mirror of https://github.com/vladmandic/human
5169 lines
1.3 MiB
5169 lines
1.3 MiB
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/*
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Human library
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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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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:a,asyncInit:r}=this.initializeBackend(n);if(r||a)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),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),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 a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Lu.nextTensorId++}nextVariableId(){return Lu.nextVariableId++}clone(e){let t=D.runKernel(Is,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return D.runKernel(ps,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(ic(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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Rm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Rm(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=ic(h,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,g);let x=g.map(w=>{if(w.rank!=null)return w;let{dataId:b,shape:v,dtype:N}=w;return this.makeTensorFromDataId(b,v,N)});if(a){let w=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(w)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,A),A}}let{inputs:d,attrs:u}=e,p=Rm(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,d,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,d,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(d).map(h=>d[h]!=null?d[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=wm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,d)=>s[d]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&vr(e[0])&&(r=e.map(o=>Du(o)));let s=a.write(r,t,n),i=new Le(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=kx(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Le(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Pu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*mm(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 Pu||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*mm(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=wm(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((d,u)=>{if(d==null){let p=n[u],c=Tp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return d}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Im(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=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===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(F(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));F(r instanceof Le,()=>"The result y returned by f() must be a tensor.");let s=ZI(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?oS(r.shape):n,YI(i,s,l=>this.tidy(l),lS);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let d of l.saved)d.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return F(wr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof Le),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,a={};t.forEach((i,o)=>{a[o]=i});let r=(i,o)=>(n=e(...t,o),F(n.value instanceof Le,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(wr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),d=Array.isArray(l)?l:[l];F(d.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),F(d.every(p=>p instanceof Le),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let u={};return d.forEach((p,c)=>{u[c]=()=>p}),u};return this.runKernelFunc({forwardFunc:r,backwardsFunc:s,inputs:a})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=$u(),n=await this.backend.time(e);return n.wallMs=$u()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new _x;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Lu.nextTensorId=0;Lu.nextVariableId=0;function oS(e){let t=Am(Tt(e),"float32");return D.makeTensor(t,e,"float32")}function Px(){let e=Cx();if(e._tfengine==null){let t=new Ex(e);e._tfengine=new Lu(t)}return PI(e._tfengine.ENV),tS(()=>e._tfengine),e._tfengine}var D=Px();function lS(e,t){let n={a:e,b:t};return D.runKernel(kr,n)}var Wu={};Fe(Wu,{isBrowser:()=>Lx,isMobile:()=>uS});function dS(){return typeof navigator!="undefined"&&navigator!=null}function uS(e){if(e||dS()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function Lx(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ga=J();ga.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. 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Actual: ${r}.
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f=dT([t,n,a,1],o,1,r,e,u);c=f[0],h=f[1],m=f[2]}else if(e==="same"){c=Math.ceil(t/r),h=Math.ceil(n/s),m=Math.ceil(a/i);let f=(c-1)*r+o-t,A=(h-1)*s+l-n,y=(m-1)*i+d-a,g=Math.floor(f/2),x=f-g,w=Math.floor(A/2),b=A-w,v=Math.floor(y/2),N=y-v;p={top:w,bottom:b,left:v,right:N,front:g,back:x,type:"SAME"}}else if(e==="valid")p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},c=Math.ceil((t-o+1)/r),h=Math.ceil((n-l+1)/s),m=Math.ceil((a-d+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:c,outHeight:h,outWidth:m}}function di(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error(`Unknown roundingMode ${t}`)}}function Mr(e){let[t,n,a]=xc(e);return t===1&&n===1&&a===1}function za(e,t){return Mr(e)||Mr(t)}function Ib(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function pT(e,t){let n={x:M(e,"x","reshape","string_or_numeric")},a={shape:t};return D.runKernel(Bo,n,a)}var H=O({reshape_:pT});function cT(e,t,n,a,r){let s=M(e,"x","avgPool","float32"),i=1;F(za(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=H(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),r!=null&&F(jt(a),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let d={x:o},u={filterSize:t,strides:n,pad:a,dimRoundingMode:r},p=D.runKernel(us,d,u);return p=fe(p,s.dtype),l?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Gu=O({avgPool_:cT});function hT(e,t,n,a,r,s="NDHWC"){let i=M(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),r!=null&&F(jt(a),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${a}.`);let d={x:o},u={filterSize:t,strides:n,pad:a,dimRoundingMode:r,dataFormat:s},p=D.runKernel(yu,d,u);return p=fe(p,o.dtype),l?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var oA=O({avgPool3d_:hT});function fT(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=Bu(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
|
|
with dtype ${s.dtype}. `)}),n.length===1)return Oa(n[0]);let a=n,r={axis:t};return D.runKernel(po,a,r)}var ot=O({concat_:fT});function mT(e){let t={x:M(e,"x","sigmoid")};return D.runKernel(Gs,t)}var kn=O({sigmoid_:mT});function AT(e,t,n){let a=M(e,"x","slice","string_or_numeric");if(a.rank===0)throw new Error("Slicing scalar is not possible");let r={x:a},s={begin:t,size:n};return D.runKernel(Ho,r,s)}var Re=O({slice_:AT});function yT(e){let t={x:M(e,"x","tanh")};return D.runKernel(Qs,t)}var pi=O({tanh_:yT});function gT(e,t,n,a,r,s){let i=M(e,"forgetBias","basicLSTMCell"),o=M(t,"lstmKernel","basicLSTMCell"),l=M(n,"lstmBias","basicLSTMCell"),d=M(a,"data","basicLSTMCell"),u=M(r,"c","basicLSTMCell"),p=M(s,"h","basicLSTMCell"),c=ot([d,p],1),h=Be(c,o),m=se(h,l),f=m.shape[0],A=m.shape[1]/4,y=[f,A],g=Re(m,[0,0],y),x=Re(m,[0,A],y),w=Re(m,[0,A*2],y),b=Re(m,[0,A*3],y),v=se(_(kn(g),pi(x)),_(u,kn(se(i,w)))),N=_(pi(v),kn(b));return[v,N]}var xT=O({basicLSTMCell_:gT});function bT(e,t,n){let a=M(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);F(a.rank>=1+t.length,()=>`input rank is ${a.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(a.shape[0]%r==0,()=>`input tensor batch is ${a.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:a},i={blockShape:t,crops:n};return D.runKernel(gu,s,i)}var qu=O({batchToSpaceND_:bT});function vT(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function wT(e,t,n,a,r,s){s==null&&(s=.001);let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),d;r!=null&&(d=M(r,"scale","batchNorm"));let u;a!=null&&(u=M(a,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(d==null||o.rank===d.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:vT(i),scale:d,offset:u,mean:o,variance:l},c={varianceEpsilon:s},h=D.runKernel(ws,p,c);return H(h,i.shape)}var ci=O({batchNorm_:wT});function kT(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),d;r!=null&&(d=M(r,"scale","batchNorm"));let u;return a!=null&&(u=M(a,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),d!=null&&F(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${d.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),ci(i,o,l,u,d,s)}var Tb=O({batchNorm2d_:kT});function IT(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),d;r!=null&&(d=M(r,"scale","batchNorm"));let u;return a!=null&&(u=M(a,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),d!=null&&F(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${d.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),ci(i,o,l,u,d,s)}var Eb=O({batchNorm3d_:IT});function ST(e,t,n,a,r,s){let i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),l=M(n,"variance","batchNorm"),d;r!=null&&(d=M(r,"scale","batchNorm"));let u;return a!=null&&(u=M(a,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),d!=null&&F(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${d.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),ci(i,o,l,u,d,s)}var Cb=O({batchNorm4d_:ST});function NT(e,t,n){let a=M(e,"x","bincount"),r=M(t,"weights","bincount");F(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(r.size===a.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${a.shape}, weights shape: ${r.shape}.`);let s={x:a,weights:r},i={size:n};return D.runKernel(Rp,s,i)}var lA=O({bincount_:NT});function TT(e,t){let n=M(e,"broadcastTo","x"),a=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=H(n,l)}let r=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${a}] cannot be broadcast to [${t}].`);if(s.map((l,d)=>l>1?d:-1).filter(l=>l>=0).length===0)return Oa(n);let i={x:n},o={reps:s};return D.runKernel(Sr,i,o)}var cl=O({broadcastTo_:TT});function ET(e){let t={x:M(e,"x","ceil")};return D.runKernel(cs,t)}var uA=O({ceil_:ET});function CT(e,t,n){let a=M(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:a},s={clipValueMin:t,clipValueMax:n};return D.runKernel(Ir,r,s)}var In=O({clipByValue_:CT});function RT(e){return ot(e,0)}var Rb=O({concat1d_:RT});function MT(e,t){return ot(e,t)}var hl=O({concat2d_:MT});function FT(e,t){return ot(e,t)}var Mb=O({concat3d_:FT});function $T(e,t){return ot(e,t)}var Fb=O({concat4d_:$T});function DT(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","conv2d"),l=M(t,"filter","conv2d"),d=o,u=!1;o.rank===3&&(u=!0,d=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(d.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${d.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(jt(a),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p=r==="NHWC"?d.shape[3]:d.shape[1];F(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),F(za(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let c={x:d,filter:l},h={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},m=D.runKernel(hs,c,h);return u?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var ar=O({conv2d_:DT});function OT(e,t,n,a,r="NWC",s=1,i){let o=M(e,"x","conv1d"),l=M(t,"filter","conv1d"),d=o,u=!1;o.rank===2&&(u=!0,d=H(o,[1,o.shape[0],o.shape[1]])),F(d.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${d.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(jt(a),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`),F(d.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${d.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(za(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),c=H(d,[d.shape[0],1,d.shape[1],d.shape[2]]),h=ar(c,p,[1,n],a,"NHWC",[1,s],i);return u?H(h,[h.shape[2],h.shape[3]]):H(h,[h.shape[0],h.shape[2],h.shape[3]])}var bc=O({conv1d_:OT});function zT(e,t,n,a,r,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,d=!1;t.rank===3&&(d=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],p=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),F(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(jt(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let c={dy:l,filter:n},h={strides:a,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},m=D.runKernel(fs,c,h);return d?H(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var dA=O({conv2DBackpropInput_:zT});function _T(e,t,n,a,r,s){let i=M(e,"x","conv2dTranspose"),o=M(t,"filter","conv2dTranspose");return dA(n,i,o,a,r,"NHWC",s)}var vc=O({conv2dTranspose_:_T});function PT(e,t,n,a,r="NDHWC",s=[1,1,1]){let i=M(e,"x","conv3d"),o=M(t,"filter","conv3d"),l=i,d=!1;i.rank===4&&(d=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(za(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:o},p={strides:n,pad:a,dataFormat:r,dilations:s},c=D.runKernel(bu,u,p);return d?H(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var pA=O({conv3d_:PT});function LT(e,t,n,a,r){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],d=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(d===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},p={pad:r,strides:a,inputShape:s},c=D.runKernel(Dp,u,p);return o?H(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}var $b=O({conv3DBackpropInput_:LT});function WT(e,t,n,a,r){let s=M(e,"x","conv3dTranspose"),i=M(t,"filter","conv3dTranspose");return $b(n,s,i,a,r)}var Db=O({conv3dTranspose_:WT});function BT(e){let t={x:M(e,"x","cos")};return D.runKernel(ms,t)}var Xu=O({cos_:BT});function VT(e){let t={x:M(e,"x","cosh")};return D.runKernel(co,t)}var wc=O({cosh_:VT});function jT(e,t=0,n=!1,a=!1){let r={x:M(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:a};return D.runKernel(As,r,s)}var kc=O({cumsum_:jT});function UT(e,t,n,a=!1){let r=M(e,"x","denseBincount"),s=M(t,"weights","denseBincount");F(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),F(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:n,binaryOutput:a};return D.runKernel(Op,i,o)}var Ob=O({denseBincount_:UT});function HT(e,t,n="NHWC"){let a=M(e,"x","depthToSpace"),r=n==="NHWC"?a.shape[1]:a.shape[2],s=n==="NHWC"?a.shape[2]:a.shape[3],i=n==="NHWC"?a.shape[3]:a.shape[1];F(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${r} and ${t} for depthToSpace with input shape
|
|
${a.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${t} for depthToSpace with input shape
|
|
