:{x:e},backend:r,attrs:{shape:k}}),_=ye({inputs:{x:t},backend:r,attrs:{shape:S}}),O=[C,_],E=Math.max(A,y),R=n?C.shape[1]:C.shape[2],T=a!=null,P=o!=null,V=l==="leakyrelu",j=l!=null?Rf(l,!0):null,q=T||P||V||j!=null,X;if((h===1||f===1)&&R>r4&&q===!1){let te=C,ne=_;n&&(te=Tn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),O.push(te)),s&&(ne=Tn({inputs:{x:_},backend:r,attrs:{perm:[0,2,1]}}),O.push(ne));let se=f!==1,Q=f===1,ie=te;se&&(ie=ye({inputs:{x:te},backend:r,attrs:{shape:[E,R,1]}}),O.push(ie));let le=f===1?2:1,pe=ne;Q&&(pe=ye({inputs:{x:ne},backend:r,attrs:{shape:[E,1,R]}}),O.push(pe));let Ae=G2({inputs:{a:ie,b:pe},backend:r});X=Df({inputs:{x:Ae},backend:r,attrs:{axis:le,keepDims:!0}}),O.push(Ae)}else{let te=Ss(e.dtype,t.dtype),ne=new Q6(k,S,[E,h,f],n,s,T,j,P,V),se=[C,_];if(a!=null&&se.push(a),P&&se.push(o),V){let Q=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));se.push(Q),O.push(Q)}X=r.runWebGLProgram(ne,se,te)}let ee=ye({inputs:{x:X},backend:r,attrs:{shape:v}});O.push(X);for(let te of O)r.disposeIntermediateTensorInfo(te);return ee}function qK(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s;return Ff({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var XK={kernelName:So,backendName:"webgl",kernelFunc:qK},a4="return abs(x);";function KK(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=z6(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new uu(s.shape,a4):r=new pa(s.shape,a4),n.runWebGLProgram(r,[s],s.dtype)}var ZK={kernelName:Ii,backendName:"webgl",kernelFunc:KK},YK=Gs+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,JK=et({opSnippet:YK}),QK={kernelName:Si,backendName:"webgl",kernelFunc:JK},eZ=Gs+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,tZ=et({opSnippet:eZ}),nZ={kernelName:Ci,backendName:"webgl",kernelFunc:tZ},o4="return a + b;",sZ=dn({opSnippet:o4,packedOpSnippet:o4,supportsComplex:!0,cpuKernelImpl:pX}),rZ={kernelName:Wr,backendName:"webgl",kernelFunc:sZ},aZ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},oZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}};function $f(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Jn({inputs:{x:s[0]},backend:n});if(s.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=$f({inputs:s.slice(0,l),backend:n}),c=$f({inputs:s.slice(l),backend:n});return $f({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Ss(l,u)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new oZ(s[0].shape,a):new aZ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var iZ={kernelName:Na,backendName:"webgl",kernelFunc:$f};function lZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,i)),D.assertAxesAreInnerMostDims("all",u,i);let[p,h]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"all",n),A;if(o){let y=D.expandShapeToKeepDim(p,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var uZ={kernelName:Ti,backendName:"webgl",kernelFunc:lZ};function cZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,i)),D.assertAxesAreInnerMostDims("any",u,i);let[p,h]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"any",n),A;if(o){let y=D.expandShapeToKeepDim(p,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var dZ={kernelName:Ni,backendName:"webgl",kernelFunc:cZ},pZ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=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 * ${s};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${s}; i++) {
|
|
int inIdx = ${i};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},hZ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=ft(i),u=Cn("coords",i),c,d;if(a===1){d=i+1;let S=ft(d);c=`
|
|
${S} sourceLocR = ${S}(${u.join()}, 0);
|
|
++${u[i-1]};
|
|
${S} sourceLocG = ${S}(${u.join()}, 0);
|
|
++${u[i-2]};
|
|
${S} sourceLocA = ${S}(${u.join()}, 0);
|
|
--${u[i-1]};
|
|
${S} sourceLocB = ${S}(${u.join()}, 0);
|
|
--${u[i-2]};`}else d=i,c=`
|
|
${l} sourceLocR = coords;
|
|
++${u[i-1]};
|
|
${l} sourceLocG = coords;
|
|
++${u[i-2]};
|
|
${l} sourceLocA = coords;
|
|
--${u[i-1]};
|
|
${l} sourceLocB = coords;
|
|
--${u[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=Cn("sourceLocR",d-1).concat("inIdx.r"),g=Cn("sourceLocG",d-1).concat("inIdx.g"),A=Cn("sourceLocB",d-1).concat("inIdx.b"),y=Cn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()})));`,v=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,k=s?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}
|
|
${k}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${u[i-1]} < ${o[i-1]-1};
|
|
bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
|
|
${c}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${v};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${v};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${x}(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 i4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=D.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new pZ(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=i4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function l4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=D.computeOptimalWindowSize(a),i=new hZ(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=l4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function u4(e,t,n,s){let r=[n];if(D.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=D.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(c),p=ye({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=i4(e,p,s);a.push(h);let f=ye({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return l4(e,t,s)}function fZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),D.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=u4(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var mZ={kernelName:Ea,backendName:"webgl",kernelFunc:fZ};function gZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=D.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Tn({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=D.getInnerMostAxes(o.length,l.shape.length)),D.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=u4(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var AZ={kernelName:Bu,backendName:"webgl",kernelFunc:gZ},yZ=Gs+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,xZ=et({opSnippet:yZ}),bZ={kernelName:Ei,backendName:"webgl",kernelFunc:xZ},vZ=Gs+"return log(x + sqrt(x * x + 1.0));",wZ=et({opSnippet:vZ}),kZ={kernelName:Ri,backendName:"webgl",kernelFunc:wZ},IZ=Gs+`
|
|
return atan(x);
|
|
`,SZ=et({opSnippet:IZ}),CZ={kernelName:_i,backendName:"webgl",kernelFunc:SZ},TZ=$K+`
|
|
return atan(a, b);
|
|
`,NZ=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+OK+`
|
|
return result;
|
|
`,EZ=dn({opSnippet:TZ,packedOpSnippet:NZ}),RZ={kernelName:Fi,backendName:"webgl",kernelFunc:EZ},_Z=Gs+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,DZ=et({opSnippet:_Z}),FZ={kernelName:Di,backendName:"webgl",kernelFunc:DZ},sd=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
|
|
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 < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
wC += ${u}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.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 ${S} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,v=a%4,k=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${y}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
const float initializationValue = ${A};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${A});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
getValue(batch, xR, xC + 3 * ${u}, d)
|
|
);
|
|
|
|
${k}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${v===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},j2=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let _=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${A});
|
|
|
|
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 < ${p};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${c}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${d}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.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 ${_} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,C=`
|
|
if (${y}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${A});
|
|
const float initializationValue = ${x};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${x});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${c}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${k}; wC += 4) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${S===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${S===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${S===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${v});
|
|
}
|
|
}
|
|
`}};function $Z(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ru(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Jn({inputs:{x:r},backend:n});let d=new sd(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var OZ={kernelName:Ra,backendName:"webgl",kernelFunc:$Z};function PZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=D.computePool3DInfo(r.shape,a,o,c,i,l,u),p=new j2(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var MZ={kernelName:Wu,backendName:"webgl",kernelFunc:PZ},zZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${u}, ${c});
|
|
const float avgMultiplier = float(${d});
|
|
|
|
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 < ${i};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC+= ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},LZ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${f}, ${m});
|
|
const float avgMultiplier = float(${g});
|
|
|
|
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 < ${c};
|
|
wD += ${i}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${u}) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.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 BZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=D.computePool3DInfo(o.shape,i,l,d,u,c),h=new LZ(p);return n.runWebGLProgram(h,[r],o.dtype)}var WZ={kernelName:Jd,backendName:"webgl",kernelFunc:BZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ru([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=D.computePool2DInfo(o.shape,i,l,1,u),d=new zZ(c);return n.runWebGLProgram(d,[r],o.dtype)}var UZ={kernelName:Yd,backendName:"webgl",kernelFunc:VZ};function HZ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Ff({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var GZ={kernelName:_a,backendName:"webgl",kernelFunc:HZ},jZ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],D.assertAndGetBroadcastShape(e,t),D.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(D.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(D.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${o};
|
|
float scale = ${i};
|
|
float inv = scale * inversesqrt(variance + float(${a}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},qZ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],D.assertAndGetBroadcastShape(e,t),D.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(D.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(D.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${o};
|
|
vec4 scale = ${i};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},XZ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new qZ(s.shape,r.shape,a.shape,c,d,l):new jZ(s.shape,r.shape,a.shape,c,d,l);return t.runWebGLProgram(p,u,u[0].dtype)},KZ={kernelName:Ga,backendName:"webgl",kernelFunc:XZ},ZZ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ft(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=YZ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${q2[o]} = start[${o}] + coords.${q2[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},q2=["x","y","z","w","u","v"];function YZ(e){if(e===1)return"sourceLoc";if(e<=6)return q2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var JZ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ft(this.rank),n=Cn("coords",this.rank),s=Cn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
|
|
result.x = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.y = ${a};
|
|
--${s[this.rank-1]};
|
|
}
|
|
`,i=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${s[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${o}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function QZ(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=vn.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function pu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=vn.parseSliceParams(r,a,o);if(vn.assertParamsValid(r,i,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=zX(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:u}=n.texData.get(r.dataId),c=vn.isSliceContinous(r.shape,i,l);if(u||!c){let d=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new JZ(l):new ZZ(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),QZ(r,i,l,n)}var eY={kernelName:hl,backendName:"webgl",kernelFunc:pu},tY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),p=D.getSliceSize(c,o,a.length),h=[],f=ye({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Tn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ye({inputs:{x:m},backend:n,attrs:{shape:c}}),A=pu({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},nY={kernelName:$i,backendName:"webgl",kernelFunc:tY};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),u=M6(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var rY={kernelName:Qd,backendName:"webgl",kernelFunc:sY},aY="return float(a != b);",c4=dn({opSnippet:aY,cpuKernelImpl:FX,dtype:"bool"}),oY={kernelName:tl,backendName:"webgl",kernelFunc:c4};function rd(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Jn({inputs:{x:r.complexTensorInfos.real},backend:n})}var iY={kernelName:vp,backendName:"webgl",kernelFunc:rd},lY="return float(int(x));";function uY(e,t){let n=new pa(e.shape,lY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function X2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Jn({inputs:{x:r},backend:n});let o=$t(r.shape),i=X2({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ha({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=rd({inputs:{input:r},backend:n}),i=X2({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Jn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return uY(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=c4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var cY={kernelName:Da,backendName:"webgl",kernelFunc:X2},d4="return ceil(x);",dY=et({opSnippet:d4,packedOpSnippet:d4,cpuKernelImpl:fX}),pY={kernelName:Fa,backendName:"webgl",kernelFunc:dY},hY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}},fY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function mY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Y().getBool("WEBGL_PACK_CLIP")?i=new fY(r.shape):i=new hY(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var gY={kernelName:Vr,backendName:"webgl",kernelFunc:mY},AY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,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 p4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function yY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new AY(s.shape),o=[p4(s,r.complexTensorInfos.real),p4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var xY={kernelName:Vu,backendName:"webgl",kernelFunc:yY},bY=class{constructor(e){this.outputShape=[],this.outputShape=D.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},vY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=D.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ft(s),a=Cn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),d=`if (${l} < ${i[0]}) {
|
|
return getChannel(
|
|
getT0(${c}), vec2(${u.join()}));
|
|
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
|
|
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${Of(o,l,m)}),
|
|
vec2(${Of(u,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${Of(o,l,h)}),
|
|
vec2(${Of(u,l,h)}));`,this.userCode=`
|
|
float getValue(${o.map(f=>"int "+f)}) {
|
|
${d}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[s-1]} = ${a[s-1]} + 1;
|
|
if (${a[s-1]} < ${n[s-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[s-2]} = ${a[s-2]} + 1;
|
|
if (${a[s-2]} < ${n[s-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[s-1]} = ${a[s-1]} - 1;
|
|
if (${a[s-2]} < ${n[s-2]} &&
|
|
${a[s-1]} < ${n[s-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Of(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Pf(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Jn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var wY={kernelName:fp,backendName:"webgl",kernelFunc:Pf};function hu(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>rd({inputs:{input:m},backend:n})),d=e.map(m=>Pf({inputs:{input:m},backend:n})),p=hu(c,t,n),h=hu(d,t,n),f=ha({inputs:{real:p,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(A=>{let y=w.sizeFromShape(A.shape.slice(t));return ye({inputs:{x:A},backend:n,attrs:{shape:[-1,y]}})}),d=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=D.computeOutShape(c.map(A=>A.shape),1),h=c[0].shape[0]===1,f=mX(d,p,s,h),m=D.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),d=hu(e.slice(0,c),t,n),p=hu(e.slice(c),t,n),h=hu([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new vY(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=kY(e,t,n),i=new bY(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ye({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function kY(e,t,n){let s=D.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ye({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function h4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=D.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return Jn({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return D.assertParamsConsistent(l,a),hu(i,a,n)}var IY={kernelName:Oi,backendName:"webgl",kernelFunc:h4},f4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,A=m?2:3,y=m?3:1,x="",b="";n&&(s?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${y}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${A}]) * 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 < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