${a.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},l={blockSize:t,dataFormat:n};return D.runKernel(fo,o,l)}var cA=O({depthToSpace_:HT});function GT(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","depthwiseConv2d"),l=M(t,"filter","depthwiseConv2d"),d=o,u=!1;o.rank===3&&(u=!0,d=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(d.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(d.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(jt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:d,filter:l},c={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=D.runKernel(ys,p,c);return u?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var fl=O({depthwiseConv2d_:GT});function qT(e){let t={x:M(e,"x","diag")};return D.runKernel(Pp,t)}var XT=O({diag_:qT});function KT(e,t,n,a,r=[1,1],s="NHWC"){let i=M(e,"x","dilation2d"),o=M(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,d=!1;i.rank===3&&(l=H(i,[1,i.shape[0],i.shape[1],i.shape[2]]),d=!0);let u={x:l,filter:o},p={strides:n,pad:a,dilations:r},c=D.runKernel(vu,u,p);return d?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var hA=O({dilation2d_:KT});function ZT(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let s=n-1-r,i=e[s]||1;(t[t.length-1-r]||1)>1&&i===1&&a.unshift(s)}return a}function _t(e,t){let n=[];for(let a=0;a<t.length;a++){let 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rank ${s.rank}.`),F(jt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=H(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},d={depthRadius:t,bias:n,alpha:a,beta:r},u=D.runKernel(Su,l,d);return o?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var xA=O({localResponseNormalization_:vE});function wE(e){let t={x:M(e,"x","log")};return D.runKernel(Ns,t)}var $n=O({log_:wE});function kE(e){let t={x:M(e,"x","log1p")};return D.runKernel(Co,t)}var Nc=O({log1p_:kE});function IE(e){return F(wr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=M(t,"x","tf.grad","string_or_numeric"),r=n!=null?M(n,"dy","tf.grad"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(a),[a],r);return r!=null&&on(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Tc(i),i[0]})}}function SE(e){return F(wr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=Bu(t,"args","tf.grads","string_or_numeric"),r=n!=null?M(n,"dy","tf.grads"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(...a),a,r);return r!=null&&on(s.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Tc(i),i})}}function NE(e){return F(wr(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof Le,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof Le,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:a,value:r}=D.gradients(()=>e(t),[t],n);return Tc(a),{grad:a[0],value:r}}}function TE(e){return F(wr(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(r=>r instanceof Le),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof Le,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let a=D.gradients(()=>e(...t),t,n);return n!=null&&on(a.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Tc(a.grads),a}}function Bb(e,t){F(wr(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(d=>d instanceof Pu),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let d in D.registeredVariables)t.push(D.registeredVariables[d])}let a=n?t.filter(d=>!d.trainable):null,r=t.length;t=t.filter(d=>d.trainable),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=D.gradients(e,t,null,s);F(o.some(d=>d!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((d,u)=>{o[u]!=null&&(l[d.name]=o[u])}),a!=null&&a.forEach(d=>l[d.name]=null),{value:i,grads:l}}function _a(e){return D.customGrad(e)}function Tc(e){if(e.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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the f you passed encloses all operations that lead from x to y.`)}function EE(e){let t={x:M(e,"x","neg")};return D.runKernel(Fo,t)}var wt=O({neg_:EE});function CE(e){let t={x:M(e,"x","softplus")};return D.runKernel(Xo,t)}var fi=O({softplus_:CE});function RE(e){let t=M(e,"x","logSigmoid");return _a(n=>({value:wt(fi(wt(n))),gradFunc:a=>_(a,kn(wt(n)))}))(t)}var Vb=O({logSigmoid_:RE});function ME(e,t=null,n=!1){let a={x:M(e,"x","max")},r={reductionIndices:t,keepDims:n};return D.runKernel(Ts,a,r)}var Kn=O({max_:ME});function FE(e,t){let n=M(e,"a","sub"),a=M(t,"b","sub");[n,a]=vt(n,a);let r={a:n,b:a};return D.runKernel(Ys,r)}var ge=O({sub_:FE});function $E(e,t=null,n=!1){let a=M(e,"x","sum");a.dtype==="bool"&&(a=fe(a,"int32"));let r={x:a},s={axis:t,keepDims:n};return D.runKernel(Xs,r,s)}var ke=O({sum_:$E});function DE(e,t=-1){let n=M(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. 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EM=O({transform_:TM});function CM(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let a=M(e,"a","bandPart");F(a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${a.rank}.`);let r=a.shape,[s,i]=a.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=H(vl(0,s,1,"int32"),[-1,1]),l=vl(0,i,1,"int32"),d=ge(o,l),u=la(Or(d,Se(+t,"int32")),Dr(d,Se(-n,"int32"))),p=Rt([s,i],a.dtype);return H(_n(ua(H(a,[-1,s,i])).map(c=>an(u,c,p))),r)}var RM=O({bandPart_:CM});function MM(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let s=1;s<e.length;++s)F(e[s].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${r})`)}else t=!0,e=rn(e,e.shape[0],0).map(r=>zr(r,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],a=e;for(let r=0;r<e.length;++r)n.push(D.tidy(()=>{let s=a[r];if(r>0)for(let i=0;i<r;++i){let o=_(ke(_(n[i],s)),n[i]);s=ge(s,o)}return me(s,Uc(s,"euclidean"))}));return t?_n(n,0):n}var FM=O({gramSchmidt_:MM});function $M(e,t=!1){if(F(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return m3(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,d)=>l*d),a=ua(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],s=[];a.forEach(l=>{let[d,u]=m3(l,t);r.push(d),s.push(u)});let i=H(_n(r,0),e.shape),o=H(_n(s,0),e.shape);return[i,o]}}function m3(e,t=!1){return D.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],a=e.shape[1],r=yA(n),s=Oa(e),i=xa([[1]],[1,1]),o=Oa(i),l=n>=a?a:n;for(let d=0;d<l;++d){let u=s,p=o,c=r;[o,s,r]=D.tidy(()=>{let h=Re(s,[d,d],[n-d,1]),m=Uc(h),f=Re(s,[d,d],[1,1]),A=an(Fn(f,0),xa([[-1]]),xa([[1]])),y=ge(f,_(A,m)),g=me(h,y);g.shape[0]===1?o=Oa(i):o=ot([i,Re(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let x=wt(me(Be(A,y),m)),w=Re(s,[d,0],[n-d,a]),b=_(x,o),v=Ze(o);if(d===0)s=ge(w,Be(b,Be(v,w)));else{let R=ge(w,Be(b,Be(v,w)));s=ot([Re(s,[0,0],[d,a]),R],0)}let N=Ze(b),T=Re(r,[0,d],[n,r.shape[1]-d]);if(d===0)r=ge(T,Be(Be(T,o),N));else{let R=ge(T,Be(Be(T,o),N));r=ot([Re(r,[0,0],[n,d]),R],1)}return[o,s,r]}),Ee([u,p,c])}return!t&&n>a&&(r=Re(r,[0,0],[n,a]),s=Re(s,[0,0],[a,a])),[r,s]})}var DM=O({qr_:$M}),dn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(dn||(dn={}));function OM(e,t,n=dn.SUM_BY_NONZERO_WEIGHTS){let a=M(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=M(t,"weights","computeWeightedLoss"));let s=r==null?a:_(a,r);if(n===dn.NONE)return s;if(n===dn.SUM)return ke(s);if(n===dn.MEAN){if(r==null)return kt(s);{let i=a.size/r.size,o=me(ke(s),ke(r));return i>1?me(o,Se(i)):o}}if(n===dn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return me(ke(s),Se(a.size));{let i=_(r,Dn(a.shape)),o=fe(ke(Ai(i,Se(0))),"float32");return me(ke(s),o)}}throw Error(`Unknown reduction: ${n}`)}var ir=O({computeWeightedLoss_:OM});function zM(e,t,n,a=dn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","absoluteDifference"),s=M(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=M(n,"weights","absoluteDifference")),on(r.shape,s.shape,"Error in absoluteDifference: ");let o=zt(ge(r,s));return ir(o,i,a)}var _M=O({absoluteDifference_:zM});function PM(e,t,n,a,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","cosineDistance"),i=M(t,"predictions","cosineDistance"),o=null;a!=null&&(o=M(a,"weights","cosineDistance")),on(s.shape,i.shape,"Error in cosineDistance: ");let l=Se(1),d=ge(l,ke(_(s,i),n,!0));return ir(d,o,r)}var LM=O({cosineDistance_:PM});function WM(e,t,n,a=dn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","hingeLoss"),s=M(t,"predictions","hingeLoss"),i=null;n!=null&&(i=M(n,"weights","hingeLoss")),on(r.shape,s.shape,"Error in hingeLoss: ");let o=Se(1);r=ge(_(Se(2),r),o);let l=La(ge(o,_(r,s)));return ir(l,i,a)}var BM=O({hingeLoss_:WM});function VM(e,t,n,a=1,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","huberLoss"),i=M(t,"predictions","huberLoss"),o=null;n!=null&&(o=M(n,"weights","huberLoss")),on(s.shape,i.shape,"Error in huberLoss: ");let l=Se(a),d=zt(ge(i,s)),u=xl(d,l),p=ge(d,u),c=se(_(Se(.5),st(u)),_(l,p));return ir(c,o,r)}var jM=O({huberLoss_:VM});function UM(e,t,n,a=1e-7,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"labels","logLoss"),i=M(t,"predictions","logLoss"),o=null;n!=null&&(o=M(n,"weights","logLoss")),on(s.shape,i.shape,"Error in logLoss: ");let l=Se(1),d=Se(a),u=wt(_(s,$n(se(i,d)))),p=_(ge(l,s),$n(se(ge(l,i),d))),c=ge(u,p);return ir(c,o,r)}var HM=O({logLoss_:UM});function GM(e,t,n,a=dn.SUM_BY_NONZERO_WEIGHTS){let r=M(e,"labels","meanSquaredError"),s=M(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=M(n,"weights","meanSquaredError")),on(r.shape,s.shape,"Error in meanSquaredError: ");let o=Bc(r,s);return ir(o,i,a)}var qM=O({meanSquaredError_:GM});function XM(e,t){let n=M(e,"labels","sigmoidCrossEntropyWithLogits"),a=M(t,"logits","sigmoidCrossEntropyWithLogits");on(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=La(a),s=_(a,n),i=Nc(Xn(wt(zt(a))));return se(ge(r,s),i)}function KM(e,t,n,a=0,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),on(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let d=Se(a),u=Se(1),p=Se(.5);s=se(_(s,ge(u,d)),_(p,d))}let l=XM(s,i);return ir(l,o,r)}var ZM=O({sigmoidCrossEntropy_:KM});function YM(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 _a((a,r,s)=>{let i=wA(r,[n],!0),o=ge(fe(r,"float32"),i);s([a,o]);let l=wt(_(o,a));return{value:ke(l,[n]),gradFunc:(d,u)=>{let[p,c]=u,h=mi(d.shape,[n]);return[_(H(d,h),ge(fe(p,"float32"),Xn(c))),_(H(d,h),ge(Xn(c),fe(p,"float32")))]}}})(e,t)}function JM(e,t,n,a=0,r=dn.SUM_BY_NONZERO_WEIGHTS){let s=M(e,"onehotLabels","softmaxCrossEntropy"),i=M(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","softmaxCrossEntropy")),on(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let d=Se(a),u=Se(1),p=Se(s.shape[1]);s=se(_(s,ge(u,d)),me(d,p))}let l=YM(s,i);return ir(l,o,r)}var QM=O({softmaxCrossEntropy_:JM});function eF(e,t,n,a){let r=M(e,"indices","sparseFillEmptyRows"),s=M(t,"values","sparseFillEmptyRows"),i=M(n,"denseShape","sparseFillEmptyRows"),o=M(a,"defaultValue","sparseFillEmptyRows",s.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
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|
${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let l={indices:r,values:s,denseShape:i,defaultValue:o},d=D.runKernel(tc,l);return{outputIndices:d[0],outputValues:d[1],emptyRowIndicator:d[2],reverseIndexMap:d[3]}}var tF=O({sparseFillEmptyRows_:eF});function nF(e,t,n){let a=M(e,"inputIndices","sparseReshape"),r=M(t,"inputShape","sparseReshape"),s=M(n,"newShape","sparseReshape");if(a.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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|
${a.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=D.runKernel(nc,i);return{outputIndices:o[0],outputShape:o[1]}}var aF=O({sparseReshape_:nF}),rF={fft:ad,ifft:wl,rfft:rd,irfft:Wc},sF={hammingWindow:qR,hannWindow:o3,frame:l3,stft:YR},Ye={flipLeftRight:tM,resizeNearestNeighbor:f3,resizeBilinear:h3,rotateWithOffset:aM,cropAndResize:QR,nonMaxSuppression:sM,nonMaxSuppressionAsync:hM,nonMaxSuppressionWithScore:mM,nonMaxSuppressionWithScoreAsync:yM,nonMaxSuppressionPadded:xM,nonMaxSuppressionPaddedAsync:vM,threshold:NM,transform:EM},A3={bandPart:RM,gramSchmidt:FM,qr:DM},iF={absoluteDifference:_M,computeWeightedLoss:ir,cosineDistance:LM,hingeLoss:BM,huberLoss:jM,logLoss:HM,meanSquaredError:qM,sigmoidCrossEntropy:ZM,softmaxCrossEntropy:QM},y3={sparseFillEmptyRows:tF,sparseReshape:aF},or=class extends xb{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Ee(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Bb(e,t)}dispose(){this.iterations_!=null&&Ee(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(or,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Kc=class extends or{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=D.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:W(()=>Ue(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:W(()=>Ue(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;W(()=>{let l=se(_(i,this.rho),_(st(s),1-this.rho)),d=_(me(Qt(se(o,this.epsilon)),Qt(se(i,this.epsilon))),s),u=se(_(o,this.rho),_(st(d),1-this.rho));i.assign(l),o.assign(u);let p=se(_(d,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Kc.className="Adadelta";Rr(Kc);var Zc=class extends or{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=D.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:W(()=>Al(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;W(()=>{let i=se(s,st(r));s.assign(i);let o=se(_(me(r,Qt(se(i,D.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(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)}};Zc.className="Adagrad";Rr(Zc);var Yc=class extends or{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],W(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(n).variable()}),a==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=ge(1,this.accBeta1),a=ge(1,this.accBeta2);t.forEach((r,s)=>{let i=D.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:W(()=>Ue(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:W(()=>Ue(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let d=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,p=se(_(d,this.beta1),_(l,1-this.beta1)),c=se(_(u,this.beta2),_(st(l),1-this.beta2)),h=me(p,n),m=me(c,a);d.assign(p),u.assign(c);let f=se(_(me(h,se(Qt(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(_(this.accBeta1,this.beta1)),this.accBeta2.assign(_(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(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),W(()=>{this.accBeta1.assign(sr(this.beta1,this.iterations_+1)),this.accBeta2.assign(sr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.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)}};Yc.className="Adam";Rr(Yc);var Jc=class extends or{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],W(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),a==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);W(()=>{let n=ge(1,this.accBeta1),a=me(-this.learningRate,se(_(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=D.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ue(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ue(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let d=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,p=se(_(d,this.beta1),_(l,1-this.beta1)),c=_(u,this.beta2),h=zt(l),m=Pa(c,h);d.assign(p),u.assign(m);let f=se(_(me(a,n),me(p,se(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(_(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ee(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Jc.className="Adamax";Rr(Jc);var sd=class extends or{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=D.registeredVariables[t];W(()=>{let s=se(_(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ut(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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${s}).`);if(n<a)throw new Error(`batchDims (${a}) must be less than or equal to axis (${n}).`);for(let p=0;p<a;++p)if(e.shape[p]!==t.shape[p])throw new Error(`x.shape[${p}]: ${e.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);let i=e.shape[n],o=[],l=1,d=1,u=1;for(let p=0;p<a;++p)o.push(e.shape[p]),l*=e.shape[p];for(let p=a;p<n;p++)o.push(e.shape[p]),d*=e.shape[p];for(let p=a;p<r;p++)o.push(t.shape[p]);for(let p=n+1;p<s;p++)o.push(e.shape[p]),u*=e.shape[p];return{batchSize:l,sliceSize:u,outerSize:d,dimSize:i,outputShape:o}}function LF(e){try{return e.map(t=>lc(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function WF(e){return e.map(t=>Du(t))}var Wa={};Fe(Wa,{nonMaxSuppressionV3Impl:()=>u3,nonMaxSuppressionV4Impl:()=>d3,nonMaxSuppressionV5Impl:()=>p3,whereImpl:()=>Qb});function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var GF=Wa.whereImpl,nh=class extends hu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ip(this,nr())}nextDataId(){return nh.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&C.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 a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,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,a,r){this.data.set(e,{values:t,dtype:a,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 a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return C.mergeRealAndImagArrays(a,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(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return nr().makeTensorFromDataId(a,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=k.now();return e(),{kernelMs:k.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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r==="string"?C.fromStringArrayToUint8(u.values):u.values}function bi(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;ve(r,"slice");let[o,l]=ln.parseSliceParams(r,s,i);ln.assertParamsValid(r,o,l);let d=n.data.get(r.dataId).values,u=ah(d,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}var C$={kernelName:Ho,backendName:"cpu",kernelFunc:bi};function V3(e,t,n,a,r,s,i){let o=t[0],l=s[0],d=new Array(l),u=new Array(o),p=t[1];if(l===0){if(o!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
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indices.shape[0] = ${o}`);let A=k.getArrayFromDType(n,0),y=k.getArrayFromDType(r,0);return[A,[0,p],y,d,u]}let c=!0,h=0,m=new Array(l).fill(0);for(let A=0;A<o;++A){let y=e[A*p];if(y<0)throw new Error(`indices(${A}, 0) is invalid: ${y} < 0`);if(y>=l)throw new Error(`indices(${A}, 0) is invalid: ${y} >= ${l}`);++m[y],c=c&&y>=h,h=y}let f=!0;for(let A=0;A<l;++A){let y=m[A]===0;d[A]=y,f=f&&!y,m[A]=Math.max(m[A],1),A>0&&(m[A]+=m[A-1])}if(f&&c){let A=e,y=a;for(let g=0;g<o;++g)u[g]=g;return[A,[o,p],y,d,u]}else{let A=m[l-1],y=k.getArrayFromDType(n,A*p),g=k.getArrayFromDType(r,A),x=new Array(l).fill(0);for(let w=0;w<o;++w){let b=e[w*p],v=x[b],N=(b===0?0:m[b-1])+v;x[b]++;for(let T=0;T<p;++T)y[N*p+T]=e[w*p+T];g[N]=a[w],u[w]=N}for(let w=0;w<l;++w)if(x[w]===0){let b=w===0?0:m[w-1];y[b*p+0]=w;for(let v=1;v<p;++v)y[b*p+v]=0;g[b]=i}return[y,[o,p],g,d,u]}}function j3(e,t,n,a,r){let s=k.sizeFromShape(a),i=t[0],o=r.length,l=[],d=1,u=-1;for(let A=0;A<o;++A){let y=r[A];if(y===-1){if(u!==-1)throw new Error(`only one output dimension may be -1, not both ${u} and ${A}`);u=A,l.push(1)}else{if(y<0)throw new Error(`size ${A} must be non-negative, not ${y}`);d*=y,l.push(y)}}if(u!==-1){if(d<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let A=Math.trunc(s/d);if(d*A!==s)throw new Error(`Input to reshape is a SparseTensor with ${s}
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dense values, but the requested shape requires a multiple of ${d}. inputShape=${a} outputShape= ${l}`);l[u]=A}let p=k.sizeFromShape(l);if(p!==s)throw new Error(`Input to reshape is a tensor with ${s} dense values, but the requested shape has ${p}. inputShape=${a} outputShape=${l}`);let c=a.length,h=[];if(c>0){h[c-1]=1;for(let A=c-2;A>=0;--A)h[A]=h[A+1]*a[A+1]}let m=[];if(o>0){m[o-1]=1;for(let A=o-2;A>=0;--A)m[A]=m[A+1]*l[A+1]}let f=k.getArrayFromDType(n,i*o);for(let A=0;A<i;++A){let y=0;for(let g=0;g<c;++g)y+=e[A*c+g]*h[g];for(let g=0;g<o;++g)f[A*o+g]=Math.trunc(y/m[g]),y%=m[g]}return[f,[i,o],l]}var U3=Mt((e,t)=>{let n=e-t;return n*n}),R$=Ht(Zs,U3),M$={kernelName:Zs,backendName:"cpu",kernelFunc:R$};function H3(e,t,n,a){let r=We(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+a[l];r.set(t.get(...o),...i)}return r}var G3=Mt((e,t)=>e-t),F$=KA((e,t,n,a)=>({real:e-n,imag:t-a})),ZA=Ht(Ys,G3,F$),$$={kernelName:Ys,backendName:"cpu",kernelFunc:ZA};function q3(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=We(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}function X3(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=k.getTypedArrayFromDType(n,i*a),d=k.getTypedArrayFromDType("int32",i*a);for(let p=0;p<i;p++){let c=p*o,h=e.subarray(c,c+o),m=[];for(let g=0;g<h.length;g++)m.push({value:h[g],index:g});m.sort((g,x)=>x.value-g.value);let f=p*a,A=l.subarray(f,f+a),y=d.subarray(f,f+a);for(let g=0;g<a;g++)A[g]=m[g].value,y[g]=m[g].index}let u=t.slice();return u[u.length-1]=a,[We(u,n,l),We(u,"int32",d)]}function K3(e,t,n,a){let r=k.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let m=0;m<r;m++)s[0]*=n[m];s[1]=n[r];for(let m=r+1;m<n.length;m++)s[2]*=n[m];let i={},o=new Int32Array(n[r]),l=new Ot(s,a,e),d=[],u=s[0]===1&&s[2]===1;for(let m=0;m<n[r];m++){let f;if(u)f=e[m].toString();else{let A=[];for(let y=0;y<s[0];y++)for(let g=0;g<s[2];g++)A.push(l.get(y,m,g));f=A.join(",")}if(i[f]!==void 0)o[m]=i[f];else{let A=Object.keys(i).length;i[f]=A,o[m]=A,d.push(m)}}let p=s.slice();p[1]=Object.keys(i).length;let c=new Ot(p,a);d.forEach((m,f)=>{for(let A=0;A<s[0];A++)for(let y=0;y<s[2];y++)c.set(l.get(A,m,y),A,f,y)});let h=n.slice();return h[r]=p[1],{outputValues:c.values,outputShape:h,indices:o}}var Y3="3.6.0";dl("cpu",()=>new nh,1);var J3=nt(mo,e=>e>=0?e:Math.exp(e)-1),D$={kernelName:mo,backendName:"cpu",kernelFunc:J3};function Q3(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;ve([r],"leakyRelu");let i=k.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=k.getTypedArrayFromDType("float32",i);for(let d=0;d<o.length;d++)l[d]=o[d]<0?s*o[d]:o[d];return n.makeTensorInfo(r.shape,"float32",l)}var O$={kernelName:Ss,backendName:"cpu",kernelFunc:Q3},z$=Mt((e,t)=>e<0?t*e:e);function e7(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;ve([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=z$(a.shape,r.shape,s,i,a.dtype);return n.makeTensorInfo(l,a.dtype,o)}var _$={kernelName:Ps,backendName:"cpu",kernelFunc:e7},t7=nt(Ls,e=>Math.max(0,e)),P$={kernelName:Ls,backendName:"cpu",kernelFunc:t7},n7=nt(Bs,e=>Math.min(Math.max(0,e),6)),L$={kernelName:Bs,backendName:"cpu",kernelFunc:n7},a7=nt(Gs,e=>1/(1+Math.exp(-e))),W$={kernelName:Gs,backendName:"cpu",kernelFunc:a7};function YA(e,t,n,a,r){if(n==="linear")return Ba({inputs:{x:t},backend:e});if(n==="relu")return t7({inputs:{x:t},backend:e});if(n==="elu")return J3({inputs:{x:t},backend:e});if(n==="relu6")return n7({inputs:{x:t},backend:e});if(n==="prelu")return e7({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return Q3({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return a7({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function ht(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let d=n.data.get(r.dataId);if(d.complexTensorInfos!=null){let u=d.complexTensorInfos.real,p=d.complexTensorInfos.imag;u.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var B$={kernelName:Bo,backendName:"cpu",kernelFunc:ht};function r7(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;ve([r,s],"matMul");let l=r.shape.length,d=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[d-1]:s.shape[d-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[d-2]:s.shape[d-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),A=k.sizeFromShape(m),y=k.sizeFromShape(f),g=A===y||A===1||y===1;k.assert(l>=2&&d>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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rt=0;for(let Xe=Ne;Xe<De;Xe++){let pt=Math.min(ye,A-1)*X,Ve=Math.min(ye,y-1)*ne,yn=P[pt+Qe*G+Xe*ee],xt=V[Xe*Y+et*re+Ve];rt+=yn*xt}de[ye*ie+(Qe*$+et)]+=rt}}return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(x,Q.dtype,Q.values)}var V$={kernelName:ds,backendName:"cpu",kernelFunc:r7};function j$(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a,c,h,m,f=[];c=r7({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:d},backend:n}),i&&(h=id({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),u&&(m=YA(n,c,u,o,p),f.push(c),c=m);for(let A of f)n.disposeIntermediateTensorInfo(A);return c}var U$={kernelName:ti,backendName:"cpu",kernelFunc:j$},H$=nt(to,e=>Math.acos(e)),G$={kernelName:to,backendName:"cpu",kernelFunc:H$},q$=nt(no,e=>Math.acosh(e)),X$={kernelName:no,backendName:"cpu",kernelFunc:q$};function K$(e){let{inputs:t,backend:n}=e,a=t;ve(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=We(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let d=0;d<i.length;d++)i[d]+=l[d]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var Z$={kernelName:os,backendName:"cpu",kernelFunc:K$};function Y$(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"all");let o=k.parseAxisParam(s,r.shape),l=o,d=C.getAxesPermutation(l,r.shape.length),u=r;d!=null&&(u=Zn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[p,c]=C.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let g=y*h,x=f[g];for(let w=0;w<h;++w){let b=f[g+w];x=x&&b}m[y]=x}d!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(p,u.dtype,m);if(i){let y=C.expandShapeToKeepDim(p,o),g=ht({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var J$={kernelName:ao,backendName:"cpu",kernelFunc:Y$};function Q$(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"any");let o=k.parseAxisParam(s,r.shape),l=o,d=C.getAxesPermutation(l,r.shape.length),u=r;d!=null&&(u=Zn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[p,c]=C.computeOutAndReduceShapes(u.shape,l),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),u.dtype),f=n.data.get(u.dataId).values;for(let y=0;y<m.length;++y){let g=y*h,x=f[g];for(let w=0;w<h;++w){let b=f[g+w];x=x||b}m[y]=x}d!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(p,u.dtype,m);if(i){let y=C.expandShapeToKeepDim(p,o),g=ht({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var eD={kernelName:ro,backendName:"cpu",kernelFunc:Q$};function tD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=Zn({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,p]=C.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(u),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let A=0;A<h.length;++A){let y=A*m,g=f[y],x=0;for(let w=0;w<m;++w){let b=f[y+w];b>g&&(g=b,x=w)}h[A]=x}return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",h)}var nD={kernelName:ls,backendName:"cpu",kernelFunc:tD};function aD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=Zn({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,p]=C.computeOutAndReduceShapes(l.shape,i),c=k.sizeFromShape(u),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let A=0;A<h.length;++A){let y=A*m,g=f[y],x=0;for(let w=0;w<m;++w){let b=f[y+w];b<g&&(g=b,x=w)}h[A]=x}return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",h)}var rD={kernelName:Au,backendName:"cpu",kernelFunc:aD},sD=nt(so,e=>Math.asin(e)),iD={kernelName:so,backendName:"cpu",kernelFunc:sD},oD=nt(io,e=>Math.asinh(e)),lD={kernelName:io,backendName:"cpu",kernelFunc:oD},uD=nt(oo,e=>Math.atan(e)),dD={kernelName:oo,backendName:"cpu",kernelFunc:uD},pD=Mt((e,t)=>Math.atan2(e,t)),cD=Ht(uo,pD),hD={kernelName:uo,backendName:"cpu",kernelFunc:cD},fD=nt(lo,e=>Math.atanh(e)),mD={kernelName:lo,backendName:"cpu",kernelFunc:fD};function JA(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,d=r.dilationWidth,u=r.effectiveFilterHeight,p=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,m=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=We(r.outShape,n),A=f.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],g=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let w=0;w<r.batchSize;++w){let b=w*y,v=w*a[0];for(let N=0;N<r.inChannels;++N)for(let T=0;T<r.outHeight;++T){let R=T*i-c,$=Math.max(0,R),z=Math.min(r.inHeight,u+R),P=b+T*g;for(let V=0;V<r.outWidth;++V){let j=V*o-h,U=Math.max(0,j),X=Math.min(r.inWidth,p+j),G=m,ee=0,Y=0;for(let ne=$;ne<z;ne+=l){let ie=v+ne*a[1];for(let Q=U;Q<X;Q+=d){let de=ie+Q*a[2],oe=e[de+N];s==="max"&&oe>G?G=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(G))break}let re=P+V*x+N;A[re]=s==="avg"?ee/Y:G}}}return f}function s7(e,t,n,a,r=!1,s=!1){let i=We(a.outShape,"int32"),o=a.strideHeight,l=a.strideWidth,d=a.dilationHeight,u=a.dilationWidth,p=a.effectiveFilterHeight,c=a.effectiveFilterWidth,h=a.padInfo.top,m=a.padInfo.left,f=We(t,n,e);for(let A=0;A<a.batchSize;++A)for(let y=0;y<a.inChannels;++y)for(let g=0;g<a.outHeight;++g){let x=g*o-h,w=x;for(;w<0;)w+=d;let b=Math.min(a.inHeight,p+x);for(let v=0;v<a.outWidth;++v){let N=v*l-m,T=N;for(;T<0;)T+=u;let R=Math.min(a.inWidth,c+N),$=Number.NEGATIVE_INFINITY,z=-1;for(let P=w;P<b;P+=d){let V=P-x;for(let j=T;j<R;j+=u){let U=j-N,X=f.get(A,P,j,y);X>$&&($=X,r?z=s?((A*a.inHeight+P)*a.inWidth+j)*a.inChannels+y:(P*a.inWidth+j)*a.inChannels+y:z=V*c+U)}}i.set(z,A,g,v,y)}}return i}function i7(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,d=r.dilationDepth,u=r.dilationHeight,p=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,A=r.padInfo.top,y=r.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(r.outShape,n),w=x.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],v=r.outShape[2]*r.outShape[3]*r.outShape[4],N=r.outShape[3]*r.outShape[4],T=r.outShape[4];for(let R=0;R<r.batchSize;++R){let $=R*b,z=R*a[0];for(let P=0;P<r.inChannels;++P)for(let V=0;V<r.outDepth;++V){let j=V*i-f,U=j;for(;U<0;)U+=d;let X=Math.min(r.inDepth,c+j),G=$+V*v;for(let ee=0;ee<r.outHeight;++ee){let Y=ee*o-A,re=Y;for(;re<0;)re+=u;let ne=Math.min(r.inHeight,h+Y),ie=G+ee*N;for(let Q=0;Q<r.outWidth;++Q){let de=Q*l-y,oe=de;for(;oe<0;)oe+=p;let ye=Math.min(r.inWidth,m+de),ce=ie+Q*T,Ie=g,Ne=0,$e=0;for(let De=U;De<X;De+=d){let Qe=z+De*a[1];for(let et=re;et<ne;et+=u){let rt=Qe+et*a[2];for(let Xe=oe;Xe<ye;Xe+=p){let pt=rt+Xe*a[3],Ve=e[pt+P];if(s==="max"&&Ve>Ie?Ie=Ve:s==="avg"&&(Ne+=Ve,$e++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let ze=ce+P;w[ze]=s==="avg"?Ne/$e:Ie}}}}return x}function AD(e,t){let n=We(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,d=t.effectiveFilterDepth,u=t.effectiveFilterHeight,p=t.effectiveFilterWidth,c=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*a-c,x=g;for(;x<0;)x+=i;let w=Math.min(t.inDepth,d+g);for(let b=0;b<t.outHeight;++b){let v=b*r-h,N=v;for(;N<0;)N+=o;let T=Math.min(t.inHeight,u+v);for(let R=0;R<t.outWidth;++R){let $=R*s-m,z=$;for(;z<0;)z+=l;let P=Math.min(t.inWidth,p+$),V=Number.NEGATIVE_INFINITY,j=-1;for(let U=x;U<w;U+=i){let X=U-g;for(let G=N;G<T;G+=o){let ee=G-v;for(let Y=z;Y<P;Y+=l){let re=Y-$,ne=e.get(f,U,G,Y,A);ne>=V&&(V=ne,j=X*u*p+ee*u+re)}}}n.set(j,f,y,b,R,A)}}}return n}function yD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;ve(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;k.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in avgPool: Either strides or dilations must be 1. 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u=C.computePool3DInfo(s.shape,i,o,1,l,d),p=u.strideDepth,c=u.strideHeight,h=u.strideWidth,m=u.filterDepth,f=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,x=u.dilationWidth,w=u.effectiveFilterDepth,b=u.effectiveFilterHeight,v=u.effectiveFilterWidth,N=w-1-u.padInfo.front,T=v-1-u.padInfo.left,R=b-1-u.padInfo.top,$=We(s.shape,"float32"),z=1/(m*f*A),P=n.bufferSync(r);for(let V=0;V<u.batchSize;++V)for(let j=0;j<u.inChannels;++j)for(let U=0;U<u.inDepth;++U)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=U-N,Y=X-R,re=G-T,ne=0;for(let ie=0;ie<w;ie+=y){let Q=(ee+ie)/p;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let de=0;de<b;de+=g){let oe=(Y+de)/c;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let ye=0;ye<v;ye+=x){let ce=(re+ye)/h;ce<0||ce>=u.outWidth||Math.floor(ce)!==ce||(ne+=P.get(V,Q,oe,ce,j))}}}$.set(ne*z,V,U,X,G,j)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var wD={kernelName:Cp,backendName:"cpu",kernelFunc:vD};function kD(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ve([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:d}=a,u=C.computePool2DInfo(i.shape,o,l,1,d),p=u.strideHeight,c=u.strideWidth,h=u.filterHeight,m=u.filterWidth,f=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,x=g-1-u.padInfo.left,w=y-1-u.padInfo.top,b=We(i.shape,"float32"),v=1/(h*m),N=n.data.get(r.dataId).values,T=We(r.shape,"float32",N);for(let R=0;R<u.batchSize;++R)for(let $=0;$<u.inChannels;++$)for(let z=0;z<u.inHeight;++z)for(let P=0;P<u.inWidth;++P){let V=z-w,j=P-x,U=0;for(let X=0;X<y;X+=f){let G=(V+X)/p;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=A){let Y=(j+ee)/c;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(U+=T.get(R,G,Y,$))}}b.set(U*v,R,z,P,$)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var ID={kernelName:Ep,backendName:"cpu",kernelFunc:kD};function SD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;k.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([r,o,l,s,i],"batchNorm");let{varianceEpsilon:d}=a;d==null&&(d=.001);let u=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,c=n.data.get(l.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(u.length),A=m.length,y=h.length,g=c.length,x=p.length,w=0,b=0,v=0,N=0;for(let T=0;T<u.length;++T)f[T]=m[w++]+(u[T]-p[b++])*h[v++]/Math.sqrt(c[N++]+d),w>=A&&(w=0),b>=x&&(b=0),v>=y&&(v=0),N>=g&&(N=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var ND={kernelName:ws,backendName:"cpu",kernelFunc:SD};function TD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ve([r],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=C.getReshaped(r.shape,s,o),d=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(r.shape,s,o),p=C.getSliceBeginCoords(i,s.length),c=C.getSliceSize(u,i,s.length),h=ht({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Zn({inputs:{x:h},backend:n,attrs:{perm:d}}),f=ht({inputs:{x:m},backend:n,attrs:{shape:u}}),A=bi({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var ED={kernelName:gu,backendName:"cpu",kernelFunc:TD};function CD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=UA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}var RD={kernelName:Rp,backendName:"cpu",kernelFunc:CD},MD=nt(Ir,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),FD={kernelName:Ir,backendName:"cpu",kernelFunc:MD},$D=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let d=0;d<o.length;d++){let u=o[d],p=l[d];a[d]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},DD={kernelName:xu,backendName:"cpu",kernelFunc:$D};function Tl(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var OD={kernelName:Hp,backendName:"cpu",kernelFunc:Tl};function El(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return Ba({inputs:{x:o[0]},backend:n});let l=o.map(f=>f.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let f=o.map(w=>xi({inputs:{input:w},backend:n})),A=o.map(w=>Tl({inputs:{input:w},backend:n})),y=El({inputs:f,backend:n,attrs:{axis:s}}),g=El({inputs:A,backend:n,attrs:{axis:s}}),x=Pn({inputs:{real:y,imag:g},backend:n});return f.forEach(w=>n.disposeIntermediateTensorInfo(w)),A.