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 (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${v}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},SY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${a}, ${o});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${s});
|
|
|
|
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 < ${c}; wF++) {
|
|
int xF = xFCorner + wF * ${i};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},CY=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{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=e,this.enableShapeUniforms=xs(this.outputShape.length);let{dataFormat:n}=t,s=Sn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
|
|
blockIndex = rc.y + ${c};
|
|
pos = rc.x + ${u};
|
|
|
|
${i}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${a}] && 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[${o}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${u*2+c}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${u*2+c}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${l}
|
|
|
|
${s.output} = result;
|
|
}
|
|
`}};function m4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,A=[];if(!((d===1||p===1)&&c>r4)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(ed(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let S=ye({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let C=Ff({a:v,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),_=s.texData.get(C.dataId);w.assert(_.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,_.shape=n.outShape,g=Jn({inputs:{x:C},backend:s}),g.shape=n.outShape,A.push(C)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ye({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ye({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Ff({a:v,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ye({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),A.push(v),A.push(k),A.push(S)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function g4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=p*d,A=[m,g],y=!0,x=!1,b=[],v=ye({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ye({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(k);let S=new CY(A,n),C=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],_=s.runWebGLProgram(S,[v],"float32",C),O=ye({inputs:{x:_},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(_),b.push(O);let E=r!=null,R=a!=null,T=i==="leakyrelu",P=i?Rf(i,!0):null,V=new Q6(O.shape,k.shape,[1,g,n.outChannels],y,x,E,P,R,T),j=[O,k];if(r&&j.push(r),R&&j.push(a),T){let te=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(te),b.push(te)}let q=s.runWebGLProgram(V,j,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],ee=ye({inputs:{x:q},backend:s,attrs:{shape:X}});b.push(q);for(let te of b)s.disposeIntermediateTensorInfo(te);return ee}function TY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,d=D.convertConv2DDataFormat(l),p=D.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=m4({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=g4({x:r,filter:a,convInfo:p,backend:n});else{let m=new f4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ye({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var NY={kernelName:$a,backendName:"webgl",kernelFunc:TY},EY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.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 < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${a}) {
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
} else {
|
|
float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},RY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${c}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - 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 < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${a}) {
|
|
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);
|
|
}
|
|
`}},_Y=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.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 < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${s} - ${o};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},DY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${i}, ${l}, ${u});
|
|
|
|
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 < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${s} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,d=D.convertConv2DDataFormat(l),p=D.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),h=new EY(p);return n.runWebGLProgram(h,[r,a],"float32")}var $Y={kernelName:tp,backendName:"webgl",kernelFunc:FY};function OY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,d=D.convertConv2DDataFormat(u),p=D.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),h=new RY(p);return n.runWebGLProgram(h,[r,a],"float32")}var PY={kernelName:Oa,backendName:"webgl",kernelFunc:OY};function MY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=D.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new SY(u);return n.runWebGLProgram(c,[r,a],"float32")}var zY={kernelName:Uu,backendName:"webgl",kernelFunc:MY};function LY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=D.computeConv3DInfo(r.shape,l,o,1,i),c=new _Y(u);return n.runWebGLProgram(c,[r,a],"float32")}var BY={kernelName:np,backendName:"webgl",kernelFunc:LY};function WY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=D.computeConv3DInfo(l,a.shape,i,1,o),c=new DY(u);return n.runWebGLProgram(c,[r,a],"float32")}var VY={kernelName:sp,backendName:"webgl",kernelFunc:WY},UY=J6+`
|
|
return cos(x);
|
|
`,HY=et({opSnippet:UY}),GY={kernelName:Pa,backendName:"webgl",kernelFunc:HY},jY=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,qY=et({opSnippet:jY}),XY={kernelName:Ma,backendName:"webgl",kernelFunc:qY},KY=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${y});
|
|
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 >= ${a}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${A};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},ZY=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new KY(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},YY={kernelName:Pi,backendName:"webgl",kernelFunc:ZY},A4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${y4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${ft(s)} coords = getOutputCoords();
|
|
int end = ${x4(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${x4(s,"coords")} = idx;
|
|
val += getX(${y4(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function y4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function x4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function JY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,u=D.getAxesPermutation([a],l),c=r;u!=null&&(c=Tn({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=D.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=c.shape[d],h=Jn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new A4(c.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new A4(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=D.getUndoAxesPermutation(u),m=Tn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var QY={kernelName:za,backendName:"webgl",kernelFunc:JY};function eJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=M6(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=hX(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var tJ={kernelName:rp,backendName:"webgl",kernelFunc:eJ},nJ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,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 / ${t};
|
|
int offset_h = imod(h, ${t});
|
|
int in_w = w / ${t};
|
|
int offset_w = imod(w, ${t});
|
|
int offset_d = (offset_h * ${t} + 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 sJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=u*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new nJ(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var rJ={kernelName:Mi,backendName:"webgl",kernelFunc:sJ},b4=class{constructor(e,t=!1,n=null,s=!1,r=!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=e.outShape,this.enableShapeUniforms=xs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,u="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&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 / ${i};
|
|
int q = d2 - d1 * ${i};
|
|
|
|
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 < ${a}; wR++) {
|
|
int xR = xRCorner + wR * dilations[0];
|
|
|
|
if (xR < 0 || xR >= inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${o}; 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;
|
|
${c}
|
|
${u}
|
|
setOutput(result);
|
|
}
|
|
`}},v4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=xs(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,d=c,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)p+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;for(let g=0;g<u;g++){for(let A=0;A<c;A++)p+=`
|
|
xTexelC${A*2} = vec4(0.0);
|
|
xTexelC${A*2}Ready = 0;
|
|
xTexelC${A*2+1} = vec4(0.0);
|
|
xTexelC${A*2+1}Ready = 0;
|
|
xC${A} = vec4(0.0);`;p+=`
|
|
xR = xRCorner + ${g} * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let A=0;A<(d+1)/2;A++){let y=A*2;if(p+=`
|
|
xC = xCCorner + ${y*l};
|
|
`,i===1){if(y<c&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`,l===1&&y>0?p+=`
|
|
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
|
|
`:p+=`
|
|
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${y} = vec4(previous.zw, xTexelC${y}.xy);
|
|
} else {
|
|
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
|
|
}
|
|
`):p+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xC${y} = xTexelC${y};
|
|
`,y+1<c)){let x=o%2==0?w.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
`,l>1&&(p+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`),p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
|
|
`):x===1?p+=`
|
|
xC${y+1} = xTexelC${y};
|
|
`:p+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y+1} = xTexelC${y+1};
|
|
`}}else y<c&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`,y+1<c&&(p+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
|
|
`)):(p+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(
|
|
xTexelC${y}.xy, xTexelC${y+1}.xy);
|
|
`,y+1<c&&(p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`)));y<c&&(p+=`
|
|
wTexel = getW(${g}, ${y}, d1, q);
|
|
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
|
|
`,y+1<c&&(p+=`
|
|
wTexel = getW(${g}, ${y+1}, d1, q);
|
|
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}p+=`
|
|
}
|
|
`}let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:h=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${h}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${a};
|
|
int q = d2 - d1 * ${a};
|
|
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);
|
|
|
|
${p}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${m}
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};function aJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=D.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new v4(d):p=new b4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var oJ={kernelName:La,backendName:"webgl",kernelFunc:aJ},iJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.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 * ${a} + dm;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
// TO DO: Vec4 over the batch size
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},lJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
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 < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.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 < ${i}; dm++) {
|
|
int d2 = d1 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function uJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=D.computeConv2DInfo(r.shape,c,o,i,l,u,!0),p=new iJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var cJ={kernelName:ap,backendName:"webgl",kernelFunc:uJ};function dJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=D.computeConv2DInfo(c,a.shape,o,i,l,u,!0),p=new lJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var pJ={kernelName:op,backendName:"webgl",kernelFunc:dJ},hJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function fJ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=ye({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new hJ(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ye({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var mJ={kernelName:ip,backendName:"webgl",kernelFunc:fJ},gJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${a});
|
|
const ivec2 pads = ivec2(${c}, ${d});
|
|
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 < ${o}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${i}; w++) {
|
|
int wIn = wBeg + w * ${u};
|
|
|
|
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 AJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=D.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new gJ(u);c=n.runWebGLProgram(d,[r,a],"float32");let p=ye({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),p}var yJ={kernelName:Hu,backendName:"webgl",kernelFunc:AJ};function xJ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=D.decodeEinsumEquation(r,a.length);D.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=D.getEinsumComputePath(i,l),d=c.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:A,expandDims:y}=D.getEinsumPermutation(h,l[g]),x;D.isIdentityPermutation(A)?x=a[g]:(x=Tn({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(x));let b=x.shape.slice();for(let v=0;v<y.length;++v)b.splice(y[v],0,1);w.arraysEqual(x.shape,b)||(x=ye({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=G2({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(u[m]>=0&&(p=Df({inputs:{x:p},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var bJ={kernelName:cp,backendName:"webgl",kernelFunc:xJ},vJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",wJ=`
|
|
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;
|
|
`,kJ=et({opSnippet:vJ,packedOpSnippet:wJ}),IJ={kernelName:Wa,backendName:"webgl",kernelFunc:kJ},SJ="return (b >= 1.0) ? a : a * (b + 1.0);",CJ=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,TJ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new nd(CJ,s.shape,r.shape):new du(SJ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},NJ={kernelName:dp,backendName:"webgl",kernelFunc:TJ},EJ=`
|
|
return vec4(equal(a, b));
|
|
`,RJ="return float(a == b);",_J=dn({opSnippet:RJ,packedOpSnippet:EJ,dtype:"bool",cpuKernelImpl:gX}),DJ={kernelName:Li,backendName:"webgl",kernelFunc:_J},FJ=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${D.ERF_P};
|
|
float a1 = ${D.ERF_A1};
|
|
float a2 = ${D.ERF_A2};
|
|
float a3 = ${D.ERF_A3};
|
|
float a4 = ${D.ERF_A4};
|
|
float a5 = ${D.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,$J=et({opSnippet:FJ}),OJ={kernelName:zi,backendName:"webgl",kernelFunc:$J},w4="return exp(x);",k4=et({opSnippet:w4,packedOpSnippet:w4,cpuKernelImpl:AX}),PJ={kernelName:Va,backendName:"webgl",kernelFunc:k4};function K2(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ye({inputs:{x:a},backend:s,attrs:{shape:i}})}var MJ={kernelName:Bi,backendName:"webgl",kernelFunc:K2},I4="return exp(x) - 1.0;",zJ=et({opSnippet:I4,packedOpSnippet:I4,cpuKernelImpl:yX}),LJ={kernelName:Wi,backendName:"webgl",kernelFunc:zJ},S4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${o}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${s});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${s}; 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) / ${a};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function C4(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ye({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new S4("real",l,t),c=new S4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=ha({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ye({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function BJ(e){let{inputs:t,backend:n}=e,{input:s}=t;return C4(s,!1,n)}var WJ={kernelName:pp,backendName:"webgl",kernelFunc:BJ},VJ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}};function ad(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new VJ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var UJ={kernelName:Gu,backendName:"webgl",kernelFunc:ad},HJ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${t} - x - 1;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},GJ={kernelName:Vi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new HJ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},T4="return floor(x);",jJ=et({opSnippet:T4,packedOpSnippet:T4,cpuKernelImpl:xX}),qJ={kernelName:Ua,backendName:"webgl",kernelFunc:jJ},XJ=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,KJ=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,ZJ=dn({opSnippet:XJ,packedOpSnippet:KJ,dtype:"int32"}),YJ={kernelName:Ha,backendName:"webgl",kernelFunc:ZJ},JJ=class{constructor(e){this.variableNames=["A"];let t=Sn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},QJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Sn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}.0, ${n}.0);
|
|