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),x}let d=o.map(f=>{let A=k.sizeFromShape(f.shape.slice(s));return ht({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),u=d.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=C.computeOutShape(d.map(f=>f.shape),1);let p=d[0].shape[0]===1,c=HA(u,i,t[0].dtype,p),h=C.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var zD={kernelName:po,backendName:"cpu",kernelFunc:El};function o7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:d,dimRoundingMode:u}=a;ve([r,s],"conv2d");let p=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!1,p),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,A=c.dilationWidth,y=c.padInfo.left,g=c.padInfo.top,x=c.dataFormat==="channelsLast",w=new Ot(c.outShape,r.dtype),b=k.computeStrides(r.shape),v=k.computeStrides(s.shape),N=b[0],T=x?b[1]:b[2],R=x?b[2]:1,$=x?1:b[1],z=w.strides[0],P=x?w.strides[1]:w.strides[2],V=x?w.strides[2]:1,j=x?1:w.strides[1],U=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,G=w.values;for(let ee=0;ee<c.batchSize;++ee){let Y=ee*N,re=ee*z;for(let ne=0;ne<c.outHeight;++ne){let ie=re+ne*P,Q=ne*c.strideHeight-g;for(let de=0;de<h;++de){let oe=Q+de*f;if(oe<0||oe>=c.inHeight)continue;let ye=de*v[0],ce=Y+oe*T;for(let 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Ot(m.inShape,"float32"),A=f.values,y=n.data.get(r.dataId).values,g=n.data.get(s.dataId).values,[x,w,b]=p,{batchSize:v,filterHeight:N,filterWidth:T,inChannels:R,inHeight:$,inWidth:z,outChannels:P,outHeight:V,outWidth:j,strideHeight:U,strideWidth:X}=m;h=m.dataFormat;let G=N-1-m.padInfo.top,ee=T-1-m.padInfo.left,Y=h==="channelsLast",re=f.strides[0],ne=Y?f.strides[1]:f.strides[2],ie=Y?f.strides[2]:1,Q=Y?1:f.strides[1],de=c[0],oe=Y?c[1]:c[2],ye=Y?c[2]:1,ce=Y?1:c[1];for(let Ie=0;Ie<v;++Ie)for(let Ne=0;Ne<R;++Ne)for(let $e=0;$e<$;++$e){let ze=$e-G,De=Math.max(0,Math.ceil(ze/U)),Qe=Math.min(V,(N+ze)/U);for(let et=0;et<z;++et){let rt=et-ee,Xe=Math.max(0,Math.ceil(rt/X)),pt=Math.min(j,(T+rt)/X),Ve=0;for(let xt=De;xt<Qe;++xt){let jn=xt*U-ze;for(let Kt=Xe;Kt<pt;++Kt){let gn=Kt*X-rt,Un=de*Ie+oe*xt+ye*Kt,Mn=x*(N-1-jn)+w*(T-1-gn)+b*Ne;for(let sn=0;sn<P;++sn){let Zt=y[Un+ce*sn],Ma=g[Mn+sn];Ve+=Zt*Ma}}}let yn=re*Ie+ne*$e+ie*et+Q*Ne;A[yn]=Ve}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var BD={kernelName:fs,backendName:"cpu",kernelFunc:WD};function VD(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;ve([r,s],"conv3d");let d=C.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:A}=d,y=A.front,g=A.left,x=A.top,w=new Ot(d.outShape,r.dtype),b=n.data.get(r.dataId).values,v=n.data.get(s.dataId).values,N=w.values,T=k.computeStrides(r.shape),R=k.computeStrides(s.shape);for(let $=0;$<d.batchSize;++$){let z=$*T[0],P=$*w.strides[0];for(let V=0;V<d.outDepth;++V){let j=P+V*w.strides[1],U=V*d.strideDepth-y;for(let X=0;X<u;++X){let G=U+X*h;if(G<0||G>=d.inDepth)continue;let ee=X*R[0],Y=z+G*T[1];for(let re=0;re<d.outHeight;++re){let ne=j+re*w.strides[2],ie=re*d.strideHeight-x;for(let Q=0;Q<p;++Q){let de=ie+Q*m;if(de<0||de>=d.inHeight)continue;let oe=ee+Q*R[1],ye=Y+de*T[2];for(let ce=0;ce<d.outWidth;++ce){let Ie=ne+ce*d.outChannels,Ne=ce*d.strideWidth-g;for(let 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Ot(p.inShape,"float32"),h=c.values,[m,f,A,y]=c.strides,g=n.data.get(r.dataId).values,[x,w,b,v]=d,N=n.data.get(s.dataId).values,[T,R,$,z]=u,{batchSize:P,filterDepth:V,filterHeight:j,filterWidth:U,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:re,outDepth:ne,outHeight:ie,outWidth:Q,strideDepth:de,strideHeight:oe,strideWidth:ye}=p,ce=V-1-p.padInfo.front,Ie=j-1-p.padInfo.top,Ne=U-1-p.padInfo.left;for(let $e=0;$e<P;++$e)for(let ze=0;ze<X;++ze)for(let De=0;De<G;++De){let Qe=De-ce,et=Math.max(0,Math.ceil(Qe/de)),rt=Math.min(ne,(V+Qe)/de);for(let Xe=0;Xe<ee;++Xe){let pt=Xe-Ie,Ve=Math.max(0,Math.ceil(pt/oe)),yn=Math.min(ie,(j+pt)/oe);for(let xt=0;xt<Y;++xt){let jn=xt-Ne,Kt=Math.max(0,Math.ceil(jn/ye)),gn=Math.min(Q,(U+jn)/ye),Un=0;for(let Mn=et;Mn<rt;++Mn){let sn=Mn*de-Qe;for(let Zt=Ve;Zt<yn;++Zt){let Ma=Zt*oe-pt;for(let ta=Kt;ta<gn;++ta){let na=ta*ye-jn,fr=x*$e+w*Mn+b*Zt+v*ta,Ka=T*(V-1-sn)+R*(j-1-Ma)+$*(U-1-na)+z*ze;for(let mr=0;mr<re;++mr){let 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ee=Math.floor(G),Y=Math.ceil(G),re=G-ee;for(let ne=0;ne<A;ne++){let ie=A>1?$*(c-1)+ne*U:.5*($+P)*(c-1);if(ie<0||ie>c-1){for(let ye=0;ye<h;ye++){let ce=ye+ne*v[2]+X*v[1]+N*v[0];y.values[ce]=d}continue}let Q=Math.floor(ie),de=Math.ceil(ie),oe=ie-Q;for(let ye=0;ye<h;ye++){let ce=ye+Q*b[2]+ee*b[1]+V*b[0],Ie=w[ce];ce=ye+de*b[2]+ee*b[1]+V*b[0];let Ne=w[ce];ce=ye+Q*b[2]+Y*b[1]+V*b[0];let $e=w[ce];ce=ye+de*b[2]+Y*b[1]+V*b[0];let ze=w[ce],De=Ie+(Ne-Ie)*oe,Qe=$e+(ze-$e)*oe;ce=ye+ne*v[2]+X*v[1]+N*v[0],y.values[ce]=De+(Qe-De)*re}}}else for(let ee=0;ee<A;++ee){let Y=A>1?$*(c-1)+ee*U:.5*($+P)*(c-1);if(Y<0||Y>c-1){for(let ie=0;ie<h;ie++){let Q=ie+ee*v[2]+X*v[1]+N*v[0];y.values[Q]=d}continue}let re=Math.round(Y),ne=Math.round(G);for(let ie=0;ie<h;ie++){let Q=ie+re*b[2]+ne*b[1]+V*b[0],de=ie+ee*v[2]+X*v[1]+N*v[0];y.values[de]=w[Q]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var QD={kernelName:ho,backendName:"cpu",kernelFunc:JD};function eO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ve(r,"cumsum");let l=C.getAxesPermutation([s],r.shape.length),d=r;l!=null&&(d=Zn({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=C.getInnerMostAxes(1,r.shape.length)[0];if(u!==d.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${d.shape.length-1} but got axis=${u}`);let p=ia(d.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(d.shape),p),h=n.data.get(d.dataId).values,m=d.shape[d.shape.length-1],f=o?(y,g)=>y+m-g-1:(y,g)=>y+g;for(let y=0;y<h.length;y+=m)for(let g=0;g<m;g++){let x=f(y,g);if(g===0)c[x]=i?0:h[x];else{let w=f(y,g-1);c[x]=i?h[w]+c[w]:h[x]+c[w]}}let A=n.makeTensorInfo(d.shape,p,c);if(l!=null){let y=C.getUndoAxesPermutation(l),g=Zn({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(d),g}return A}var tO={kernelName:As,backendName:"cpu",kernelFunc:eO};function nO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,u=UA(l,d,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),d=n.bufferSync(s),u=S3(l,d,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var aO={kernelName:Op,backendName:"cpu",kernelFunc:nO};function rO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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oO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,filterShape:u}=a;ve([r,s],"depthwiseConv2dNativeBackpropFilter");let p=C.computeConv2DInfo(r.shape,u,i,o,l,d,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=p,A=new Ot(p.filterShape,"float32"),y=p.padInfo.left,g=p.padInfo.top,x=p.outChannels/p.inChannels,w=n.data.get(r.dataId).values,b=new Ot(r.shape,r.dtype,w),v=n.data.get(s.dataId).values,N=new Ot(s.shape,s.dtype,v);for(let T=0;T<m;++T){let R=Math.max(0,Math.ceil((g-T)/c)),$=Math.min(p.outHeight,(p.inHeight+g-T)/c);for(let z=0;z<f;++z){let P=Math.max(0,Math.ceil((y-z)/h)),V=Math.min(p.outWidth,(p.inWidth+y-z)/h);for(let j=0;j<p.outChannels;++j){let U=Math.trunc(j/x),X=j%x,G=0;for(let ee=0;ee<p.batchSize;++ee)for(let Y=R;Y<$;++Y){let re=T+Y*c-g;for(let ne=P;ne<V;++ne){let ie=z+ne*h-y;G+=b.get(ee,re,ie,U)*N.get(ee,Y,ne,j)}}A.set(G,T,z,U,X)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var lO={kernelName:zp,backendName:"cpu",kernelFunc:oO};function uO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,inputShape:u}=a;ve([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=C.computeConv2DInfo(u,s.shape,i,o,l,d,!0),m=new Ot(h.inShape,"float32"),f=m.values,[A,y,g]=m.strides,x=n.data.get(r.dataId).values,[w,b,v]=p,N=n.data.get(s.dataId).values,[T,R,$]=c,{batchSize:z,filterHeight:P,filterWidth:V,inChannels:j,inHeight:U,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:re,strideWidth:ne}=h,ie=P-1-h.padInfo.top,Q=V-1-h.padInfo.left,de=G/j;for(let oe=0;oe<z;++oe)for(let ye=0;ye<j;++ye)for(let ce=0;ce<U;++ce){let Ie=ce-ie,Ne=Math.max(0,Math.ceil(Ie/re)),$e=Math.min(ee,(P+Ie)/re);for(let ze=0;ze<X;++ze){let De=ze-Q,Qe=Math.max(0,Math.ceil(De/ne)),et=Math.min(Y,(V+De)/ne),rt=0;for(let Xe=Ne;Xe<$e;++Xe){let pt=Xe*re-Ie;for(let Ve=Qe;Ve<et;++Ve){let yn=Ve*ne-De,xt=w*oe+b*Xe+v*Ve,jn=T*(P-1-pt)+R*(V-1-yn)+$*ye;for(let Kt=0;Kt<de;++Kt){let gn=ye*de+Kt,Un=x[xt+gn],Mn=N[jn+Kt];rt+=Un*Mn}}}f[A*oe+y*ce+g*ze+ye]=rt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var dO={kernelName:_p,backendName:"cpu",kernelFunc:uO};function pO(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=We([r,r],a.dtype),o=i.values;for(let d=0;d<s.length;d++)o[d*r+d]=s[d];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var cO={kernelName:Pp,backendName:"cpu",kernelFunc:pO},hO={kernelName:vu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,d=l.data.get(a.dataId).values,u=a.shape.length,p=l.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:A,outHeight:y,outWidth:g,padInfo:x,strideHeight:w,strideWidth:b,filterHeight:v,filterWidth:N,dilationHeight:T,dilationWidth:R,outShape:$}=C.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),z=k.sizeFromShape($),P=$.length,V=k.getArrayFromDType(a.dtype,z);for(let j=0;j<h;++j)for(let U=0;U<y;++U){let X=U*w-x.top;for(let G=0;G<g;++G){let ee=G*b-x.left;for(let Y=0;Y<A;++Y){let re=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<v;++ie){let Q=X+ie*T;if(Q>=0&&Q<m)for(let de=0;de<N;++de){let oe=ee+de*R;if(oe>=0&&oe<f){let ye=k.locToIndex([j,Q,oe,Y],u,k.computeStrides(a.shape)),ce=k.locToIndex([ie,de,Y],c,k.computeStrides(r.shape)),Ie=d[ye]+p[ce];Ie>re&&(re=Ie)}}}let ne=k.locToIndex([j,U,G,Y],P,k.computeStrides($));V[ne]=re}}}return{dataId:l.write(k.toTypedArray(V,a.dtype),$,a.dtype),shape:$,dtype:a.dtype}}},fO={kernelName:Wp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,d=t,u=k.toNestedArray(a.shape,d.data.get(a.dataId).values),p=k.toNestedArray(r.shape,d.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:x,strideWidth:w,filterHeight:b,filterWidth:v,dilationHeight:N,dilationWidth:T,outShape:R}=C.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===R.length,()=>`Error in ${Wp}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let $=k.toNestedArray(R,d.data.get(s.dataId).values),z=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let P=0;P<c;++P)for(let V=0;V<A;++V){let j=V*x-g.top;for(let U=0;U<y;++U){let X=U*w-g.left;for(let G=0;G<f;++G){let ee=Number.MIN_SAFE_INTEGER,Y=0,re=0;for(let ne=0;ne<b;++ne){let ie=j+ne*N;if(ie>=0&&ie<h)for(let Q=0;Q<v;++Q){let de=X+Q*T;if(de>=0&&de<m){let oe=u[P][ie][de][G]+p[ne][Q][G];oe>ee&&(ee=oe,Y=ne,re=Q)}}}z[Y][re][G]+=$[P][V][U][G]}}}return{dataId:d.write(k.toTypedArray(z,a.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},mO={kernelName:Lp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,d=t,u=k.toNestedArray(a.shape,d.data.get(a.dataId).values),p=k.toNestedArray(r.shape,d.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:x,strideWidth:w,filterHeight:b,filterWidth:v,dilationHeight:N,dilationWidth:T,outShape:R}=C.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);k.assert(s.rank===R.length,()=>`Error in ${Lp}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);let 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l=o.shape.length,d=k.parseAxisParam(s,o.shape),u=C.getAxesPermutation(d,l),p=d,c=o;u!=null&&(c=Zn({inputs:{x:o},backend:n,attrs:{perm:u}}),p=C.getInnerMostAxes(p.length,l)),C.assertAxesAreInnerMostDims("sum",p,c.shape.length);let[h,m]=C.computeOutAndReduceShapes(c.shape,p),f=C.upcastType(c.dtype,"int32"),A=rh(n,h,f),y=k.sizeFromShape(m),g=n.data.get(A.dataId).values,x=n.data.get(c.dataId).values;for(let w=0;w<g.length;++w){let b=w*y,v=0;for(let N=0;N<y;++N)v+=x[b+N];g[w]=v}if(i){let w=C.expandShapeToKeepDim(A.shape,d),b=A;A=ht({inputs:{x:A},backend:n,attrs:{shape:w}}),n.disposeIntermediateTensorInfo(b)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(c),A}var AO={kernelName:Xs,backendName:"cpu",kernelFunc:od};function 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MO={kernelName:go,backendName:"cpu",kernelFunc:ih},FO=Mt((e,t)=>e/t),QA=Ht(gs,FO),e1={kernelName:gs,backendName:"cpu",kernelFunc:QA};function d7(e,t,n){let a=e.shape,r=a[0],s=a[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,d=[r,s],u=k.sizeFromShape(d),p=k.getTypedArrayFromDType("float32",u),c=k.getTypedArrayFromDType("float32",u);for(let A=0;A<r;A++){let y=bi({inputs:{x:o},backend:n,attrs:{begin:[A,0],size:[1,s]}}),g=bi({inputs:{x:l},backend:n,attrs:{begin:[A,0],size:[1,s]}}),x=Pn({inputs:{real:y,imag:g},backend:n}),{real:w,imag:b}=$O(x,t,n),v=C.mergeRealAndImagArrays(w,b);for(let N=0;N<s;N++){let T=C.getComplexWithIndex(v,N);p[A*s+N]=T.real,c[A*s+N]=T.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(d,"float32",p),m=n.makeTensorInfo(d,"float32",c),f=Pn({inputs:{real:h,imag:m},backend:n});return 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ki[e]},!1),e===1?t.getContext("webgl",i1)||t.getContext("experimental-webgl",i1):t.getContext("webgl2",i1)}var cd;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(cd||(cd={}));var Jn;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Jn||(Jn={}));var en;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(en||(en={}));function hd(e,t){return[t,e]}function jP(e,t){return e*t}function fd(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Rl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function UP(e,t){let[n,a]=Rl(e,t);return n*a*4}function o1(e,t){let n=e,a,r,s,i,o,l,d,u,p,c;return 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e.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function ud(e,t){return lr(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function w7(e,t){let n=lr(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function k7(e,t){let n=lr(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw XP(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var KP=/ERROR: [0-9]+:([0-9]+):/g;function XP(e,t){let n=KP.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let a=+n[1],r=e.split(`
|
|
`),s=r.length.toString().length+2,i=r.map((p,c)=>k.rightPad((c+1).toString(),s)+p),o=0;for(let p=0;p<i.length;p++)o=Math.max(i[p].length,o);let l=i.slice(0,a-1),d=i.slice(a-1,a),u=i.slice(a);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${k.rightPad(d[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
|
|
`))}function I7(e){return lr(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function S7(e,t){if(xe(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function lh(e,t){if(xe(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function N7(e,t){let n=lr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function T7(e,t){let n=lr(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function zP(){return J().getNumber("WEBGL_VERSION")===2?1:4}function E7(e){return lr(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function C7(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let a=`[${e}x${t}]`;throw new Error("Requested texture size "+a+" is invalid.")}if(e>n||t>n){let a=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+a+" greater than WebGL maximum on this browser / GPU "+r+".")}}function R7(e){return lr(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function a1(e,t,n,a,r,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,a)),xe(e,()=>e.vertexAttribPointer(o,r,e.FLOAT,!1,s,i)),xe(e,()=>e.enableVertexAttribArray(o)),!0)}function M7(e,t,n){j7(e,n),xe(e,()=>e.activeTexture(e.TEXTURE0+n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function _P(e,t){j7(e,t),xe(e,()=>e.activeTexture(e.TEXTURE0+t)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function F7(e,t,n){return lr(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function $7(e,t,n){return e.getUniformLocation(t,n)}function D7(e,t,n,a){xe(e,()=>M7(e,t,a)),xe(e,()=>e.uniform1i(n,a))}function PP(e){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),xe(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function uh(e,t,n){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function r1(e,t){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function dd(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+O7(e,t))}function O7(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function lr(e,t,n){let a=xe(e,()=>t());if(a==null)throw new Error(n);return a}function j7(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function vi(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function wi(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function dh(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[vi(e),...wi(e)]),t}function z7(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,s)=>s>=e.length-2?k.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let a=k.sizeFromShape(e);if(e.length<=1&&a<=n)return[1,a];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=vi(e),s=2,i=2;return e.length&&([s,i]=wi(e)),a=r*(s/2)*(i/2),k.sizeToSquarishShape(a).map(o=>o*2)}return k.sizeToSquarishShape(a)}function ch(e){return e%2==0}function pd(e,t){if(e=e.slice(-2),t=t.slice(-2),k.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],a=t.slice(-1)[0];if(n===a||ch(n)&&ch(a)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&ch(e[0])&&ch(t[0])}var hh,fh;function _7(e){if(hh==null){let t=Va(e);hh=t.getParameter(t.MAX_TEXTURE_SIZE)}return hh}function LP(){hh=null}function WP(){fh=null}function P7(e){if(fh==null){let t=Va(e);fh=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,fh)}function L7(e){if(e===0)return 0;let t,n=Va(e);return Yn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Yn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Yn(e,t){return e.getExtension(t)!=null}function s1(e){try{if(Va(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function W7(e){if(e===0)return!1;let t=Va(e);if(e===1){if(!Yn(t,"OES_texture_float"))return!1}else if(!Yn(t,"EXT_color_buffer_float"))return!1;return l1(t)}function B7(e){if(e===0)return!1;let t=Va(e);if(e===1){if(!Yn(t,"OES_texture_float")||!Yn(t,"WEBGL_color_buffer_float"))return!1}else{if(Yn(t,"EXT_color_buffer_float"))return l1(t);let n="EXT_color_buffer_half_float";if(Yn(t,n)){let a=t.getExtension(n);return ZP(t,a)}return!1}return l1(t)}function l1(e){let t=o1(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function ZP(e,t){let n=o1(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function V7(e){return e!==2?!1:Va(e).fenceSync!=null}function Cl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Me=J();Me.registerFlag("HAS_WEBGL",()=>Me.getNumber("WEBGL_VERSION")>0);Me.registerFlag("WEBGL_VERSION",()=>s1(2)?2:s1(1)?1:0);Me.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Me.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Me.get("WEBGL_VERSION")===2);Me.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Me.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Me.registerFlag("WEBGL_PACK",()=>Me.getBool("HAS_WEBGL"));Me.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_CLIP",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_REDUCE",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_LAZILY_UNPACK",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_CONV_IM2COL",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>_7(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>P7(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Me.getNumber("WEBGL_VERSION");return e===0?0:L7(e)});Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Me.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Wu.isMobile());Me.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>W7(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Me.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Me.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Me.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>B7(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>V7(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Me.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Me.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}.`)});Me.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Wu.isMobile()&&Me.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}.`)});function pn(){let e,t,n,a,r,s,i,o,l,d;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
|
|
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="",d=`
|
|
#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",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
|
|
#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));
|
|
}
|
|
`,d=`
|
|
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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:d}}function Ii(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function u1(e){let t=k.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}var U7=`
|
|
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;
|
|
}
|
|
`,YP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=cd.DENSE;let t=fd(e),n=pn();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Ii(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[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);
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},JP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=cd.DENSE;let t=fd(e),n=pn();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Ii(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[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));
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},QP=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Jn.DOWNLOAD;let t=pn();this.outputShape=e,this.userCode=`
|
|
${U7}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},eL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jn.DOWNLOAD;let t=pn();this.outputShape=e,this.userCode=`
|
|
${U7}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},tL=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=pn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${u1(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / ${s};
|
|
int c = imod(flatIndex, ${s});
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
|
|
vec4 values = ${a.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];
|
|
}
|
|
|
|
${a.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}},nL=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=pn(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let d=0;d<=1;d++){let u=l*2+d;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${d} < ${e[2]}) {
|
|
localCoords[2] += ${d};
|
|
if(localCoords[1] + ${l} < ${e[1]}) {
|
|
localCoords[1] += ${l};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${s};
|
|
c = imod(flatIndex, ${s});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
|
|
values = ${a.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${u}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${u}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${u}] = values[2];
|
|
} else {
|
|
result[${u}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${u1(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${a.output} = ${o};
|
|
}
|
|
`}},H7={};Fe(H7,{bindVertexProgramAttributeStreams:()=>ev,createBufferFromOutputTexture:()=>av,createFloat16MatrixTexture:()=>Z7,createFloat16PackedMatrixTexture:()=>Q7,createFloat32MatrixTexture:()=>K7,createIndexBuffer:()=>X7,createPackedMatrixTexture:()=>J7,createUnsignedBytesMatrixTexture:()=>Y7,createVertexBuffer:()=>q7,createVertexShader:()=>G7,downloadByteEncodedFloatMatrixFromOutputTexture:()=>sv,downloadFloat32MatrixFromBuffer:()=>rv,downloadMatrixFromPackedOutputTexture:()=>ov,downloadPackedMatrixFromBuffer:()=>iv,getInternalFormatForFloat16MatrixTexture:()=>p1,getInternalFormatForFloat16PackedMatrixTexture:()=>f1,getInternalFormatForFloat32MatrixTexture:()=>d1,getInternalFormatForPackedMatrixTexture:()=>h1,getInternalFormatForUnsignedBytesMatrixTexture:()=>c1,uploadDenseMatrixToTexture:()=>tv,uploadPixelDataToTexture:()=>nv});function G7(e){let t=pn(),n=`${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 w7(e,n)}function q7(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 N7(e,t)}function X7(e){let t=new Uint16Array([0,1,2,2,1,3]);return T7(e,t)}function md(e,t,n,a,r,s){C7(t,n);let i=E7(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function d1(e){return e.internalFormatFloat}function K7(e,t,n,a){let[r,s]=hd(t,n);return md(e,r,s,d1(a),a.textureFormatFloat,e.FLOAT)}function p1(e){return e.internalFormatHalfFloat}function Z7(e,t,n,a){let[r,s]=hd(t,n);return md(e,r,s,p1(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function c1(e){return e.downloadTextureFormat}function Y7(e,t,n,a){let[r,s]=hd(t,n);return md(e,r,s,c1(a),e.RGBA,e.UNSIGNED_BYTE)}function h1(e){return e.internalFormatPackedFloat}function J7(e,t,n,a){let[r,s]=Rl(t,n);return md(e,r,s,h1(a),e.RGBA,e.FLOAT)}function f1(e){return e.internalFormatPackedHalfFloat}function Q7(e,t,n,a){let[r,s]=Rl(t,n);return md(e,r,s,f1(a),e.RGBA,a.textureTypeHalfFloat)}function ev(e,t,n){let a=0,r=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),a1(e,t,"clipSpacePos",n,3,s,a)&&a1(e,t,"uv",n,2,s,r)}function tv(e,t,n,a,r,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function nv(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function av(e,t,n,a){let r=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function rv(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function sv(e,t,n,a){let[r,s]=hd(t,n),i=4,o=new Uint8Array(jP(t*n,i));return xe(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function iv(e,t,n,a,r,s,i,o){let l=e,d=new Float32Array(UP(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,d),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),d}function ov(e,t,n){let a=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var mh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,ph(t,e)):this.gl=Va(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ud(this.gl,r),Yn(this.gl,s))this.textureHalfFloatExtension=ud(this.gl,s);else if(J().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),Yn(this.gl,a))this.colorBufferHalfFloatExtension=ud(this.gl,a);else if(J().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",Yn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Yn(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=q7(this.gl),this.indexBuffer=X7(this.gl),this.framebuffer=R7(this.gl),this.textureConfig=o1(this.gl,this.textureHalfFloatExtension)}get debug(){return J().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;xe(e,()=>e.finish()),xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.deleteFramebuffer(this.framebuffer)),xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),xe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),K7(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Z7(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Y7(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),nv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),tv(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Q7(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),J7(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(r1(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>sv(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return iv(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return rv(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=av(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>ov(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=k7(t,e);this.vertexShader==null&&(this.vertexShader=G7(t));let a=I7(t);return xe(t,()=>t.attachShader(a,this.vertexShader)),xe(t,()=>t.attachShader(a,n)),S7(t,a),this.debug&&lh(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=ev(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&lh(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?F7(this.gl,e,t):$7(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(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(),D7(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Rl(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&lh(this.gl,this.program),dd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ud(this.gl,J().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(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().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,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=aL(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)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),uh(this.gl,e,this.framebuffer),this.debug&&dd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(uh(this.gl,this.outputTexture,this.framebuffer),this.debug&&dd(this.gl)):r1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;uh(a,e,this.framebuffer),this.debug&&dd(a),this.outputTexture=e,xe(a,()=>a.viewport(0,0,t,n)),xe(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,a))}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 aL(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:lv}=C;function cL(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
|
|
`),i=e.map(h=>rL(h,t,a)).join(`
|
|
`),o=t.texShape,l=pn(),d=oL(l),u,p,c=dL(l);return t.isPacked?(u=sL(t.logicalShape,o),p=uL(l)):(u=iL(t.logicalShape,o),p=lL(l)),a&&(c+=pL),[c,d,p,s,u,i,n].join(`
|
|
`)}function Ml(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return hL(e);case 1:return fL(e);case 2:return mL(e);case 3:return AL(e);case 4:return yL(e);case 5:return gL(e);case 6:return xL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function uv(e){switch(e.shapeInfo.logicalShape.length){case 0:return bL(e);case 1:return vL(e);case 2:return wL(e);case 3:return kL(e);default:return IL(e)}}function rL(e,t,n=!1){let a="";n?a+=uv(e):a+=Ml(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=SL(e,t):a+=NL(e,t)),a}function sL(e,t){switch(e.length){case 0:return dv();case 1:return TL(e,t);case 2:return RL(e,t);case 3:return EL(e,t);default:return CL(e,t)}}function iL(e,t){switch(e.length){case 0:return dv();case 1:return ML(e,t);case 2:return zL(e,t);case 3:return FL(e,t);case 4:return $L(e,t);case 5:return DL(e,t);case 6:return OL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function oL(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function lL(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function uL(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function dL(e){return`${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);
|
|
}
|
|
|
|
${_L}
|
|
${PL}
|
|
${LL}
|
|
`}var _L=`
|
|
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);
|
|
}
|
|
`,PL=`
|
|
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);
|
|
}
|
|
`,LL=`
|
|
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);
|
|
}
|
|
`,pL=`
|
|
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 dv(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function TL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${n[1]}.0);
|
|
}
|
|
`:n[1]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${n[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
|
|
}
|
|
`}function ML(e,t){return t[0]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function EL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function FL(e,t){let n=Ii(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function CL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
|
|
int b${l} = index / ${s};
|
|
index -= b${l} * ${s};
|
|
`+i,o=`b${l}, `+o;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec${e.length}(${o});
|
|
}
|
|
`}function $L(e,t){let n=Ii(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function DL(e,t){let n=Ii(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function OL(e,t){let n=Ii(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function RL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let a=Math.ceil(e[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function zL(e,t){return k.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Si(e){return`offset${e}`}function bL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=pn();return`
|
|
vec4 ${n}() {
|
|
return ${a.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function hL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
|
|
float ${n}() {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let[s,i]=e.shapeInfo.texShape,o=Si(t);return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function vL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=pn();return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${r[0]}, ${r[1]}, index);
|
|
return ${s.texture2D}(${t}, uv);
|
|
}
|
|
`}function fL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Fl(e)}
|
|
}
|
|
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
|
|