vec4 values = ${t.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);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},eQ={kernelName:Fp,backendName:"webgl",kernelFunc:tQ},fu;function tQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(fu==null&&(fu=document.createElement("canvas").getContext("2d")),fu.canvas.width=l,fu.canvas.height=u,fu.drawImage(r,0,0,l,u),r=fu.canvas);let p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=As.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new QJ(d):new JJ(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function nQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=D.convertConv2DDataFormat(c),g=D.computeConv2DInfo(r.shape,a.shape,l,d,u,p,!1,m),A,y=[];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"))A=m4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=g4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,k=h==="leakyrelu",S=h?Rf(h,!1):null,C=new f4(g,b,S,v,k),_=[r,a];if(o&&_.push(o),i&&_.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));_.push(O),y.push(O)}A=n.runWebGLProgram(C,_,"float32")}let x=ye({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var sQ={kernelName:Co,backendName:"webgl",kernelFunc:nQ};function rQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(D.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=D.computeConv2DInfo(r.shape,a.shape,l,m,u,d,!0),A=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=p?Rf(p,A):null,x=[r,a],b=o!=null,v=i!=null,k=p==="leakyrelu";if(b&&x.push(o),v&&x.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(O),f.push(O)}let S;A?S=new v4(g,b,y,v,k):S=new b4(g,b,y,v,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=n.runWebGLProgram(S,x,"float32",C);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),_}var aQ={kernelName:To,backendName:"webgl",kernelFunc:rQ},oQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ft(t.length),r=ft(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${s} strides = ${s}(${this.strides});
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${a};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function iQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[l,u,c,d]=D.prepareAndValidate(s,r),p=ye({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=ye({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),y=n.bufferSync(s),x=bX(A,y,s.dtype,u,o,c,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new oQ(o,d,[u,c]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ye({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var lQ={kernelName:Hi,backendName:"webgl",kernelFunc:iQ},uQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ft(this.rank),s=cQ(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function cQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function N4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=w.parseAxisParam(o,r.shape)[0],u=D.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=w.sizeFromShape(a.shape),d=[],p=ye({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ye({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});d.push(p),d.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(p),b=vX(x,y,f);return d.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new uQ(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let A=ye({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}var dQ={kernelName:Ui,backendName:"webgl",kernelFunc:N4},pQ="return float(a > b);",hQ=`
|
|
return vec4(greaterThan(a, b));
|
|
`,fQ=dn({opSnippet:pQ,packedOpSnippet:hQ,cpuKernelImpl:wX,dtype:"bool"}),mQ={kernelName:Gi,backendName:"webgl",kernelFunc:fQ},gQ="return float(a >= b);",AQ=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,yQ=dn({opSnippet:gQ,packedOpSnippet:AQ,dtype:"bool",cpuKernelImpl:kX}),xQ={kernelName:ja,backendName:"webgl",kernelFunc:yQ};function bQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return C4(s,!0,n)}var vQ={kernelName:hp,backendName:"webgl",kernelFunc:bQ},wQ="return float(!isnan(x) && !isinf(x));",kQ=et({opSnippet:wQ,dtype:"bool"}),IQ={kernelName:ji,backendName:"webgl",kernelFunc:kQ},SQ="return float(isinf(x));",CQ=et({opSnippet:SQ,dtype:"bool"}),TQ={kernelName:qi,backendName:"webgl",kernelFunc:CQ},NQ="return float(isnan(x));",EQ=et({opSnippet:NQ,dtype:"bool"}),RQ={kernelName:Xi,backendName:"webgl",kernelFunc:EQ},_Q="return float(a < b);",DQ=`
|
|
return vec4(lessThan(a, b));
|
|
`,FQ=dn({opSnippet:_Q,packedOpSnippet:DQ,cpuKernelImpl:IX,dtype:"bool"}),$Q={kernelName:Ki,backendName:"webgl",kernelFunc:FQ},OQ="return float(a <= b);",PQ=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,MQ=dn({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:SX,dtype:"bool"}),zQ={kernelName:Zi,backendName:"webgl",kernelFunc:MQ};function LQ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=CX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var BQ={kernelName:mp,backendName:"webgl",kernelFunc:LQ},WQ=`if (x < 0.0) return NAN;
|
|
return log(x);`,VQ=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,UQ=et({opSnippet:WQ,packedOpSnippet:VQ,cpuKernelImpl:TX}),HQ={kernelName:Ka,backendName:"webgl",kernelFunc:UQ},GQ="return log(1.0 + x);",jQ=et({opSnippet:GQ}),qQ={kernelName:Yi,backendName:"webgl",kernelFunc:jQ},XQ="return float(a >= 1.0 && b >= 1.0);",KQ=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,ZQ=dn({opSnippet:XQ,packedOpSnippet:KQ,dtype:"bool"}),YQ={kernelName:Ji,backendName:"webgl",kernelFunc:ZQ},JQ="return float(!(x >= 1.0));",QQ=et({opSnippet:JQ}),eee={kernelName:ju,backendName:"webgl",kernelFunc:QQ},tee="return float(a >= 1.0 || b >= 1.0);",nee=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,see=dn({opSnippet:tee,packedOpSnippet:nee,dtype:"bool"}),ree={kernelName:qu,backendName:"webgl",kernelFunc:see},aee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,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 = -${a}; j <= ${a}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${o}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},oee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,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 - ${a};
|
|
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 = - ${a}; j <= ${a}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
|
|
|
|
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 * ${i};
|
|
setOutput(result);
|
|
}
|
|
`}},iee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new oee(r.shape,a,o,i,l):new aee(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},lee={kernelName:Xu,backendName:"webgl",kernelFunc:iee},uee=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,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 - ${t})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${t} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${s}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${s})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},cee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new uee(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},dee={kernelName:gp,backendName:"webgl",kernelFunc:cee};function pee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=si(i,e.dtype,"max",s),u=ye({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function E4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=c!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[c[S]];let v=H2(x,r.shape,r.dtype,c,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=v}else h=_f(r,c,n);u=D.getInnerMostAxes(u.length,i)}D.assertAxesAreInnerMostDims("max",u,i);let[f,m]=D.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=D.expandShapeToKeepDim(f,l));let A;if(p){let x=n.texData.get(h.dataId).values,b=NX(x,w.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(A.dataId);v.values=b}else A=pee(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var hee={kernelName:Za,backendName:"webgl",kernelFunc:E4},fee=q6+`
|
|
return max(a, b);
|
|
`,mee=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Ef+`
|
|
return result;
|
|
`,gee=dn({opSnippet:fee,packedOpSnippet:mee,cpuKernelImpl:EX}),Aee={kernelName:Ya,backendName:"webgl",kernelFunc:gee};function yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ru(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(D.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=D.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Jn({inputs:{x:r},backend:n});let d=new sd(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var xee={kernelName:Ja,backendName:"webgl",kernelFunc:yee};function bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=D.computePool3DInfo(r.shape,a,o,c,i,u,l),p=new j2(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var vee={kernelName:Ku,backendName:"webgl",kernelFunc:bee},wee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
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 < ${r};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${t}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},kee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${c}, ${d}, ${p});
|
|
|
|
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 < ${i};
|
|
wD += ${r}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${u};
|
|
wC += ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${u} +
|
|
wR * ${u} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=D.computePool3DInfo(o.shape,i,l,d,u,c),h=new j2(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new kee(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var See={kernelName:yp,backendName:"webgl",kernelFunc:Iee};function Cee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;ru([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=D.computePool2DInfo(i.shape,l,u,1,c,d),h=!0,f=new sd(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new wee(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var Tee={kernelName:Ap,backendName:"webgl",kernelFunc:Cee};function Nee(e,t,n,s){let r=new sd(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new sd(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Eee={kernelName:xp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];w.assert(D.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=D.computePool2DInfo(s.shape,r,a,u,o),[d,p]=Nee(s,i,c,l);return[d,p]}};function Ree(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=si(i,"float32","mean",s),u=ye({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var _ee={kernelName:Qa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=D.getAxesPermutation(u,i),d=c!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,v=new Array(i);for(let C=0;C<v.length;C++)v[C]=s.shape[c[C]];let k=H2(b,s.shape,s.dtype,c,v);f=o.makeTensorInfo(v,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=_f(s,c,o);h.push(f),u=D.getInnerMostAxes(u.length,i)}D.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=D.computeOutAndReduceShapes(f.shape,u),A=m;r&&(A=D.expandShapeToKeepDim(m,l));let y=Ree(f,g,A,o);for(let x of h)o.disposeIntermediateTensorInfo(x);return y}};function Dee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=D.getAxesPermutation(u,i),d=r;c!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:c}}),u=D.getInnerMostAxes(u.length,r.shape.length)),D.assertAxesAreInnerMostDims("min",u,i);let[p,h]=D.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=si(m,m.dtype,"min",n),A;if(o){let y=D.expandShapeToKeepDim(p,l);A=ye({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ye({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var Fee={kernelName:eo,backendName:"webgl",kernelFunc:Dee},$ee=q6+`
|
|
return min(a, b);
|
|
`,Oee=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Ef+`
|
|
return result;
|
|
`,Pee=dn({opSnippet:$ee,packedOpSnippet:Oee,cpuKernelImpl:RX}),Mee={kernelName:to,backendName:"webgl",kernelFunc:Pee},zee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=ft(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
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=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${s}; 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};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}},Lee=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ft(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Cn("rc",s),l=Cn("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${d};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${d};
|
|
}
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${d}) +
|
|
gte * ((end - 1) * 2 - source + ${d});
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},Bee=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lee(s.shape,r,a):new zee(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Wee={kernelName:no,backendName:"webgl",kernelFunc:Bee},Vee=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Uee=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Ef+`
|
|
return result;
|
|
`,Hee=dn({opSnippet:Vee,packedOpSnippet:Uee}),Gee={kernelName:Qi,backendName:"webgl",kernelFunc:Hee},jee=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,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 < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}},qee=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Xee=`
|
|
// 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;
|
|
`,R4=dn({opSnippet:qee,packedOpSnippet:Xee,checkOutOfBounds:!0}),Kee={kernelName:Ba,backendName:"webgl",kernelFunc:R4},_4="return a - b;",D4=dn({opSnippet:_4,packedOpSnippet:_4,supportsComplex:!0,cpuKernelImpl:jX}),Zee={kernelName:vo,backendName:"webgl",kernelFunc:D4};function F4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=E4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=D.expandShapeToKeepDim(i.shape,o),u=ye({inputs:{x:i},backend:n,attrs:{shape:l}}),c=D4({inputs:{a:r,b:u},backend:n}),d=k4({inputs:{x:c},backend:n}),p=Df({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ye({inputs:{x:p},backend:n,attrs:{shape:l}}),f=R4({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var Yee={kernelName:xo,backendName:"webgl",kernelFunc:F4};function Jee(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:F4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new jee(u,c,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Qee={kernelName:bp,backendName:"webgl",kernelFunc:Jee},$4="return -x;";function ete(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=DX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new uu(s.shape,$4):r=new pa(s.shape,$4),n.runWebGLProgram(r,[s],s.dtype)}var tte={kernelName:el,backendName:"webgl",kernelFunc:ete},nte=rr.nonMaxSuppressionV3Impl;function ste(e){D.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=nte(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var rte={kernelName:nl,backendName:"webgl",kernelFunc:ste},ate=rr.nonMaxSuppressionV4Impl;function ote(e){D.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=ate(c,d,o,i,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var ite={kernelName:sl,backendName:"webgl",kernelFunc:ote},lte=rr.nonMaxSuppressionV5Impl;function ute(e){D.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:A}=lte(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var cte={kernelName:rl,backendName:"webgl",kernelFunc:ute},dte=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${s}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},pte=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=w.sizeFromShape(r.shape),u=new dte(l,a,o,i),c=ye({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let p=[...r.shape,a],h=ye({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},hte={kernelName:ro,backendName:"webgl",kernelFunc:pte};function Mf(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=rd({inputs:{input:s},backend:n}),a=Mf({inputs:{x:r},backend:n}),o=Pf({inputs:{input:s},backend:n}),i=Mf({inputs:{x:o},backend:n}),l=ha({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return ad({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var fte={kernelName:kl,backendName:"webgl",kernelFunc:Mf};function O4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=rd({inputs:{input:s},backend:n}),a=O4({inputs:{x:r},backend:n}),o=Pf({inputs:{input:s},backend:n}),i=Mf({inputs:{x:o},backend:n}),l=ha({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return ad({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var mte={kernelName:al,backendName:"webgl",kernelFunc:O4};function gte(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return K2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=K2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=h4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Ate={kernelName:ol,backendName:"webgl",kernelFunc:gte},yte=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=ft(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
}
|
|
`}},xte=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ft(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Cn("rc",s),l=Cn("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
|
|
if(${u}) {
|
|
`,s===1?"":`}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
|
|
if(${u}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
|
|
${d[f]}
|
|
if (${p}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`;h+=s===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},P4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let u=a.map((c,d)=>c[0]+r.shape[d]+c[1]);return ad({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xte(r.shape,a,o):new yte(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},bte={kernelName:ao,backendName:"webgl",kernelFunc:P4},vte=`
|
|
if(a < 0.0 && floor(b) < b){
|
|
return NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
return (round(mod(b, 2.0)) != 1) ?