float ${n}(int index) {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let i=Si(t);return s===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:r===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function wL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=pn();if(r!=null&&k.arraysEqual(t,r))return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
|
|
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],d=Math.ceil(t[1]/2);return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${d}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function mL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],c=r[1];return`
|
|
float ${a}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=$l(e,o),c=["row","col"];return`
|
|
${Ml(p)}
|
|
float ${a}(int row, int col) {
|
|
return ${a}(${Dl(c,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
|
|
${Fl(e)}
|
|
}
|
|
`;let l=r[0],d=r[1],u=Si(n);return d===1?`
|
|
float ${a}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:l===1?`
|
|
float ${a}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${t[1]} + col + ${u};
|
|
vec2 uv = uvFromFlat(${l}, ${d}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function kL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),c=[1,2],h=$l(e,p),m=["b","row","col"];return`
|
|
${uv(h)}
|
|
vec4 ${a}(int b, int row, int col) {
|
|
return ${a}(${Dl(m,c)});
|
|
}
|
|
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),d=l*Math.ceil(t[1]/2),u=pn();return`
|
|
vec4 ${a}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${d}, ${l}, b, row, col);
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`}function AL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=$l(e,l),f=["row","col","depth"];return`
|
|
${Ml(m)}
|
|
float ${a}(int row, int col, int depth) {
|
|
return ${a}(${Dl(f,o)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${r}, ${s}, 1)));
|
|
${Fl(e)}
|
|
}
|
|
`;let d=e.shapeInfo.texShape,u=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===r&&c==null)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(p===s&&c==null)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let h=Si(n);return`
|
|
float ${a}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${r} + col * ${s} + depth + ${h};
|
|
vec2 uv = uvFromFlat(${u}, ${p}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function IL(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],d=Math.ceil(t[n-1]/2),u=d*Math.ceil(t[n-2]/2),p="int b, int row, int col",c=`b * ${u} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],c=`b${m} * ${u} + `+c;let h=pn();return`
|
|
vec4 ${r}(${p}) {
|
|
int index = ${c};
|
|
int texR = index / ${l};
|
|
int texC = index - texR * ${l};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
|
|
return ${h.texture2D}(${a}, uv);
|
|
}
|
|
`}function yL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=$l(e,o),f=["row","col","depth","depth2"];return`
|
|
${Ml(m)}
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
return ${a}(${Dl(f,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${s}, ${r}, 1)));
|
|
${Fl(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,p=u[0],c=u[1];if(c===i&&d==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(c===r&&d==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${t[1]*t[2]}, ${t[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let h=Si(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${s} +
|
|
depth * ${r} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${c}, index + ${h});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function gL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:d}=k.squeezeShape(t);if(l.length<t.length){let f=$l(e,l),A=["row","col","depth","depth2","depth3"];return`
|
|
${Ml(f)}
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${a}(${Dl(A,d)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${r})) +
|
|
depth3;
|
|
${Fl(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&u==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&u==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${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, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=Si(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} + depth * ${s} +
|
|
depth2 * ${r} + depth3 + ${m};
|
|
vec2 uv = uvFromFlat(${c}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function xL(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let A=$l(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Ml(A)}
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${a}(${Dl(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,l=t[3]*o,d=t[2]*l,u=t[1]*d;if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${d}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Fl(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===u&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${d}, ${l}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(m===i&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=Si(n);return`
|
|
float ${a}(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 * ${u} + col * ${d} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
|
|
vec2 uv = uvFromFlat(${h}, ${m}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Fl(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function SL(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=lv(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),d=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${p[A+d]} = 0;`).join(`
|
|
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((A,y)=>`coords.${p[y+d]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!f)i===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(A)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${a}(${c});
|
|
${h}
|
|
}
|
|
`}function NL(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let d=lt(l),u=lv(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&u.length>=1?c="coords = 0;":c=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
|
|
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,A)=>`coords.${h[A+p]}`).join(", "),`
|
|
float ${r}() {
|
|
${d} coords = getOutputCoords();
|
|
${c}
|
|
return get${a}(${m});
|
|
}
|
|
`}function lt(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 $l(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Dl(e,t){return t.map(n=>e[n]).join(", ")}function WL(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},l=cL(s,o,r,t.packedInputs),d=e.createProgram(l),u=null,p=e.getUniformLocation(d,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(d,"INFINITY",!1));let c={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;c[m]=e.getUniformLocation(d,m,f),c[`offset${m}`]=e.getUniformLocation(d,`offset${m}`,f)}return{program:t,source:l,webGLProgram:d,uniformLocations:c,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:p}}function pv(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,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function BL(e,t,n,a,r){pv(t.inShapeInfos,n),pv([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let d=t.program.variableNames[l],u=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`];if(u!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let c=o.uniformValues;c instanceof Float32Array||(c=new Float32Array(c)),e.gl.uniform1fv(u,c)}return}o.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function VL(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:jL,bincountImpl:cv,bincountReduceImpl:UL,ceilImpl:HL,concatImpl:GL,expImpl:qL,expm1Impl:XL,floorImpl:KL,gatherV2Impl:ZL,greaterImpl:YL,lessImpl:JL,linSpaceImpl:QL,logImpl:eW,maxImpl:tW,maximumImpl:nW,minimumImpl:aW,multiplyImpl:rW,negImpl:sW,prodImpl:iW,rangeImpl:oW,rsqrtImpl:lW,simpleAbsImpl:hv,sliceImpl:uW,sparseFillEmptyRowsImpl:dW,sparseReshapeImpl:pW,stridedSliceImpl:cW,subImpl:hW,tileImpl:fW,topKImpl:mW,transposeImpl:m1,uniqueImpl:AW}=jA;function fv(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function cn(e,t){return t===1?[e]:fv(e,t)}function yW(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var vW=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),a=lt(t),r=gW(t,e,n),s=xW(t,e[e.length-1],e[e.length-2],n),i=bW(e,n);this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function wW(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function gW(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function xW(e,t,n,a){if(e===1)return"";let r=a.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 bW(e,t){let n=e.length,a=wW(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${a[0]}),
|
|
cEdge ? 0. : getA(${a[1]}),
|
|
rEdge ? 0. : getA(${a[2]}),
|
|
rEdge || cEdge ? 0. : getA(${a[3]})`}var mv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${a}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${a>0?"}":""}
|
|
`}this.userCode=`
|
|
${kW(t)}
|
|
${u1(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function kW(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${Ii(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var IW=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=yv(t,n),r=gv(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=Av(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===en.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===en.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===en.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===en.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===en.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=yv(n,a),s=gv(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Av(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],d=l.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(d,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 SW(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function Av(e,t,n,a,r){let s=NW(t,a),i;if(r){let[l,d]=Rl(e[0],e[1]);i=l*d}else{let[l,d]=hd(e[0],e[1]);i=l*d}let o=SW(n,s);return i*o}function NW(e,t){switch(e){case en.PACKED_2X2_FLOAT32:return h1(t);case en.PACKED_2X2_FLOAT16:return f1(t);case en.UNPACKED_FLOAT32:return d1(t);case en.UNPACKED_FLOAT16:return p1(t);case en.PACKED_4X1_UNSIGNED_BYTE:return c1(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function TW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?en.PACKED_2X2_FLOAT32:en.UNPACKED_FLOAT32:e?en.PACKED_2X2_FLOAT16:en.UNPACKED_FLOAT16}function yv(e,t){if(e===Jn.UPLOAD)return en.PACKED_2X2_FLOAT32;if(e===Jn.RENDER||e==null)return TW(t);if(e===Jn.DOWNLOAD||e===Jn.PIXELS)return en.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function gv(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Lr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},ba="if (isnan(x)) return x;",EW="return x;",xv="return abs(x);",CW="return (x >= 0.0) ? x : (exp(x) - 1.0);",RW=ba+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,MW=ba+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Ah="return x;",FW="return 1.0 / (1.0 + exp(-1.0 * x));",$W="return x;",DW=`
|
|
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;
|
|
`,OW=`
|
|
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;
|
|
`,zW=`
|
|
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;
|
|
`,_W="return 1.0 / (1.0 + exp(-1.0 * x));",Ol=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},PW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=cn("rc",t),a=lt(t),r=yW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},LW=Wa.whereImpl,WW=1e-7,BW=1e-4,A1={};function VW(e){return e in A1||(A1[e]={}),A1[e]}var jW=128,UW=600;function HW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*UW/1024/1024}var zl=class extends hu{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,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Va(J().getNumber("WEBGL_VERSION"));this.binaryCache=VW(J().getNumber("WEBGL_VERSION")),this.gpgpu=new mh(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 IW(this.gpgpu),this.numMBBeforeWarning=HW(),this.texData=new Ip(this,nr())}nextDataId(){return zl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().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 a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:Jn.UPLOAD,refCount:1}),a}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,a,r){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:Jn.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new Ol(i,Ah):p=new Lr(i,Ah);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,d;l&&(d=k.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);u=C.mergeRealAndImagArrays(p,c)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-d),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Ol(a,Ah):h=new Lr(a,Ah);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().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,d;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);let h=this.texData.get(d.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...fd(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];u=C.mergeRealAndImagArrays(m,f)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}d!=null&&this.disposeIntermediateTensorInfo(d);let p=this.convertAndCacheOnCPU(e,u),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&nr().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!b7(n))throw J().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...fd(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=J().getBool("WEBGL_PACK")&&a===!0,i=s?dh(t):t,o=s?new eL(i):new QP(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),d=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,d)=>({name:s[d],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(J().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:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=jW){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return LW(e.shape,t)}packedUnaryOp(e,t,n){let a=new Ol(e.shape,t),r=this.compileAndRun(a,[e],n);return nr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=hv(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,xv,e.dtype);let t=new Lr(e.shape,xv),n=this.compileAndRun(t,[e]);return nr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return nr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new PW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[vi(e.shape),...wi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[vi(t),...wi(t)],s=new mv(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=dh(a),i;n?i=new JP(s):i=new YP(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:l.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===cd.DENSE){let f=fd(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!pd(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let d={shape:s.shape,texData:i,isUniform:!1},u=VL(e,l,d),p=this.getAndSaveBinary(u,()=>WL(this.gpgpu,e,l,d)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),BL(this.gpgpu,p,l,d,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=W(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?WW:BW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,d;l&&(d=k.now());let u=t.texShape;if(u==null&&(u=z7(n,o),t.texShape=u),r!=null){let p=dh(n),c,h=u[1],m=u[0],f=r instanceof Uint8Array;o?([h,m]=Rl(u[0],u[1]),c=new nL(p,[m,h],f)):c=new tL(p,[m,h],f);let A=this.makeTensorInfo([m,h],a);f?this.texData.get(A.dataId).usage=Jn.PIXELS:this.texData.get(A.dataId).usage=Jn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),h,m,r);let y=!0,g=this.runWebGLProgram(c,[A],a,null,y),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-d)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=GW(t,a)),n.values}acquireTexture(e,t,n,a){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,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};zl.nextDataId=0;function GW(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var bv="3.6.0";function vv(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Wu.isBrowser()&&dl("webgl",()=>new zl,2);var qW={forceHalfFloat:vv},wv=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,_l=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},yh=`
|
|
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;
|
|
`,Ad=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${lt(r)} coords = getOutputCoords();
|
|
`,r===1)s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=cn("coords",r);s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Ln(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var XW={kernelName:Is,backendName:"webgl",kernelFunc:Ln};function Wr(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Ln({inputs:{x:a},backend:n}),l=Ln({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var KW={kernelName:Mp,backendName:"webgl",kernelFunc:Wr},kv="return (a < 0.) ? b * a : a;",Iv=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function ZW(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ad(Iv,r.shape,i.shape):new _l(kv,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),l}var YW={kernelName:Ss,backendName:"webgl",kernelFunc:ZW},Sv="return (a < 0.) ? b * a : a;",Nv=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function JW(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ad(Nv,a.shape,r.shape):new _l(Sv,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var QW={kernelName:Ps,backendName:"webgl",kernelFunc:JW},Tv="if (isnan(x)) return x;",eB=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,tB=`
|
|
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 qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let d=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return d?u=new Ol(i.shape,t):u=new Lr(i.shape,e),o.runWebGLProgram(u,[i],l)}}function tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:d}=i,u=o;if(a&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(d.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,b]=x,v={dataId:w.dataId,dtype:w.dtype,shape:l.shape},N={dataId:b.dataId,dtype:b.dtype,shape:d.shape},T=new _l(e,l.shape,d.shape);return u.runWebGLProgram(T,[v,N],ia(w.dtype,b.dtype))}),g=Wr({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let p=s||ia(l.dtype,d.dtype);if(u.shouldExecuteOnCPU([l,d])&&r!=null){let m=u.texData.get(l.dataId),f=u.texData.get(d.dataId),[A,y]=r(l.shape,d.shape,m.values,f.values,p),g=u.makeTensorInfo(y,p),x=u.texData.get(g.dataId);return x.values=A,g}let c=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Ad(t,l.shape,d.shape,n):h=new _l(e,l.shape,d.shape),u.runWebGLProgram(h,[l,d],p)}}function gh(e,t=!1){if(e==="linear")return t?$W:EW;if(e==="relu")return t?OW:RW;if(e==="elu")return t?DW:CW;if(e==="relu6")return t?zW:MW;if(e==="prelu")return t?Nv:Sv;if(e==="leakyrelu")return t?Iv:kv;if(e==="sigmoid")return t?_W:FW;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Ev=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let d=a?e[1]:e[2],u=Math.ceil(d/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:l?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:f=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${f}
|
|
|
|
const float sharedDimension = ${u}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${u}; i++) {
|
|
int batchA = ${g};
|
|
int batchB = ${x};
|
|
vec4 a = getMatrixA(batchA, ${p});
|
|
vec4 b = getMatrixB(batchB, ${c});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${m[0]});
|
|
result += (${h[1]} * ${m[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${A}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},Cv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Rv=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},Mv="return a * b;";function y1(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=C.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),d=new Rv(Cv.REAL,a.shape,r.shape),u=new Rv(Cv.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(d,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=Wr({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[d,u]=rW(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),c=n.texData.get(p.dataId);return c.values=d,p}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ad(Mv,a.shape,r.shape):i=new _l(Mv,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var nB={kernelName:Ds,backendName:"webgl",kernelFunc:y1};function aB(e,t,n){let a=[vi(e.shape),...wi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[vi(t),...wi(t)],i=new mv(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),d=k.sizeFromShape(l);k.assert(o===d,()=>`The new shape (${l}) has ${d} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!pd(r.shape,l)&&!(u.texture!==null&&pd(u.shape,l))?aB(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var rB={kernelName:Bo,backendName:"webgl",kernelFunc:Ae},Fv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let d="";r%n>0&&(d=`
|
|
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) {
|
|
${d}
|
|
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 < ${i}; 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 + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},sB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 d=Math.floor(n/4)*4,u=n%4,p=`
|
|
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 = ${o}(values, minMaxValue);
|
|
}
|
|
`,c="vec4";t==="all"?(i="1.0",p=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,c="bvec4"):t==="any"&&(i="0.0",p=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,c="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
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 * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${d}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${d};
|
|
if (${u===1}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===2}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===3}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function iB(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Ni(e,t,n,a){let r=iB(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:d}=r[i],u,p;n==="mean"?u=i===0?new Fv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d},o):new Fv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d}):u=new sB({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var lB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=lt(this.rank),r=oB(t);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function oB(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var uB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let d=0;d<n.length;d++)n[d]=e[t[d]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=lt(this.rank),r=fv("rc",this.rank),s=new Array(this.rank);for(let d=0;d<t.length;d++)s[t[d]]=r[d];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${o}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${o}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function xh(e,t,n){let a=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uB(e.shape,t):new lB(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function dB(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=C.getAxesPermutation(o,s),d=l!=null,u=e;d&&(u=xh(e,l,a),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=C.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,A=Ae({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=dc(e.dtype),g=Ni(A,y,"sum",a),x=Ae({inputs:{x:g},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(A),a.disposeIntermediateTensorInfo(g),d&&a.disposeIntermediateTensorInfo(u),x}function bh(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return dB(r,s,i,n)}var pB={kernelName:Xs,backendName:"webgl",kernelFunc:bh};function hn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let d;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=m1(u,r.shape,r.dtype,s,l);d=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(d.dataId);c.values=p}else d=xh(r,s,i);return d}var cB={kernelName:ei,backendName:"webgl",kernelFunc:hn},$v=1e3;function vh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let d=e.shape.length,u=t.shape.length,p=n?e.shape[d-2]:e.shape[d-1],c=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[d-1]:e.shape[d-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=k.sizeFromShape(f),g=k.sizeFromShape(A),x=y===g||y===1||g===1;k.assert(d>=2&&u>=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 (${f}) and (${A}).`);let w=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[y,p,h]:[y,h,p],v=a?[g,m,c]:[g,c,m],N=Ae({inputs:{x:e},backend:r,attrs:{shape:b}}),T=Ae({inputs:{x:t},backend:r,attrs:{shape:v}}),R=[N,T],$=Math.max(y,g),z=n?N.shape[1]:N.shape[2],P=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?gh(l,!0):null,X=P||V||j||U!=null,G;if((h===1||m===1)&&z>$v&&X===!1){let Y=N,re=T;n&&(Y=hn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),R.push(Y)),a&&(re=hn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),R.push(re));let ne=m!==1,ie=m===1,Q=Y;ne&&(Q=Ae({inputs:{x:Y},backend:r,attrs:{shape:[$,z,1]}}),R.push(Q));let de=m===1?2:1,oe=re;ie&&(oe=Ae({inputs:{x:re},backend:r,attrs:{shape:[$,1,z]}}),R.push(oe));let ye=y1({inputs:{a:Q,b:oe},backend:r});G=bh({inputs:{x:ye},backend:r,attrs:{axis:de,keepDims:!0}}),R.push(ye)}else{let Y=ia(e.dtype,t.dtype),re=new Ev(b,v,[$,h,m],n,a,P,U,V,j),ne=[N,T];if(s!=null&&ne.push(s),V&&ne.push(i),j){let ie=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));ne.push(ie),R.push(ie)}G=r.runWebGLProgram(re,ne,Y)}let ee=Ae({inputs:{x:G},backend:r,attrs:{shape:w}});R.push(G);for(let Y of R)r.disposeIntermediateTensorInfo(Y);return ee}function hB(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a;return vh({a:r,b:s,transposeA:l,transposeB:d,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var fB={kernelName:ti,backendName:"webgl",kernelFunc:hB},Dv="return abs(x);";function mB(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=hv(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Ol(a.shape,Dv):r=new Lr(a.shape,Dv),n.runWebGLProgram(r,[a],a.dtype)}var AB={kernelName:eo,backendName:"webgl",kernelFunc:mB},yB=ba+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,gB=qe({opSnippet:yB}),xB={kernelName:to,backendName:"webgl",kernelFunc:gB},bB=ba+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,vB=qe({opSnippet:bB}),wB={kernelName:no,backendName:"webgl",kernelFunc:vB},Ov="return a + b;",kB=tn({opSnippet:Ov,packedOpSnippet:Ov,supportsComplex:!0,cpuKernelImpl:jL}),IB={kernelName:kr,backendName:"webgl",kernelFunc:kB},SB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}},NB=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}};function wh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Ln({inputs:{x:a[0]},backend:n});if(a.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=wh({inputs:a.slice(0,o),backend:n}),d=wh({inputs:a.slice(o),backend:n});return wh({inputs:[l,d],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ia(o,l)),s=a.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new NB(a[0].shape,s):new SB(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var TB={kernelName:os,backendName:"webgl",kernelFunc:wh};function EB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),d=l,u=C.getAxesPermutation(d,o),p=r;u!=null&&(p=hn({inputs:{x:r},backend:n,attrs:{perm:u}}),d=C.getInnerMostAxes(d.length,o)),C.assertAxesAreInnerMostDims("all",d,o);let[c,h]=C.computeOutAndReduceShapes(p.shape,d),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),A=Ni(f,f.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(c,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(p),y}var CB={kernelName:ao,backendName:"webgl",kernelFunc:EB};function RB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),d=l,u=C.getAxesPermutation(d,o),p=r;u!=null&&(p=hn({inputs:{x:r},backend:n,attrs:{perm:u}}),d=C.getInnerMostAxes(d.length,o)),C.assertAxesAreInnerMostDims("any",d,o);let[c,h]=C.computeOutAndReduceShapes(p.shape,d),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),A=Ni(f,f.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(c,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(p),y}var MB={kernelName:ro,backendName:"webgl",kernelFunc:RB},FB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=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 * ${a};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${a}; i++) {
|
|
int inIdx = ${o};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},$B=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.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],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),d=cn("coords",o),u,p;if(s===1){p=o+1;let N=lt(p);u=`
|
|
${N} sourceLocR = ${N}(${d.join()}, 0);
|
|
++${d[o-1]};
|
|
${N} sourceLocG = ${N}(${d.join()}, 0);
|
|
++${d[o-2]};
|
|
${N} sourceLocA = ${N}(${d.join()}, 0);
|
|
--${d[o-1]};
|
|
${N} sourceLocB = ${N}(${d.join()}, 0);
|
|
--${d[o-2]};`}else p=o,u=`
|
|
${l} sourceLocR = coords;
|
|
++${d[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${d[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${d[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${d[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(N=>"int "+N),f=cn("sourceLocR",p-1).concat("inIdx.r"),A=cn("sourceLocG",p-1).concat("inIdx.g"),y=cn("sourceLocB",p-1).concat("inIdx.b"),g=cn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",w=a?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${g.join()})));`,b=`vec4(
|
|
getAChannel(${f.join()}),
|
|
hasNextCol ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,v=a?"":`
|
|
float getBestIndicesAChannel(${m.join()}) {
|
|
return getChannel(getBestIndicesA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${m.join()}) {
|
|
return getChannel(getA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}
|
|
${v}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${d[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${d[o-2]} < ${i[o-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${b};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${w}
|
|
vec4 candidate = ${b};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${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 zv(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new FB(o,n,a==null),d=[t];a!=null&&d.push(a);let u=e.runWebGLProgram(l,d,"int32");if(u.shape[1]===1)return u;let p=zv(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function _v(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=C.computeOptimalWindowSize(s),o=new $B(r,i,n,a==null),l=a==null?[t]:[t,a],d=e.runWebGLProgram(o,l,"int32");if(d.shape.length===t.shape.length){let u=_v(e,t,n,d);return e.disposeIntermediateTensorInfo(d),u}return d}function Pv(e,t,n,a){let r=[n];if(C.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),d=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(d);let u=zv(e,d,a);s.push(u);let p=Ae({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return _v(e,t,a)}function DB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=hn({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=Pv(n,l,i[0],"max");return d.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var OB={kernelName:ls,backendName:"webgl",kernelFunc:DB};function zB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=C.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=hn({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=Pv(n,l,i[0],"min");return d.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var _B={kernelName:Au,backendName:"webgl",kernelFunc:zB},PB=ba+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,LB=qe({opSnippet:PB}),WB={kernelName:so,backendName:"webgl",kernelFunc:LB},BB=ba+"return log(x + sqrt(x * x + 1.0));",VB=qe({opSnippet:BB}),jB={kernelName:io,backendName:"webgl",kernelFunc:VB},UB=ba+`
|
|
return atan(x);
|
|
`,HB=qe({opSnippet:UB}),GB={kernelName:oo,backendName:"webgl",kernelFunc:HB},qB=eB+`
|
|
return atan(a, b);
|
|
`,XB=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+tB+`
|
|
return result;
|
|
`,KB=tn({opSnippet:qB,packedOpSnippet:XB}),ZB={kernelName:uo,backendName:"webgl",kernelFunc:KB},YB=ba+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,JB=qe({opSnippet:YB}),QB={kernelName:lo,backendName:"webgl",kernelFunc:JB},yd=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,d=e.dilationWidth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${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 < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${d}) {
|
|
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 ${N} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?r?f:A:`wR * ${p} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let w=Math.floor(s/4)*4,b=s%4,v=`
|
|
if (${m}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${g}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
const float initializationValue = ${y};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${w}; wC += 4) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${d}, d),
|
|
getValue(batch, xR, xC + 2 * ${d}, d),
|
|
getValue(batch, xR, xC + 3 * ${d}, d)