|
|
pow(abs(a), b) : sign(a) * pow(abs(a), b);
|
|
`,wte=`
|
|
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
|
|
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
|
|
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
|
|
vec4 result = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
bvec4 isExpZero = equal(b, vec4(0.0));
|
|
result.r = isExpZero.r ? 1.0 : result.r;
|
|
result.g = isExpZero.g ? 1.0 : result.g;
|
|
result.b = isExpZero.b ? 1.0 : result.b;
|
|
result.a = isExpZero.a ? 1.0 : result.a;
|
|
|
|
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
|
|
`+Ef+`
|
|
return result;
|
|
`,kte=dn({opSnippet:vte,packedOpSnippet:wte}),Ite={kernelName:oo,backendName:"webgl",kernelFunc:kte};function Ste(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=w.parseAxisParam(a,r.shape),c=u,d=D.getAxesPermutation(c,i),p=r;d!=null&&(p=Tn({inputs:{x:r},backend:n,attrs:{perm:d}}),c=D.getInnerMostAxes(c.length,i),l.push(p)),D.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=$X(p.shape,p.dtype,f,c);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=D.computeOutAndReduceShapes(p.shape,c),g=w.sizeFromShape(m),A=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=Lp(r.dtype),x=si(A,y,"prod",n);h=ye({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(o){l.push(h);let f=D.expandShapeToKeepDim(h.shape,u);h=ye({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Cte={kernelName:il,backendName:"webgl",kernelFunc:Ste},M4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=OX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Tte={kernelName:Zu,backendName:"webgl",kernelFunc:M4},Nte="return 1.0 / x;",Ete=et({opSnippet:Nte}),Rte={kernelName:ll,backendName:"webgl",kernelFunc:Ete},_te=Gs+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Dte=`
|
|
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;
|
|
`,Fte=et({opSnippet:_te,packedOpSnippet:Dte}),$te={kernelName:lo,backendName:"webgl",kernelFunc:Fte},Ote=Gs+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Pte=`
|
|
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;
|
|
`,Mte=et({opSnippet:Ote,packedOpSnippet:Pte}),zte={kernelName:co,backendName:"webgl",kernelFunc:Mte},Lte=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${d};
|
|
|
|
// 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);
|
|
}
|
|
`}},Bte=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.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 = ${d};
|
|
|
|
// 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 Wte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Bte(r.shape,l,u,a,o):new Lte(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var Vte={kernelName:uo,backendName:"webgl",kernelFunc:Wte},Ute=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,f=Math.ceil(p)*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(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// 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 >= ${a}) {
|
|
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 >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${s-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), ${r-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 Hte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Ute(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Gte={kernelName:kp,backendName:"webgl",kernelFunc:Hte},jte=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},qte=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.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 = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Xte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new qte(r.shape,l,u,a,o):new jte(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var Kte={kernelName:Yu,backendName:"webgl",kernelFunc:Xte},Zte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// 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 >= ${a}) {
|
|
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 >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${i[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${i[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${s}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Yte(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Zte(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Jte={kernelName:wp,backendName:"webgl",kernelFunc:Yte},Qte=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=ft(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},ene=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=Cn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ft(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${i(s.slice())};
|
|
if(${r}){
|
|
result.g = ${l(s.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${u(s.slice())};
|
|
if(${r}) {
|
|
result.a = ${c(s.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function tne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return Jn({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ene(r.shape,i):new Qte(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var nne={kernelName:po,backendName:"webgl",kernelFunc:tne},sne=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${t.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]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},rne={kernelName:Il,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new sne(s.shape,a),[u,c]=D.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},ane=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,one=et({opSnippet:ane}),ine={kernelName:ho,backendName:"webgl",kernelFunc:one},lne="return inversesqrt(x);",une=et({opSnippet:lne,cpuKernelImpl:PX}),cne={kernelName:fo,backendName:"webgl",kernelFunc:une},z4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=ft(r.length),l=ft(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${i} strides = ${i}(${r});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${c});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${p};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function dne(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=D.calculateShapes(a,r,o),p=[d/u,u];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ye({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ye({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new z4(l,i,h.shape.length,f.shape.length,c,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),y=ye({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),y}var pne={kernelName:cl,backendName:"webgl",kernelFunc:dne},hne=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=ft(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function fne(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new hne(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ss(r.dtype,a.dtype))}var mne={kernelName:dl,backendName:"webgl",kernelFunc:fne},gne=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${D.SELU_SCALEALPHA};
|
|
float scale = ${D.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Ane=et({opSnippet:gne}),yne={kernelName:pl,backendName:"webgl",kernelFunc:Ane},L4="return 1.0 / (1.0 + exp(-1.0 * x));",xne=et({opSnippet:L4,packedOpSnippet:L4,cpuKernelImpl:MX}),bne={kernelName:go,backendName:"webgl",kernelFunc:xne},vne=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,wne=et({opSnippet:vne}),kne={kernelName:ml,backendName:"webgl",kernelFunc:wne},Ine=J6+`
|
|
return sin(x);
|
|
`,Sne=et({opSnippet:Ine}),Cne={kernelName:mo,backendName:"webgl",kernelFunc:Sne},Tne=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Nne=et({opSnippet:Tne}),Ene={kernelName:fl,backendName:"webgl",kernelFunc:Nne},Rne=`
|
|
float epsilon = 1.1920928955078125e-7;
|
|
float threshold = log(epsilon) + 2.0;
|
|
|
|
bool too_large = x > -threshold;
|
|
bool too_small = x < threshold;
|
|
|
|
float result;
|
|
float exp_x = exp(x);
|
|
|
|
if (too_large){
|
|
result = x;
|
|
}
|
|
else if (too_small){
|
|
result = exp_x;
|
|
}
|
|
else{
|
|
result = log(exp_x + 1.0);
|
|
}
|
|
return result;
|
|
`,_ne=et({opSnippet:Rne}),Dne={kernelName:gl,backendName:"webgl",kernelFunc:_ne},Fne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let u=[],c=P4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=D.getReshaped(c.shape,a,i,!1),p=D.getPermuted(d.length,a.length,!1),h=D.getReshapedPermuted(c.shape,a,i,!1),f=ye({inputs:{x:c},backend:n,attrs:{shape:d}}),m=Tn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ye({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},$ne={kernelName:Al,backendName:"webgl",kernelFunc:Fne};function One(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,p,h,f,m]=LX(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Pne={kernelName:Ip,backendName:"webgl",kernelFunc:One};function Mne(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=BX(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var zne={kernelName:Sp,backendName:"webgl",kernelFunc:Mne};function Lne(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=L6(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Bne={kernelName:Cp,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=L6(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var Vne={kernelName:Tp,backendName:"webgl",kernelFunc:Wne};function Une(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=D.calculateShapes(a,r,i),p=!1,h=new z4(u,l,r.shape.length,a.shape.length,c,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ye({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Hne={kernelName:Np,backendName:"webgl",kernelFunc:Une};function Gne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=D.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=pu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var jne={kernelName:yl,backendName:"webgl",kernelFunc:Gne},B4="return sqrt(x);",qne=et({opSnippet:B4,packedOpSnippet:B4,cpuKernelImpl:WX}),Xne={kernelName:Ao,backendName:"webgl",kernelFunc:qne},Kne="return x * x;",Zne=et({opSnippet:Kne}),Yne={kernelName:Ju,backendName:"webgl",kernelFunc:Zne},W4="return (a - b) * (a - b);",Jne=dn({opSnippet:W4,packedOpSnippet:W4}),Qne={kernelName:bo,backendName:"webgl",kernelFunc:Jne};function ese({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=Gs+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new pa(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var tse={kernelName:Hr,backendName:"webgl",kernelFunc:ese},nse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ft(n.length),a=ft(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}};function sse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=vn.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=ye({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=pu({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ye({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,_=Ue(x.shape,x.dtype,C),O=VX(y,_,m,f);b=n.makeTensorInfo(y,x.dtype,O.values)}else{let S=new nse(f,m,y);b=n.runWebGLProgram(S,[x],x.dtype)}let v=ye({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var rse={kernelName:xl,backendName:"webgl",kernelFunc:sse};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[f,m]=UX(p,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var ose={kernelName:Ep,backendName:"webgl",kernelFunc:ase};function ise(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,d]=HX(i,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var lse={kernelName:Rp,backendName:"webgl",kernelFunc:ise};function use(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=GX(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var cse={kernelName:_p,backendName:"webgl",kernelFunc:use},dse="return tan(x);",pse=et({opSnippet:dse}),hse={kernelName:wo,backendName:"webgl",kernelFunc:pse},fse=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,mse=et({opSnippet:fse}),gse={kernelName:ko,backendName:"webgl",kernelFunc:mse},Ase=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ft(this.rank),r=yse(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function yse(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function V4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(p=>w.decodeString(p)):l,c=Ue(r.shape,r.dtype,u),d=qX(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Ase(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var xse={kernelName:Ur,backendName:"webgl",kernelFunc:V4},bse=class{constructor(e){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=e,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));
|
|
}
|
|
}
|
|
`}},vse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,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 ri(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function U4(e){let t=1;for(;t<e;)t*=2;return t}function wse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let O=n.readSync(r.dataId),[E,R]=XX(O,u,r.dtype,a,o);return[n.makeTensorInfo(E.shape,E.dtype,E.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,ad({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=w.sizeFromShape(u)/c,g=ye({inputs:{x:h},attrs:{shape:[m,c]},backend:n});p&&ri(n,h);let A=U4(a),y=U4(c),x=null,b=()=>x===null?[g,g]:[g,x],v=(O,E,R)=>{let T=b(),P=new bse(R),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[O],[E]],q=x;x=n.runWebGLProgram(P,T,"int32",j),ri(n,q)};for(let O=1;O<A;O*=2){let E=O*2;for(let R=O;R>=1;R/=2)v(E,R,[m,y])}for(let O=y;O>A;O/=2){let E=b(),R=new vse([m,O/2]),P=[[c],[x===null?1:0],[A]],V=x;x=n.runWebGLProgram(R,E,"int32",P),ri(n,V);let j=A/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=pu({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),ri(n,k);let S=N4({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});ri(n,g);let C=u.slice(0,-1);C.push(a),k=x,x=ye({inputs:{x},attrs:{shape:C},backend:n}),ri(n,k);let _=S;return S=ye({inputs:{x:S},attrs:{shape:C},backend:n}),ri(n,_),[S,x]}var kse={kernelName:bl,backendName:"webgl",kernelFunc:wse},Ise=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${i} == 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 (${i} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${i} == 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 < ${e} && 0 <= coordX && coordX < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
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(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${o} == 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 Sse(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,p,h]=r.shape,[f,m]=u!=null?u:[d,p],g=[c,f,m,h],A=new Ise(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var Cse={kernelName:vl,backendName:"webgl",kernelFunc:Sse};function Tse(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ru(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=KX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var Nse={kernelName:Dp,backendName:"webgl",kernelFunc:Tse};function Ese(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=pu({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ye({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Rse={kernelName:wl,backendName:"webgl",kernelFunc:Ese},_se=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,d=`
|
|
sumValue += dot(values, segFilter);
|
|
`,p="";r%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${p}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${a})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${a})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; 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
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${c===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
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===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
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===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
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function Dse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=D.getAxesPermutation([u],i),d=r;c!=null&&(d=Tn({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(d),u=D.getInnerMostAxes(1,i)[0]);let p=D.segment_util.computeOutShape(d.shape,u,o),h=w.sizeFromShape([d.shape[u]]),f=ye({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Lp(r.dtype),g=(b,v,k,S,C)=>{let _=b.shape[0],O=b.shape[1],E=D.segment_util.segOpComputeOptimalWindowSize(O,C),R={windowSize:E,inSize:O,batchSize:_,numSegments:C},T=new _se(R,v),P=n.compileAndRun(T,[b,k],S);if(l.push(P),P.shape[1]===C)return P;let V=M4({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),j=V4({inputs:{x:V},backend:n,attrs:{reps:[O/E]}});return l.push(V),l.push(j),g(P,v,j,S,C)},A=g(f,"unsortedSegmentSum",a,m,o),y=ye({inputs:{x:A},backend:n,attrs:{shape:p}}),x=y;if(c!=null){l.push(y);let b=D.getUndoAxesPermutation(c);x=Tn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Fse={kernelName:Qu,backendName:"webgl",kernelFunc:Dse},$se=[lee,dee,XK,ZK,QK,nZ,rZ,iZ,uZ,dZ,mZ,AZ,bZ,kZ,RZ,CZ,FZ,MZ,OZ,WZ,UZ,GZ,KZ,nY,rY,cY,pY,gY,xY,EK,IY,$Y,PY,NY,BY,VY,zY,GY,XY,YY,QY,tJ,rJ,cJ,pJ,oJ,mJ,yJ,bJ,IJ,NJ,DJ,OJ,PJ,MJ,LJ,WJ,UJ,GJ,qJ,YJ,eQ,sQ,aQ,lQ,dQ,mQ,xQ,NK,vQ,wY,IQ,TQ,RQ,_K,$Q,zQ,BQ,qQ,HQ,YQ,eee,ree,hee,vee,xee,See,Tee,Eee,Aee,_ee,Fee,Mee,Wee,Gee,Qee,PK,tte,rte,ite,cte,oY,hte,mte,Ate,bte,Ite,FK,Cte,Tte,iY,Kee,Rte,zte,$te,zK,Vte,Gte,Kte,Jte,nne,rne,ine,cne,pne,mne,yne,bne,kne,Cne,Ene,eY,Yee,Dne,$ne,Pne,zne,Bne,Vne,Hne,jne,Xne,Yne,Qne,tse,rse,ose,lse,cse,Zee,GK,hse,gse,xse,kse,Cse,jK,Nse,Rse,Fse,fte];for(let e of $se)No(e);var Mn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Mn||(Mn={}));var od;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(od||(od={}));var H4;function Ose(e){H4=e.wasm.cwrap(So,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Pse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let C=n.dataIdMap.get(o.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);f=C.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=od[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],y=u?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,A,y],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return H4(p,k,r.shape.length,h,S,a.shape.length,l,u,g,f,m,d||0,v),b}var Mse={kernelName:So,backendName:"wasm",setupFunc:Ose,kernelFunc:Pse};function pn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var zse=pn(Ii);function Nn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,p=i.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,f=D.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),A=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,u.shape.length,p,A,c.shape.length,Mn[u.dtype],y);if(t&&u.dtype==="float32")return x(),m;let b=D.getBroadcastDims(u.shape,f),v=D.getBroadcastDims(c.shape,f),k=b.every((C,_)=>C===_),S=v.every((C,_)=>C===_);if(k&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Lse=!0,Bse=Nn(Wr,Lse),G4;function Wse(e){G4=e.wasm.cwrap(Na,null,["array","number","number","number"])}function Vse(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return G4(a,r.length,Mn[s.dtype],o),s}var Use={kernelName:Na,backendName:"wasm",setupFunc:Wse,kernelFunc:Vse};function zf(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var Hse={kernelName:qa,backendName:"wasm",kernelFunc:zf},j4;function Gse(e){j4=e.wasm.cwrap(Io,null,["number","array","number","number","number","array","number"])}function mu(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=qse(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=jse(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=zf({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,l.dtype),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return j4(c,h,l.shape.length,Mn[l.dtype],d,p,a.length),u}function jse(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function qse(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var Xse={kernelName:Io,backendName:"wasm",kernelFunc:mu,setupFunc:Gse};function fa(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=D.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=D.getInnerMostAxes(o.length,r),l=mu({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:u}}var q4;function Kse(e){q4=e.wasm.cwrap(Ti,null,["number, number, number"])}function Zse(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;D.assertAxesAreInnerMostDims("all",d,f);let[m,g]=D.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;q4(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Yse={kernelName:Ti,backendName:"wasm",setupFunc:Kse,kernelFunc:Zse},X4;function Jse(e){X4=e.wasm.cwrap(Ni,null,["number, number, number"])}function Qse(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;D.assertAxesAreInnerMostDims("any",d,f);let[m,g]=D.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;X4(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var ere={kernelName:Ni,backendName:"wasm",setupFunc:Jse,kernelFunc:Qse},K4;function tre(e){K4=e.wasm.cwrap(Ea,null,["number","number","number","number","number"])}function nre(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:u,axes:c,inputWasTransposed:d}=fa(a,r,t);if(d){let A=t.dataIdMap.get(u.dataId).id;A!==o&&(l=u,i=A)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=l.shape[c[0]];return K4(i,Mn[l.dtype],m,g,f),d&&t.disposeData(u.dataId),h}var sre={kernelName:Ea,backendName:"wasm",kernelFunc:nre,setupFunc:tre},Z4;function rre(e){Z4=e.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function are(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=D.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,A=c.strideHeight,y=c.strideWidth,x=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=s.makeOutput(c.outShape,"float32"),v=s.dataIdMap.get(b.dataId).id;return Z4(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,v),b}var ore={kernelName:Ra,backendName:"wasm",setupFunc:rre,kernelFunc:are};function zn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var ire={kernelName:ul,backendName:"wasm",kernelFunc:zn},Y4;function lre(e){Y4=e.wasm.cwrap(_a,null,["number","array","number","number","array","number","number","number","number"])}function ure(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),A=w.sizeFromShape(m),y=g===A||g===1||A===1;w.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>A?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);w.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,p]:[g,p,c],k=i?[A,h,d]:[A,d,h],S=zn({inputs:{x:r},backend:n,attrs:{shape:v}}),C=zn({inputs:{x:a},backend:n,attrs:{shape:k}}),_=n.dataIdMap.get(S.dataId).id,O=n.dataIdMap.get(C.dataId).id,E=o?S.shape[2]:S.shape[1],R=i?C.shape[1]:C.shape[2],T=Math.max(g,A),P=n.makeOutput([T,E,R],S.dtype),V=n.dataIdMap.get(P.dataId).id,j=new Uint8Array(new Int32Array(S.shape).buffer),q=new Uint8Array(new Int32Array(C.shape).buffer);return Y4(_,j,S.shape.length,O,q,C.shape.length,o,i,V),n.disposeData(S.dataId),n.disposeData(C.dataId),P.shape=b,P}var cre={kernelName:_a,backendName:"wasm",setupFunc:lre,kernelFunc:ure};function id(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=vn.parseSliceParams(t,n,s),i=vn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(i){let f=vn.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(o))),u}if(t.dtype==="string"){let f=ff(l,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let p=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)dre(l,c[0],p,a,o);else if(h===3)pre(l,c[0],c[1],p,a,o);else if(h===4)hre(l,c[0],c[1],c[2],p,a,o);else{let f=ff(l,a,o,t.shape,t.dtype);p.set(f)}return u}function dre(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function pre(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],u=r[2],c=i+a[0],d=l+a[1];for(let p=i;p<c;p++)for(let h=l;h<d;h++){let f=p*t+h*n+u;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function hre(e,t,n,s,r,a,o){let i=0,l=a[0],u=a[1],c=a[2],d=l+o[0],p=u+o[1],h=c+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=u;g<p;g++)for(let A=c;A<h;A++){let y=m*t+g*n+A*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var fre={kernelName:hl,backendName:"wasm",kernelFunc:id};function mre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,y)=>A*y),l=D.getReshaped(r.shape,a,i),u=D.getPermuted(l.length,a.length),c=D.getReshapedPermuted(r.shape,a,i),d=D.getSliceBeginCoords(o,a.length),p=D.getSliceSize(c,o,a.length),h=zn({inputs:{x:r},backend:n,attrs:{shape:l}}),f=mu({inputs:{x:h},backend:n,attrs:{perm:u}}),m=zn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=id({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var gre={kernelName:$i,backendName:"wasm",kernelFunc:mre};function Lf(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Are={kernelName:Da,backendName:"wasm",kernelFunc:Lf},yre=pn(Fa),J4;function xre(e){J4=e.wasm.cwrap(Vr,null,["number","number","number","number"])}function bre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return J4(i,a,o,u),l}var vre={kernelName:Vr,backendName:"wasm",setupFunc:xre,kernelFunc:bre};function Q4(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=D.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return zf({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(D.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=w.sizeFromShape(x.shape.slice(s));return zn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=D.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=y2(f,r,t[0].dtype,m),A=D.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=D.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return u+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*u;for(let m=0;m<d.length;m++){let g=c[m],A=h*g,y=d[m].subarray(A,A+g);p.set(y,f),f+=g}}return o}var wre={kernelName:Oi,backendName:"wasm",kernelFunc:Q4},ek;function kre(e){ek=e.wasm.cwrap($a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ire(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d,dataFormat:p}=n,h=D.convertConv2DDataFormat(p),f=D.computeConv2DInfo(r.shape,a.shape,l,u,c,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,y=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,C=f.strideWidth,_=f.inChannels,O=f.outChannels,E=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Please use 'channelsLast'.`);let E=s.makeOutput(h.outShape,"float32"),R=s.dataIdMap.get(E.dataId).id;return ak(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,y,x,O,b,v,k,S,C,_,R),E}var Vre={kernelName:La,backendName:"wasm",setupFunc:Bre,kernelFunc:Wre},Ure=pn(Wa),Hre=!1,Gre=Nn(Li,Hre,"bool"),jre=pn(Va);function Y2(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),zn({inputs:{x:r},backend:s,attrs:{shape:i}})}var qre={kernelName:Bi,backendName:"wasm",kernelFunc:Y2};function ok(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var Xre={kernelName:Gu,backendName:"wasm",kernelFunc:ok},ik;function Kre(e){ik=e.wasm.cwrap(Vi,null,["number","number","number","number","number","number"])}function Zre(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,u,c]=s.shape;return ik(a,i,l,u,c,o),r}var Yre={kernelName:Vi,backendName:"wasm",kernelFunc:Zre,setupFunc:Kre},Jre=pn(Ua),Qre=!1,eae=Nn(Ha,Qre),lk;function tae(e){lk=e.wasm.cwrap(Ga,null,["number","number","number","number","number","number","number"])}function nae(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return lk(c,d,p,h,f,r,g),m}var sae={kernelName:Ga,backendName:"wasm",setupFunc:tae,kernelFunc:nae},uk;function rae(e){uk=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function aae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=D.computeConv2DInfo(r.shape,a.shape,l,c,u,p),g=od[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);b=Q.id}let v=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,C=m.padInfo.right,_=m.padInfo.bottom,O=m.padInfo.left,E=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,P=m.strideWidth,V=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(te.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return uk(A,q,X,ee,y,v,k,b,S,C,_,O,j,E,R,T,P,V,x,g,se,f||0,ne),te}var oae={kernelName:Co,backendName:"wasm",setupFunc:rae,kernelFunc:aae},ck;function iae(e){ck=e.wasm.cwrap(To,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 lae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=D.computeConv2DInfo(r.shape,a.shape,l,c,u,p,!0),g=od[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);b=Q.id}let v=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,C=m.padInfo.right,_=m.padInfo.bottom,O=m.padInfo.left,E=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,P=m.strideWidth,V=m.inChannels,j=m.padInfo.type==="SAME"?1:0,q=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(te.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return ck(A,q,X,ee,y,v,k,b,S,C,_,O,j,E,R,T,P,V,x,g,se,f||0,ne),te}var uae={kernelName:To,backendName:"wasm",setupFunc:iae,kernelFunc:lae},dk;function cae(e){dk=e.wasm.cwrap(Hi,null,["number","number","number","number","number","number","array","number"])}function dae(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=fg.prepareAndValidate(s,r),u=t.makeOutput(a,s.dtype);if(o===0)return u;let c=r.shape,d=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),A=t.dataIdMap.get(u.dataId).id;return dk(h,Mn[s.dtype],m,o,d,i,g,A),u}var pae={kernelName:Hi,backendName:"wasm",setupFunc:cae,kernelFunc:dae},pk;function hae(e){pk=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function fae(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=w.parseAxisParam(o,r.shape)[0],u=D.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=zn({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),d=w.sizeFromShape(a.shape),p=zn({inputs:{x:a},attrs:{shape:[u.batchSize,d/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize],f=t.makeOutput(h,r.dtype);if(w.sizeFromShape(r.shape)===0)return f;let m=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(p.dataId).id,b=t.dataIdMap.get(f.dataId).id,v=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),k=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return pk(A,Mn[r.dtype],v,m,x,u.batchSize,k,b),t.disposeData(c.dataId),t.disposeData(p.dataId),f.shape=u.outputShape,f}var mae={kernelName:Ui,backendName:"wasm",setupFunc:hae,kernelFunc:fae},gae=!1,Aae=Nn(Gi,gae,"bool"),yae=!1,xae=Nn(ja,yae,"bool"),hk;function bae(e){hk=e.wasm.cwrap(Xa,null,["number","number","number"])}function vae(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,t.dtype);if(w.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;hk(r,n,o)}return a}var wae={kernelName:Xa,backendName:"wasm",setupFunc:bae,kernelFunc:vae},kae=!1,Iae=Nn(Ki,kae,"bool"),Sae=!1,Cae=Nn(Zi,Sae,"bool"),Tae=pn(Ka),Nae=!1,Eae=Nn(Ji,Nae,"bool"),fk;function Rae(e){fk=e.wasm.cwrap(Za,null,["number, number, number"])}function _ae(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;D.assertAxesAreInnerMostDims("max",d,f);let[m,g]=D.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;fk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Dae={kernelName:Za,backendName:"wasm",setupFunc:Rae,kernelFunc:_ae},Fae=!1,$ae=Nn(Ya,Fae),mk;function Oae(e){mk=e.wasm.cwrap(Ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pae(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=D.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,A=c.dilationHeight,y=c.dilationWidth,x=c.strideHeight,b=c.strideWidth,v=c.inChannels,k=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=s.makeOutput(c.outShape,"float32"),C=s.dataIdMap.get(S.dataId).id;return mk(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,b,v,k,C),S}var Mae={kernelName:Ja,backendName:"wasm",setupFunc:Oae,kernelFunc:Pae},gk;function zae(e){gk=e.wasm.cwrap(Qa,null,["number, number, number"])}function Lae(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t),f=d;if(h){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,l=b,f=D.getInnerMostAxes(f.length,u.shape.length))}D.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=D.computeOutAndReduceShapes(u.shape,f),A=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=Lf({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(y.dataId).id);let x=t.makeOutput(m,"float32");if(w.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;gk(l,A,b)}if(h&&t.disposeData(c.dataId),a){let b=D.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(y.dataId),x}var Bae={kernelName:Qa,backendName:"wasm",setupFunc:zae,kernelFunc:Lae},Ak;function Wae(e){Ak=e.wasm.cwrap(eo,null,["number, number, number"])}function Vae(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x)}let f=u.shape.length;D.assertAxesAreInnerMostDims("min",d,f);let[m,g]=D.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Ak(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Uae={kernelName:eo,backendName:"wasm",setupFunc:Wae,kernelFunc:Vae},Hae=!1,Gae=Nn(to,Hae),J2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(J2||(J2={}));var yk;function jae(e){yk=e.wasm.cwrap(no,null,["number","array","number","number","array","array","number","number"])}function qae(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(d).buffer);return yk(o,u,t.shape.length,Mn[t.dtype],p,h,J2[r],l),i}var Xae={kernelName:no,backendName:"wasm",kernelFunc:qae,setupFunc:jae},Kae=!0,Zae=Nn(so,Kae),Yae=pn(el);function Q2(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var xk;function Jae(e){xk=e.wasm.cwrap(nl,"number",["number","number","number","number","number"])}function Qae(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,d=xk(u,c,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Q2(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var eoe={kernelName:nl,backendName:"wasm",setupFunc:Jae,kernelFunc:Qae},bk;function toe(e){bk=e.wasm.cwrap(sl,"number",["number","number","number","number","number","bool"])}function noe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,p=bk(c,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Q2(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[A,y]}var soe={kernelName:sl,backendName:"wasm",setupFunc:toe,kernelFunc:noe},vk;function roe(e){vk=e.wasm.cwrap(rl,"number",["number","number","number","number","number","number"])}function aoe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(u.dataId).id,p=vk(c,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Q2(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[A,y]}var ooe={kernelName:rl,backendName:"wasm",setupFunc:roe,kernelFunc:aoe},ioe=!1,loe=Nn(tl,ioe,"bool"),wk;function uoe(e){wk=e.wasm.cwrap(ro,null,["number","number","number","number","number"])}function coe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return wk(d,a,o,i,u),l}var doe={kernelName:ro,backendName:"wasm",setupFunc:uoe,kernelFunc:coe};function poe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var hoe={kernelName:al,backendName:"wasm",kernelFunc:poe};function foe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Y2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=Y2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=Q4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),u}var moe={kernelName:ol,backendName:"wasm",kernelFunc:foe},kk;function goe(e){kk=e.wasm.cwrap(ao,null,["number","array","number","number","array","array","number","number"])}function Aoe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return ok({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return kk(o,c,t.shape.length,Mn[t.dtype],h,f,r,u),i}var Ik={kernelName:ao,backendName:"wasm",kernelFunc:Aoe,setupFunc:goe},yoe=!1,xoe=Nn(oo,yoe),Sk;function boe(e){Sk=e.wasm.cwrap(io,null,["number","number","number"])}function voe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=n.makeOutput(s.shape,"float32"),l=n.dataIdMap.get(i.dataId).id;return Sk(a,o,l),i}var woe={kernelName:io,backendName:"wasm",setupFunc:boe,kernelFunc:voe},Ck;function koe(e){Ck=e.wasm.cwrap(il,null,["number","number","number","number"])}function Ioe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t),f=d;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=D.getInnerMostAxes(f.length,u.shape.length))}D.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=D.computeOutAndReduceShapes(u.shape,f),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Ck(l,A,Mn[y.dtype],x)}if(h&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var Soe={kernelName:il,backendName:"wasm",setupFunc:koe,kernelFunc:Ioe},Coe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=v2(s,r,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},Toe={kernelName:Zu,backendName:"wasm",kernelFunc:Coe},Noe=!0,Eoe=Nn(Ba,Noe),Roe=pn(lo),_oe=pn(co),Tk;function Doe(e){Tk=e.wasm.cwrap(uo,null,["number","number","number","number","number","number","number","number","number","number"])}function Foe(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,[c,d,p,h]=r.shape,f=[c,l,u,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=Lf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let A=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let x=t.dataIdMap.get(y.dataId).id;return Tk(A,c,d,p,h,l,u,a?1:0,o?1:0,x),g!=null&&t.disposeData(g.dataId),y}var $oe={kernelName:uo,backendName:"wasm",setupFunc:Doe,kernelFunc:Foe},Nk;function Ooe(e){Nk=e.wasm.cwrap(po,null,["number","array","number","array","number","number"])}function Poe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return zf({inputs:{x:r},backend:n});let i=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(o).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);Nk(l,c,o.length,d,r.shape.length,u);let p=zn({inputs:{x:i},attrs:{shape:r.shape},backend:n});return n.disposeData(i.dataId),p}var Moe={kernelName:po,backendName:"wasm",kernelFunc:Poe,setupFunc:Ooe},Ek;function zoe(e){Ek=e.wasm.cwrap(Il,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:o,center:i}=s,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(l.dataId).id,[d,p,h,f]=r.shape,[m,g]=D.getImageCenter(i,p,h),A=o===0,y=255,x=typeof o=="number"?[o,o,o,A?0:y]:[...o,y],b=new Uint8Array(new Int32Array(x).buffer);return Ek(u,d,p,h,f,a,m,g,b,x.length,c),l}var Boe={kernelName:Il,backendName:"wasm",kernelFunc:Loe,setupFunc:zoe},Woe=pn(ho),Voe=pn(fo),Rk;function Uoe(e){Rk=e.wasm.cwrap(cl,null,["number","number","number","number","number","number","array","number","number"])}function Hoe(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:o}=s,i=t.makeOutput(o,a.dtype);if(w.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:p}=mg.calculateShapes(a,r,o),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,A=new Uint8Array(new Int32Array(d).buffer),y=t.dataIdMap.get(i.dataId).id;return Rk(f,g,Mn[a.dtype],l,u,c,A,p,y),i}var Goe={kernelName:cl,backendName:"wasm",setupFunc:Uoe,kernelFunc:Hoe},_k;function joe(e){_k=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function qoe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(a.dataId).id,u=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(u.dataId).id,d=s.shape.length,p=r.shape.length,h=d===0||d>1||p===1?1:w.sizeFromShape(r.shape.slice(1));return _k(o,i,l,h,c),u}var Xoe={kernelName:dl,backendName:"wasm",kernelFunc:qoe,setupFunc:joe},Dk;function Koe(e){Dk=e.wasm.cwrap(go,null,["number","number"])}function Zoe(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||Dk(s,a),r}var Yoe={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Koe,kernelFunc:Zoe},Joe=pn(mo),Fk;function Qoe(e){Fk=e.wasm.cwrap(xo,null,["number","number","number","number"])}function eie(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[s],l=w.sizeFromShape(n.shape)/i;return w.sizeFromShape(a.shape)===0||Fk(r,o,i,l),a}var tie={kernelName:xo,backendName:"wasm",setupFunc:Qoe,kernelFunc:eie};function nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=w.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=Ik.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=D.getReshaped(u.shape,a,i,!1),d=D.getPermuted(c.length,a.length,!1),p=D.getReshapedPermuted(u.shape,a,i,!1),m=zn({inputs:{x:u},backend:n,attrs:{shape:c}}),y=mu({inputs:{x:m},backend:n,attrs:{perm:d}}),v=zn({inputs:{x:y},backend:n,attrs:{shape:p}});return n.disposeData(u.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),v}var sie={kernelName:Al,backendName:"wasm",kernelFunc:nie};function rie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=w.parseAxisParam(o,r.shape)[0],l=D.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let p=[...c];p[i]=d;let h=id({inputs:{x:r},attrs:{begin:u,size:p},backend:s});return u[i]+=d,h})}var aie={kernelName:yl,backendName:"wasm",kernelFunc:rie},oie=pn(Ao),iie=pn(Ju),lie=!0,uie=Nn(bo,lie),$k;function cie(e){$k=e.wasm.cwrap(Hr,null,["number","number","number"])}function die(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),l=t.dataIdMap.get(i.dataId).id;return $k(o,r,l),i}var pie={kernelName:Hr,backendName:"wasm",setupFunc:cie,kernelFunc:die},Ok;function hie(e){Ok=e.wasm.cwrap(xl,null,["number","array","number","array","array","array","array","array","number","number"])}function fie(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i}=s;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s,h=D.slice_util.maskToAxes(c);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&p!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.shape.length-a.length,m=D.slice_util.maskToAxes(d),g=r.shape.slice();m.forEach(E=>{a[E]=0,o[E]=1,g.splice(E,0,1)});let A=zn({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:y,end:x,strides:b}=D.slice_util.getNormalizedAxes(A.shape,h,f,a,o,i,l,u,c);a=y,o=x,i=b;let v=D.slice_util.maskToAxes(p);v.forEach(E=>{o[E]=a[E]+1,i[E]=1});let k=D.slice_util.computeOutShape(a,o,i),S=k.filter((E,R)=>v.indexOf(R)===-1);if(i.every(E=>E===1)){let E=id({inputs:{x:A},attrs:{begin:a,size:k},backend:t});t.disposeData(A.dataId);let R=zn({inputs:{x:E},attrs:{shape:S},backend:t});return t.disposeData(E.dataId),R}let _=t.makeOutput(S,"float32");if(!S.some(E=>E===0)){let E=t.dataIdMap.get(A.dataId).id,R=new Uint8Array(new Int32Array(w.computeStrides(A.shape)).buffer),T=new Uint8Array(new Int32Array(a).buffer),P=new Uint8Array(new Int32Array(o).buffer),V=new Uint8Array(new Int32Array(i).buffer),j=new Uint8Array(new Int32Array(S).buffer),q=new Uint8Array(new Int32Array(w.computeStrides(S)).buffer),X=t.dataIdMap.get(_.dataId).id;Ok(E,R,A.shape.length,T,P,V,j,q,S.length,X)}t.disposeData(A.dataId);let O=zn({inputs:{x:_},attrs:{shape:S},backend:t});return t.disposeData(_.dataId),O}var mie={kernelName:xl,backendName:"wasm",setupFunc:hie,kernelFunc:fie},gie=!0,Aie=Nn(vo,gie),Pk;function yie(e){Pk=e.wasm.cwrap(yo,null,["number, number, number"])}function xie(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=fa(o,r,t),f=d;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(u=c,l=x,f=D.getInnerMostAxes(f.length,u.shape.length))}D.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,g]=D.computeOutAndReduceShapes(u.shape,f),A=w.sizeFromShape(g),y=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Pk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=D.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var bie={kernelName:yo,backendName:"wasm",setupFunc:yie,kernelFunc:xie},vie=pn(wo),wie=pn(ko),Mk;function kie(e){Mk=e.wasm.cwrap(Ur,null,["number","array","number","array","number","number"])}function Iie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,a=n.dataIdMap.get(r.dataId).id,{reps:o}=s,i=new Array(r.shape.length);for(let p=0;p<i.length;p++)i[p]=r.shape[p]*o[p];let l=new Uint8Array(new Int32Array(r.shape).buffer),u=new Uint8Array(new Int32Array(i).buffer),c=n.makeOutput(i,r.dtype),d=n.dataIdMap.get(c.dataId).id;return Mk(a,l,r.shape.length,u,i.length,Mn[c.dtype],d),c}var Sie={kernelName:Ur,backendName:"wasm",setupFunc:kie,kernelFunc:Iie},zk;function Cie(e){zk=e.wasm.cwrap(bl,null,["number","array","number","number","number","bool","number","number"])}var Tie=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{k:r,sorted:a}=n,o=t.dataIdMap.get(s.dataId).id,i=new Uint8Array(new Int32Array(s.shape).buffer),l=s.shape.slice();l[l.length-1]=r;let u=t.makeOutput(l,s.dtype),c=t.dataIdMap.get(u.dataId).id,d=t.makeOutput(l,"int32"),p=t.dataIdMap.get(d.dataId).id;return zk(o,i,s.shape.length,Mn[s.dtype],r,a,c,p),[u,d]},Nie={kernelName:bl,backendName:"wasm",setupFunc:Cie,kernelFunc:Tie},Lk;function Eie(e){Lk=e.wasm.cwrap(vl,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Rie(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,p,h]=r.shape,[f,m]=u!=null?u:[d,p],g=[c,f,m,h],A=new Uint8Array(new 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n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(ma(e[r],Xie(t,a)))}return n}function sy(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=qk(t[0],t[1]),o=Xk(a,r),i=qk(-t[0],-t[1]);return Xk(o,i)}function Kk(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-ma(t[0],n),-ma(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function Zk(e,t){return[ma(e,t[0]),ma(e,t[1])]}function Yk(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let u=r*(l+.5);for(let c=0;c<o;c++){let d=r*(c+.5);for(let p=0;p<i;p++)n.push([d,u])}}}return n}var Jk=6;function Kie(e,t,n){let s=De(e,[0,1],[-1,2]),r=ae(s,t),a=De(e,[0,3],[-1,2]),o=de(a,n),i=de(r,n),l=de(o,2),u=ge(i,l),c=ae(i,l),d=L(u,n),p=L(c,n);return Ml([d,p],1)}var 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r,a;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=s,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2]),this.irisSize=(s==null?void 0:s.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,s,r){let a=dd({startPoint:n.startPoint,endPoint:n.endPoint}),o=t.map(d=>[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=s!==0?sy(s,[0,0]):Uf,l=s!==0?o.map(d=>[...Zk(d,i),d[2]]):o,u=s!==0?Kk(r):Uf,c=[...pd({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+ma(c,u[0])),Math.round(d[1]+ma(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[gu.leftBounds[0]][2],s=t[gu.rightBounds[0]][2];return n-s}getEyeBox(t,n,s,r,a=!1){let 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l===2?u=r:l===4&&(u=a),[i[0],i[1],u]})}correctFaceRotation(t,n,s){let[r,a]=n.landmarks.length>=t8.count?t8.symmetryLine:Qie.symmetryLine,o=jk(n.landmarks[r],n.landmarks[a]),i=pd({startPoint:n.startPoint,endPoint:n.endPoint}),l=[i[0]/s.shape[2],i[1]/s.shape[1]],u=_e.rotateWithOffset(s,o,0,l),c=sy(-o,i),d=t.face.mesh.enabled?hd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):hd({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),p=de(d,255);return Z(d),Z(u),[o,c,p]}async augmentIris(t,n){let{box:s,boxSize:r,crop:a}=this.getEyeBox(t,n,gu.leftBounds[0],gu.leftBounds[1],!0),{box:o,boxSize:i,crop:l}=this.getEyeBox(t,n,gu.rightBounds[0],gu.rightBounds[1]),u=gt([a,l]);Z(a),Z(l);let c=this.irisModel.predict(u);Z(u);let d=await c.data();Z(c);let 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ele=["angry","disgust","fear","happy","sad","surprise","neutral"],qs,qf=[],i8=0,my=Number.MAX_SAFE_INTEGER,gy=[.2989,.587,.114];async function Ay(e){var t,n;return qs?e.debug&&ue("cached model:",qs.modelUrl):(qs=await At(yt(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!qs||!qs.modelUrl?ue("load model failed:",((n=e.face.emotion)==null?void 0:n.modelPath)||""):e.debug&&ue("load model:",qs.modelUrl)),qs}async function yy(e,t,n,s){var r;return qs?my<(((r=t.face.emotion)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&i8===s&&qf[n]&&qf[n].length>0?(my++,qf[n]):(my=0,new Promise(async a=>{var g,A;let o=_e.resizeBilinear(e,[qs.inputs[0].shape[2],qs.inputs[0].shape[1]],!1),[i,l,u]=Xt(o,3,3);Z(o);let c=L(i,gy[0]),d=L(l,gy[1]),p=L(u,gy[2]);Z(i),Z(l),Z(u);let h=qp([c,d,p]);Z(c),Z(d),Z(p);let f=H(()=>L(ge(h,.5),2));Z(h);let m=[];if((g=t.face.emotion)==null?void 0:g.enabled){let y=await qs.predict(f),x=await y.data();Z(y);for(let 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Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function by(e,t,n,s){return{y:s.get(e,t,n),x:s.get(e,t,n+l8)}}function vy(e,t,n){let{heatmapY:s,heatmapX:r,id:a}=e,{y:o,x:i}=by(s,r,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function wy(e,t,n){return e<t?t:e>n?n:e}function p8(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function ky(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Xf=1,yu=16,sle=50**2;function h8(e,t,n,s,r,a,o=2){let i=A=>({y:a.get(A.y,A.x,e),x:a.get(A.y,A.x,a.shape[2]/2+e)}),l=(A,y,x)=>({y:wy(Math.round(A.y/yu),0,y-1),x:wy(Math.round(A.x/yu),0,x-1)}),[u,c]=s.shape,d=l(t.position,u,c),p=i(d),f=ky(t.position,p);for(let A=0;A<o;A++){let y=l(f,u,c),x=by(y.y,y.x,n,r);f=ky({x:y.x*yu,y:y.y*yu},{x:x.x,y:x.y})}let m=l(f,u,c),g=s.get(m.y,m.x,n);return{position:f,part:md[n],score:g}}function rle(e,t,n,s,r){let a=u8.map(([p,h])=>[gd[p],gd[h]]),o=a.map(([,p])=>p),i=a.map(([p])=>p),l=t.shape[2],u=o.length,c=new Array(l),d=vy(e.part,yu,n);c[e.part.id]={score:e.score,part:md[e.part.id],position:d};for(let p=u-1;p>=0;--p){let 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s={};s.batched=this.model.predict(t),s.predictions=ct(s.batched),s.scores=H(()=>ct(Gn(De(s.predictions,[0,0],[-1,1]))));let r=await s.scores.data();s.boxes=De(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await _e.nonMaxSuppressionAsync(s.norm,s.scores,10*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=De(s.norm,[i,0],[1,-1]),u=H(()=>U(this.normalizeLandmarks(De(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:u,confidence:r[i]})}for(let i of Object.keys(s))Z(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=H(()=>ge(de(_e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);Z(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let u=await l.box.data(),c=u.slice(0,2),d=u.slice(2,4),p=await l.palmLandmarks.array();Z(l.box),Z(l.palmLandmarks),i.push(A8({startPoint:c,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function ule(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function x8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ule(n)}var b8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ga(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function cle(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function v8(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(ga(e[r],cle(t,a)))}return n}function Ty(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=b8(t[0],t[1]),o=v8(a,r),i=b8(-t[0],-t[1]);return v8(o,i)}function w8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-ga(t[0],n),-ga(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function Ny(e,t){return[ga(e,t[0]),ga(e,t[1])]}var 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`),d.brightness=function(v){let k=(v||0)+1;d.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},d.saturation=function(v){let k=(v||0)*2/3+1,S=(k-1)*-.5;d.colorMatrix([k,S,S,0,0,S,k,S,0,0,S,S,k,0,0,0,0,0,1,0])},d.desaturate=function(){d.saturation(-1)},d.contrast=function(v){let k=(v||0)+1,S=-128*(k-1);d.colorMatrix([k,0,0,0,S,0,k,0,0,S,0,0,k,0,S,0,0,0,1,0])},d.negative=function(){d.contrast(-2)},d.hue=function(v){v=(v||0)/180*Math.PI;let k=Math.cos(v),S=Math.sin(v),C=.213,_=.715,O=.072;d.colorMatrix([C+k*(1-C)+S*-C,_+k*-_+S*-_,O+k*-O+S*(1-O),0,0,C+k*-C+S*.143,_+k*(1-_)+S*.14,O+k*-O+S*-.283,0,0,C+k*-C+S*-(1-C),_+k*-_+S*_,O+k*(1-O)+S*O,0,0,0,0,0,1,0])},d.desaturateLuminance=function(){d.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},d.sepia=function(){d.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},d.brownie=function(){d.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},d.vintagePinhole=function(){d.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},d.kodachrome=function(){d.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},d.technicolor=function(){d.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},d.polaroid=function(){d.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},d.shiftToBGR=function(){d.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},d.convolution=function(v){let k=new Float32Array(v),S=1/i,C=1/l,_=b(d.convolution.SHADER);m.uniform1fv(_.uniform.m,k),m.uniform2f(_.uniform.px,S,C),x()},d.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
|
|
`),d.detectEdges=function(){d.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},d.sobelX=function(){d.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},d.sobelY=function(){d.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},d.sharpen=function(v){let k=v||1;d.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},d.emboss=function(v){let k=v||1;d.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},d.blur=function(v){let k=v/7/i,S=v/7/l,C=b(d.blur.SHADER);m.uniform2f(C.uniform.px,0,S),x(f.INTERMEDIATE),m.uniform2f(C.uniform.px,k,0),x()},d.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
|
|
`),d.pixelate=function(v){let k=v/i,S=v/l,C=b(d.pixelate.SHADER);m.uniform2f(C.uniform.size,k,S),x()},d.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
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|
`)}var s0=2048,Oe,Ct,Ut;function bu(e,t){let n;if(we.browser?typeof OffscreenCanvas!="undefined"?n=new OffscreenCanvas(e,t):(n=document.createElement("canvas"),n.width=e,n.height=t):n=typeof we.Canvas!="undefined"?new we.Canvas(e,t):null,!n)throw new Error("Human: Cannot create canvas");return n}function li(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof qe)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof we.Canvas!="undefined"&&e instanceof we.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof qe)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=$s(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!r)return{tensor:null,canvas:Oe};let a=s,o=r;if(a>s0&&(a=s0,o=a*r/s),o>s0&&(o=s0,a=o*s/r),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("Human: Input cannot determine dimension");(!Oe||(Oe==null?void 0:Oe.width)!==a||(Oe==null?void 0:Oe.height)!==o)&&(Oe=bu(a,o));let i=Oe.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),t.filter.enabled){if((!Ut||!Ct||Oe.width!==Ct.width||(Oe==null?void 0:Oe.height)!==(Ct==null?void 0:Ct.height))&&(Ct=bu(Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),(Ct==null?void 0:Ct.width)!==(Oe==null?void 0:Oe.width)&&(Ct.width=Oe==null?void 0:Oe.width),(Ct==null?void 0:Ct.height)!==(Oe==null?void 0:Oe.height)&&(Ct.height=Oe==null?void 0:Oe.height),Ut=we.browser?new L8({canvas:Ct}):null),!Ut)return{tensor:null,canvas:Oe};Ut.reset(),Ut.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Ut.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ut.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ut.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Ut.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Ut.addFilter("hue",t.filter.hue),t.filter.negative&&Ut.addFilter("negative"),t.filter.sepia&&Ut.addFilter("sepia"),t.filter.vintage&&Ut.addFilter("brownie"),t.filter.sepia&&Ut.addFilter("sepia"),t.filter.kodachrome&&Ut.addFilter("kodachrome"),t.filter.technicolor&&Ut.addFilter("technicolor"),t.filter.polaroid&&Ut.addFilter("polaroid"),t.filter.pixelate!==0&&Ut.addFilter("pixelate",t.filter.pixelate),Ut.apply(Oe)}else Ct=Oe,Ut&&(Ut=null);if(!n){let l;if(Ct.data){let u=[Ct.height,Ct.width,3];l=Vp(Ct.data,u,"int32")}else if(typeof ImageData!="undefined"&&Ct instanceof ImageData)l=is?is.fromPixels(Ct):null;else if(t.backend==="webgl"||t.backend==="humangl"){let u=bu(a,o);u.width=a,u.height=o;let c=u.getContext("2d");c==null||c.drawImage(Ct,0,0),l=is&&we.browser?is.fromPixels(u):null}else{let u=bu(a,o);u.width=a,u.height=o;let c=u.getContext("2d");c.drawImage(Ct,0,0);let d=c.getImageData(0,0,a,o);is&&we.browser?l=is.fromPixels(d):l=H(()=>{let p=tn(Array.from(d.data),[a,o,4]),h=Xt(p,4,2),f=gn([h[0],h[1],h[2]],2);return U(f,[p.shape[0],p.shape[1],3])})}if(l){let u=ce(l,"float32");n=zt(u,0),Z(l),Z(u)}else throw n=$t([1,a,o,3]),new Error("Human: Cannot create tensor from input")}}return{tensor:n,canvas:t.filter.return?Ct:null}}var Xy=0,B8=1;async function W8(e,t){if(e.cacheSensitivity===0)return!1;let n=32;if(!t.shape[1]||!t.shape[2])return!1;let s=_e.resizeBilinear(t,[Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=await s.data();Z(s);let a=0;for(let l=0;l<r.length/3;l++)a+=r[3*l+2];let o=100*(Math.max(a,Xy)/Math.min(a,Xy)-1);Xy=a;let i=o<Math.max(e.cacheSensitivity,B8);return B8=o>10*e.cacheSensitivity?0:o,i}var vs,Ky=!1;async function r0(e){return vs?e.debug&&ue("cached model:",vs.modelUrl):(vs=await At(yt(e.modelBasePath,e.segmentation.modelPath||"")),!vs||!vs.modelUrl?ue("load model failed:",e.segmentation.modelPath):e.debug&&ue("load model:",vs.modelUrl)),vs}async function Zy(e){var f,m;let t=((f=e.tensor)==null?void 0:f.shape[1])||0,n=((m=e.tensor)==null?void 0:m.shape[2])||0;if(!e.tensor||!vs||!vs.inputs[0].shape)return null;let s=_e.resizeBilinear(e.tensor,[vs.inputs[0].shape[1],vs.inputs[0].shape[2]],!1),r=de(s,255),a=vs.predict(r);Z(s),Z(r);let o=ct(a,0);Z(a);let i;if(o.shape[2]===2){let g=o.softmax(),[A,y]=fs(g,2),x=zt(y,2),b=zt(x,0);Z(g),Z(A),Z(y);let v=_e.cropAndResize(b,[[0,0,.5,.5]],[0],[t,n]);i=ct(v,0),Z(v),Z(x),Z(b)}else i=_e.resizeBilinear(o,[t,n]);if(Z(o),we.node){let g=await i.data();return Z(i),g}let l=typeof OffscreenCanvas!="undefined"?new 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Promise.all([e.models.face||(e.config.face.enabled?uy(e.config):null),e.models.emotion||(e.config.face.enabled&&e.config.face.emotion.enabled?Ay(e.config):null),e.models.handpose||(e.config.hand.enabled?Dy(e.config):null),e.models.posenet||(e.config.body.enabled&&e.config.body.modelPath.includes("posenet")?Sy(e.config):null),e.models.blazepose||(e.config.body.enabled&&e.config.body.modelPath.includes("blazepose")?Jf(e.config):null),e.models.efficientpose||(e.config.body.enabled&&e.config.body.modelPath.includes("efficientpose")?M8(e.config):null),e.models.movenet||(e.config.body.enabled&&e.config.body.modelPath.includes("movenet")?By(e.config):null),e.models.nanodet||(e.config.object.enabled&&e.config.object.modelPath.includes("nanodet")?Uy(e.config):null),e.models.centernet||(e.config.object.enabled&&e.config.object.modelPath.includes("centernet")?jy(e.config):null),e.models.faceres||(e.config.face.enabled&&e.config.face.description.enabled?dy(e.config):null),e.models.segmentation||(e.config.segmentation.enabled?r0(e.config):null)]):(e.config.face.enabled&&!e.models.face&&(e.models.face=await uy(e.config)),e.config.face.enabled&&e.config.face.emotion.enabled&&!e.models.emotion&&(e.models.emotion=await Ay(e.config)),e.config.hand.enabled&&!e.models.handpose&&(e.models.handpose=await Dy(e.config)),e.config.body.enabled&&!e.models.posenet&&e.config.body.modelPath.includes("posenet")&&(e.models.posenet=await Sy(e.config)),e.config.body.enabled&&!e.models.blazepose&&e.config.body.modelPath.includes("blazepose")&&(e.models.blazepose=await Jf(e.config)),e.config.body.enabled&&!e.models.efficientpose&&e.config.body.modelPath.includes("efficientpose")&&(e.models.efficientpose=await Jf(e.config)),e.config.body.enabled&&!e.models.movenet&&e.config.body.modelPath.includes("movenet")&&(e.models.movenet=await By(e.config)),e.config.object.enabled&&!e.models.nanodet&&e.config.object.modelPath.includes("nanodet")&&(e.models.nanodet=await Uy(e.config)),e.config.object.enabled&&!e.models.centernet&&e.config.object.modelPath.includes("centernet")&&(e.models.centernet=await jy(e.config)),e.config.face.enabled&&e.config.face.description.enabled&&!e.models.faceres&&(e.models.faceres=await dy(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=await r0(e.config)))}async function H8(e){let t=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"];for(let n of Object.keys(e.models))if(e.models[n]){let s=[];Array.isArray(e.models[n])?s=e.models[n].map(r=>r.executor?r:r.model):s=[e.models[n]];for(let r of s){let a=[],o=r==null?void 0:r.executor;if(o)for(let l of Object.values(o.graph.nodes)){let u=l.op.toLowerCase();a.includes(u)||a.push(u)}let i=[];for(let l of a)!t.includes(l)&&!e.env.kernels.includes(l)&&!e.env.kernels.includes(l.replace("_",""))&&!e.env.kernels.includes(l.replace("native",""))&&!e.env.kernels.includes(l.replace("v2",""))&&i.push(l);!o&&e.config.debug&&ue("model executor not found:",n),i.length>0&&e.config.debug&&ue("model validation:",n,i)}}}var Sle=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],u=Math.sqrt(l[0]**2+l[1]**2);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},Cle=(e,t)=>{let n=g=>{let A=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=A,g[1]/=A,g[2]/=A,g},s=(g,A)=>{let y=g[0]-A[0],x=g[1]-A[1],b=g[2]-A[2];return[y,x,b]},r=(g,A)=>{let y=g[1]*A[2]-g[2]*A[1],x=g[2]*A[0]-g[0]*A[2],b=g[0]*A[1]-g[1]*A[0];return[y,x,b]},a=g=>{let[A,y,x,b,v,k,S,C,_]=g,O,E,R;return b<1?b>-1?(R=Math.asin(b),E=Math.atan2(-S,A),O=Math.atan2(-k,v)):(R=-Math.PI/2,E=-Math.atan2(C,_),O=0):(R=Math.PI/2,E=Math.atan2(C,_),O=0),isNaN(O)&&(O=0),isNaN(E)&&(E=0),isNaN(R)&&(R=0),{pitch:2*-O,yaw:2*-E,roll:2*-R}},o=g=>{let A=(x,b,v,k)=>Math.atan2(k-b,v-x);return{pitch:A(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:A(g[33][0],g[33][2],g[263][0],g[263][2]),roll:A(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),c=n(s(u[1],u[0])),d=n(s(u[3],u[2])),p=n(r(d,c));d=r(c,p);let h=[d[0],d[1],d[2],c[0],c[1],c[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?Sle(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}},Yy=async(e,t)=>{var d,p,h,f,m,g;let n,s,r,a,o,i,l,u=[];e.state="run:face",n=Ke();let c=await n8(t,e.config);if(e.performance.face=Math.trunc(Ke()-n),!t.shape||t.shape.length!==4)return[];if(!c)return[];for(let A=0;A<c.length;A++){if(e.analyze("Get Face"),!c[A].tensor||c[A].tensor.isDisposedInternal){ue("Face object is disposed:",c[A].tensor);continue}let y=Cle(c[A],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?yy(c[A].tensor||tn([]),e.config,A,c.length):{}:(e.state="run:emotion",n=Ke(),o=e.config.face.emotion.enabled?await yy(c[A].tensor||tn([]),e.config,A,c.length):{},e.performance.emotion=Math.trunc(Ke()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?l=e.config.face.description.enabled?fy(c[A].tensor||tn([]),e.config,A,c.length):[]:(e.state="run:description",n=Ke(),l=e.config.face.description.enabled?await fy(c[A].tensor||tn([]),e.config,A,c.length):[],e.performance.embedding=Math.trunc(Ke()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,r]=await Promise.all([s,a,o,i,l,r])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=c[A])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((f=(h=c[A])==null?void 0:h.annotations)==null?void 0:f.rightEyeIris)&&(delete c[A].annotations.leftEyeIris,delete c[A].annotations.rightEyeIris);let x=((m=c[A].annotations)==null?void 0:m.leftEyeIris)&&((g=c[A].annotations)==null?void 0:g.rightEyeIris)?Math.max(Math.abs(c[A].annotations.leftEyeIris[3][0]-c[A].annotations.leftEyeIris[1][0]),Math.abs(c[A].annotations.rightEyeIris[4][1]-c[A].annotations.rightEyeIris[2][1]))/t.shape[2]:0,b=e.config.face.detector.return?ct(c[A].tensor):null;Z(c[A].tensor),c[A].tensor&&delete c[A].tensor,u.push({...c[A],id:A,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:x!==0?Math.trunc(500/x/11.7)/100:0,rotation:y,tensor:b}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var G8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position.y<a.position.y&&r.position.y<a.position.y?t.push({body:n,gesture:"i give up"}):a&&s&&s.position.y<a.position.y?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position.y<a.position.y&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position.y>i.position.y?"left":"right"}`})}return t},j8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},q8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(u=!1),p>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(u=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},X8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];for(let[a,o]of Object.entries(e[n].annotations))a!=="palmBase"&&Array.isArray(o)&&s.push({name:a.toLowerCase(),position:o[0]});if(s&&s.length>0){let a=s.reduce((i,l)=>i.position[2]<l.position[2]?i:l);t.push({hand:n,gesture:`${a.name} forward`});let o=s.reduce((i,l)=>i.position[1]<l.position[1]?i:l);t.push({hand:n,gesture:`${o.name} up`})}let r=D8(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}return t};var ex={};Mm(ex,{all:()=>Ele,body:()=>Y8,canvas:()=>Nle,face:()=>Z8,gesture:()=>K8,hand:()=>J8,object:()=>Q8,options:()=>ba,person:()=>Tle});var ba={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe 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2Q==`;async function Dle(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(o0);break;case"full":n=await t(i0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function Fle(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+o0;break;case"full":case"body":n="data:image/jpeg;base64,"+i0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:we.Image&&(s=new we.Image),s.onload=async()=>{let r=bu(s.naturalWidth,s.naturalHeight);if(!r)ue("Warmup: Canvas not found"),t({});else{r.getContext("2d").drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function $le(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(o0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(i0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ue("Warmup tfjs-node not loaded");return s}async function sI(e,t){let n=Ke();if(t&&(e.config=hn(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;typeof createImageBitmap=="function"?s=await Dle(e):typeof Image!="undefined"||we.Canvas!==void 0?s=await Fle(e):s=await $le(e);let r=Ke();return e.config.debug&&ue("Warmup",e.config.warmup,Math.round(r-n),"ms"),e.emit("warmup"),s}var vu,vd,wd,l0,aI=class{constructor(t){Ee(this,"version");Ee(this,"config");Ee(this,"result");Ee(this,"state");Ee(this,"process");Ee(this,"tf");Ee(this,"env");Ee(this,"draw");Ee(this,"models");Ee(this,"events");Ee(this,"faceTriangulation");Ee(this,"faceUVMap");Ee(this,"performance");$u(this,vu,void 0);$u(this,vd,void 0);$u(this,wd,void 0);Ee(this,"initial");Ee(this,"analyze",(...t)=>{if(!Fu(this,vd))return;let n=this.tf.engine().state.numTensors,s=Fu(this,vu);Ou(this,vu,n);let r=n-s;r!==0&&ue(...t,r)});$u(this,l0,t=>{if(!Fu(this,wd))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof qe))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Ee(this,"image",t=>li(t,this.config));Ee(this,"emit",t=>{var n;return(n=this.events)==null?void 0:n.dispatchEvent(new Event(t))});Ee(this,"next",t=>tI(t||this.result));Ee(this,"warmup",t=>sI(this,t));Hf(),this.env=we,bi.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Hp}/dist/`,bi.modelBasePath=this.env.browser?"../models/":"file://models/",bi.backend=this.env.browser?"humangl":"tensorflow",this.version=nx,Object.defineProperty(this,"version",{value:nx}),this.config=hn(bi,t||{}),this.tf=ai,this.draw=ex,this.state="idle",Ou(this,vu,0),Ou(this,vd,!1),Ou(this,wd,!1),this.initial=!0,this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=new EventTarget,this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null,segmentation:null},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=s8,this.faceUVMap=r8,this.emit("create")}similarity(t,n){return py(t,n)}segmentation(t,n){return t?V8(t,n,this.config):null}enhance(t){return hy(t)}match(t,n,s=0){return o8(t,n,s)}async load(t){this.state="load";let n=Ke(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=hn(this.config,t)),this.initial&&(this.config.debug&&ue(`version: ${this.version}`),this.config.debug&&ue(`tfjs version: ${this.tf.version_core}`),await tx(this),await jp(),this.env.browser&&(this.config.debug&&ue("configuration:",this.config),this.config.debug&&ue("tf flags:",this.tf.ENV.flags))),await U8(this),this.initial&&this.config.debug&&ue("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await H8(this),this.emit("load"));let a=Math.trunc(Ke()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async s=>{var m,g,A,y,x,b,v,k,S,C,_,O,E,R;this.state="config";let r,a;this.config=hn(this.config,n),this.state="check";let o=Fu(this,l0).call(this,t);o&&(ue(o,t),s({error:o}));let i=Ke();await tx(this),await this.load(),r=Ke();let l=li(t,this.config);if(this.process=l,this.performance.image=Math.trunc(Ke()-r),this.analyze("Get Image:"),this.config.segmentation.enabled&&this.process&&l.tensor&&l.canvas&&(this.analyze("Start Segmentation:"),this.state="run:segmentation",r=Ke(),await Zy(l),a=Math.trunc(Ke()-r),a>0&&(this.performance.segmentation=a),l.canvas&&(Z(l.tensor),l=li(l.canvas,this.config)),this.analyze("End Segmentation:")),!l.tensor){ue("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=Ke(),this.config.skipFrame=await W8(this.config,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Ke()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],p=[];this.config.async?(u=this.config.face.enabled?Yy(this,l.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Ke(),u=this.config.face.enabled?await Yy(this,l.tensor):[],a=Math.trunc(Ke()-r),a>0&&(this.performance.face=a)),this.analyze("Start Body:"),this.config.async?(((m=this.config.body.modelPath)==null?void 0:m.includes("posenet"))?c=this.config.body.enabled?Iy(l.tensor,this.config):[]:((g=this.config.body.modelPath)==null?void 0:g.includes("blazepose"))?c=this.config.body.enabled?Fy(l.tensor,this.config):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("efficientpose"))?c=this.config.body.enabled?My(l.tensor,this.config):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("movenet"))&&(c=this.config.body.enabled?Wy(l.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Ke(),((x=this.config.body.modelPath)==null?void 0:x.includes("posenet"))?c=this.config.body.enabled?await Iy(l.tensor,this.config):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("blazepose"))?c=this.config.body.enabled?await Fy(l.tensor,this.config):[]:((v=this.config.body.modelPath)==null?void 0:v.includes("efficientpose"))?c=this.config.body.enabled?await My(l.tensor,this.config):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("movenet"))&&(c=this.config.body.enabled?await Wy(l.tensor,this.config):[]),a=Math.trunc(Ke()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?_y(l.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Ke(),d=this.config.hand.enabled?await _y(l.tensor,this.config):[],a=Math.trunc(Ke()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(((S=this.config.object.modelPath)==null?void 0:S.includes("nanodet"))?p=this.config.object.enabled?Hy(l.tensor,this.config):[]:((C=this.config.object.modelPath)==null?void 0:C.includes("centernet"))&&(p=this.config.object.enabled?qy(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Ke(),((_=this.config.object.modelPath)==null?void 0:_.includes("nanodet"))?p=this.config.object.enabled?await Hy(l.tensor,this.config):[]:((O=this.config.object.modelPath)==null?void 0:O.includes("centernet"))&&(p=this.config.object.enabled?await qy(l.tensor,this.config):[]),a=Math.trunc(Ke()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.config.async&&([u,c,d,p]=await Promise.all([u,c,d,p]));let h=[];this.config.gesture.enabled&&(r=Ke(),h=[...j8(u),...G8(c),...X8(d),...q8(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Ke()-r)),this.performance.total=Math.trunc(Ke()-i),this.state="idle";let f=((R=(E=this.process)==null?void 0:E.tensor)==null?void 0:R.shape)||[];this.result={face:u,body:c,hand:d,gesture:h,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return eI(u,c,d,h,f)}},Z(l.tensor),this.emit("detect"),s(this.result)})}};vu=new WeakMap,vd=new WeakMap,wd=new WeakMap,l0=new WeakMap;return Ole;})();
|
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/**
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* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* https://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|