|
|
);
|
|
|
|
${v}
|
|
}
|
|
|
|
int xC = xCCorner + ${w};
|
|
if (${b===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${v}
|
|
} else if (${b===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${d}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${v}
|
|
} else if (${b===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${d}, d),
|
|
getValue(batch, xR, xC + 2 * ${d}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${v}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},g1=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,d=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${f}, ${A}, ${y});
|
|
|
|
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 < ${c};
|
|
wD += ${d}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m};
|
|
wC += ${p}) {
|
|
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 ${R} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?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} * ${m} +
|
|
wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let w="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let v=Math.floor(s/4)*4,N=s%4,T=`
|
|
if (${g}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${w}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${f}, ${A}, ${y});
|
|
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 < ${c};
|
|
wD += ${d}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${v}; wC += 4) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
|
|
);
|
|
|
|
${T}
|
|
}
|
|
|
|
int xC = xCCorner + ${v};
|
|
if (${N===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${N===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${N===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
}
|
|
}
|
|
setOutput(${b});
|
|
}
|
|
}
|
|
`}};function eV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Cl(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;k.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let u=C.computePool2DInfo(r.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Ln({inputs:{x:r},backend:n});let p=new yd(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var tV={kernelName:us,backendName:"webgl",kernelFunc:eV};function nV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:d}=a,u=[1,1,1],p=C.computePool3DInfo(r.shape,s,i,u,o,l,d),c=new g1(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var aV={kernelName:yu,backendName:"webgl",kernelFunc:nV},rV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,d=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${d}, ${u});
|
|
const float avgMultiplier = float(${p});
|
|
|
|
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 < ${o};
|
|
wR += ${s}) {
|
|
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 < ${l};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},sV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,A=1/(t*n*a);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${m}, ${f});
|
|
const float avgMultiplier = float(${A});
|
|
|
|
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 < ${u};
|
|
wD += ${o}) {
|
|
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 < ${p};
|
|
wR += ${l}) {
|
|
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 < ${c};
|
|
wC += ${d}) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.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 iV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:d,dimRoundingMode:u}=a,p=[1,1,1],c=C.computePool3DInfo(i.shape,o,l,p,d,u),h=new sV(c);return n.runWebGLProgram(h,[r],i.dtype)}var oV={kernelName:Cp,backendName:"webgl",kernelFunc:iV};function lV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Cl([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:d}=a,u=C.computePool2DInfo(i.shape,o,l,1,d),p=new rV(u);return n.runWebGLProgram(p,[r],i.dtype)}var uV={kernelName:Ep,backendName:"webgl",kernelFunc:lV};function dV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return vh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var pV={kernelName:ds,backendName:"webgl",kernelFunc:dV},cV=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${o};
|
|
float inv = scale * inversesqrt(variance + float(${s}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},hV=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${o};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},fV=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let d=[a,r,s],u=null;i!=null&&(u=i.shape,d.push(i));let p=null;o!=null&&(p=o.shape,d.push(o));let c=J().getBool("WEBGL_PACK_NORMALIZATION")?new hV(a.shape,r.shape,s.shape,u,p,l):new cV(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(c,d,d[0].dtype)},mV={kernelName:ws,backendName:"webgl",kernelFunc:fV},yV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,a=AV(this.rank),r,s=e.map((i,o)=>`sourceLoc.${x1[o]} = start[${o}] + coords.${x1[o]};`);r=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${s.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${n}
|
|
void main() {
|
|
${r}
|
|
setOutput(getSource(${a}));
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},x1=["x","y","z","w","u","v"];function AV(e){if(e===1)return"sourceLoc";if(e<=6)return x1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var gV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=cn("coords",this.rank),a=cn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
|
|
result.x = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.y = ${s};
|
|
--${a[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${a[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((d,u)=>`start[${u}]`).join()});`:e.map((d,u)=>`${a[u]} = ${n[u]} + start[${u}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function xV(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=ln.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function gd(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=ln.parseSliceParams(r,s,i);if(ln.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=uW(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:d}=n.texData.get(r.dataId),u=ln.isSliceContinous(r.shape,o,l);if(d||!u){let p=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gV(l):new yV(l),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),xV(r,o,l,n)}var bV={kernelName:Ho,backendName:"webgl",kernelFunc:gd},vV=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=C.getReshaped(r.shape,s,o),d=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(r.shape,s,o),p=C.getSliceBeginCoords(i,s.length),c=C.getSliceSize(u,i,s.length),h=[],m=Ae({inputs:{x:r},backend:n,attrs:{shape:l}}),f=hn({inputs:{x:m},backend:n,attrs:{perm:d}}),A=Ae({inputs:{x:f},backend:n,attrs:{shape:u}}),y=gd({inputs:{x:A},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(A),h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},wV={kernelName:gu,backendName:"webgl",kernelFunc:vV};function kV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),d=cv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}var IV={kernelName:Rp,backendName:"webgl",kernelFunc:kV},SV="return float(a != b);",Lv=tn({opSnippet:SV,dtype:"bool"}),NV={kernelName:$o,backendName:"webgl",kernelFunc:Lv};function xd(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Ln({inputs:{x:r.complexTensorInfos.real},backend:n})}var TV={kernelName:Jp,backendName:"webgl",kernelFunc:xd},EV="return float(int(x));";function CV(e,t){let n=new Lr(e.shape,EV),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function b1(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Ln({inputs:{x:r},backend:n});let i=Rt(r.shape),o=b1({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Wr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=xd({inputs:{input:r},backend:n}),o=b1({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Ln({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return CV(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=Lv({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var RV={kernelName:ps,backendName:"webgl",kernelFunc:b1},Wv="return ceil(x);",MV=qe({opSnippet:Wv,packedOpSnippet:Wv,cpuKernelImpl:HL}),FV={kernelName:cs,backendName:"webgl",kernelFunc:MV},$V=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},DV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function OV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;J().getBool("WEBGL_PACK_CLIP")?o=new DV(r.shape):o=new $V(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var zV={kernelName:Ir,backendName:"webgl",kernelFunc:OV},_V=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float re = abs(getRealAtOutCoords());
|
|
float im = abs(getImagAtOutCoords());
|
|
float mx = max(re, im);
|
|
|
|
// sadly the length function in glsl is not underflow-safe
|
|
// (at least not on Intel GPUs). So the safe solution is
|
|
// to ensure underflow-safety in all cases.
|
|
setOutput(
|
|
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
|
|
);
|
|
}
|
|
`}};function Bv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function PV(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new _V(a.shape),i=[Bv(a,r.complexTensorInfos.real),Bv(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var LV={kernelName:xu,backendName:"webgl",kernelFunc:PV},WV=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},BV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=lt(a),s=cn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],d=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${d.join()}));
|
|
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
|
|
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
|
|
return getChannel(
|
|
getT${m}(${kh(i,l,f)}),
|
|
vec2(${kh(d,l,f)}));
|
|
}`}let c=o.length,h=o[o.length-1];p+=`
|
|
return getChannel(
|
|
getT${c}(${kh(i,l,h)}),
|
|
vec2(${kh(d,l,h)}));`,this.userCode=`
|
|
float getValue(${i.map(m=>"int "+m)}) {
|
|
${p}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[a-1]} = ${s[a-1]} + 1;
|
|
if (${s[a-1]} < ${n[a-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[a-2]} = ${s[a-2]} + 1;
|
|
if (${s[a-2]} < ${n[a-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[a-1]} = ${s[a-1]} - 1;
|
|
if (${s[a-2]} < ${n[a-2]} &&
|
|
${s[a-1]} < ${n[a-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function kh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Ih(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Ln({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VV={kernelName:Hp,backendName:"webgl",kernelFunc:Ih};function Pl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let u=e.map(f=>xd({inputs:{input:f},backend:n})),p=e.map(f=>Ih({inputs:{input:f},backend:n})),c=Pl(u,t,n),h=Pl(p,t,n),m=Wr({inputs:{real:c,imag:h},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let u=e.map(y=>{let g=k.sizeFromShape(y.shape.slice(t));return Ae({inputs:{x:y},backend:n,attrs:{shape:[-1,g]}})}),p=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),c=C.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,m=GL(p,c,a,h),f=C.computeOutShape(e.map(y=>y.shape),t),A=n.makeTensorInfo(f,a,m);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),p=Pl(e.slice(0,u),t,n),c=Pl(e.slice(u),t,n),h=Pl([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new BV(e.map(p=>p.shape),t);return n.runWebGLProgram(u,e,a)}let{tensors2D:s,outShape:i}=jV(e,t,n),o=new WV(s.map(u=>u.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(u=>n.disposeIntermediateTensorInfo(u));let d=Ae({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),d}function jV(e,t,n){let a=C.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ae({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function Vv(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=C.computeOutShape(t.map(d=>d.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(d=>k.sizeFromShape(d.shape)>0);if(o.length===1)return Ln({inputs:{x:o[0]},backend:n});let l=o.map(d=>d.shape);return C.assertParamsConsistent(l,s),Pl(o,s,n)}var UV={kernelName:po,backendName:"webgl",kernelFunc:Vv},jv=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,d=e.dilationHeight,u=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,x="",w="";n&&(a?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}
|
|
}
|
|
`,w="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${l});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${g}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${A}], coords[${y}]) * 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 < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${d};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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 (${m===1}) {
|
|
|
|
if (${f}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${m===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${m===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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;
|
|
${b}
|
|
${w}
|
|
setOutput(result);
|
|
}
|
|
`}},HV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${a});
|
|
|
|
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 < ${u}; wF++) {
|
|
int xF = xFCorner + wF * ${o};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${m===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${m===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, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${m===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);
|
|
}
|
|
`}},GV=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:d,dilationHeight:u,dataFormat:p}=n,{left:c,top:h}=o,m=r*a,f=pn(),A=p==="channelsLast",y=A?0:1,g=A?1:2,x="";for(let w=0;w<=1;w++)for(let b=0;b<=1;b++)x+=`
|
|
blockIndex = rc.y + ${b};
|
|
pos = rc.x + ${w};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${l})) * ${i} - ${h};
|
|
d0 = offsetY + ${u} * (pos / ${m});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${c}.);
|
|
d1 = offsetX + ${d} * (int(mod(float(pos), ${m}.) / ${r}.));
|
|
|
|
if(d1 < ${t[g]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${r}.));
|
|
|
|
if (${A}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${w*2+b}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${w*2+b}] = 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;
|
|
|
|
${x}
|
|
|
|
${f.output} = result;
|
|
}
|
|
`}};function Uv({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,d=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(p===1||c===1)&&u>$v,x=l[2]%2!=0&&!!d.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let w=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],b=Ae({inputs:{x:e},backend:a,attrs:{shape:[1,w,n.inChannels]}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=vh({a:b,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:N},backend:a,attrs:{shape:n.outShape}}),y.push(b),y.push(v),y.push(N)}else{let w=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),b={dataId:e.dataId,shape:[1,w,n.inChannels],dtype:e.dtype},v=d.shape;d.shape=d.shape.slice(),d.shape[d.shape.length-2]++,k.assert(pd(d.shape,b.shape),()=>`packed reshape ${d.shape} to ${b.shape} isn't free`);let N=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=vh({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),R=a.texData.get(T.dataId);k.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),d.shape=v,R.shape=n.outShape,A=Ln({inputs:{x:T},backend:a}),A.shape=n.outShape,y.push(T)}for(let w of y)a.disposeIntermediateTensorInfo(w);return A}function Hv({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:d,inChannels:u,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=l*d*u,A=c*p,y=[f,A],g=!0,x=!1,w=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});w.push(b),w.push(v);let N=new GV(y,b.shape,n),T=a.runWebGLProgram(N,[b],"float32"),R=Ae({inputs:{x:T},backend:a,attrs:{shape:[1,y[0],y[1]]}});w.push(T),w.push(R);let $=r!=null,z=s!=null,P=o==="leakyrelu",V=o?gh(o,!0):null,j=new Ev(R.shape,v.shape,[1,A,n.outChannels],g,x,$,V,z,P),U=[R,v];if(r&&U.push(r),z&&U.push(s),P){let Y=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));U.push(Y),w.push(Y)}let X=a.runWebGLProgram(j,U,"float32"),G=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],ee=Ae({inputs:{x:X},backend:a,attrs:{shape:G}});w.push(X);for(let Y of w)a.disposeIntermediateTensorInfo(Y);return ee}function qV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:d,dimRoundingMode:u}=a,p=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=Uv({x:r,filter:s,convInfo:c,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=Hv({x:r,filter:s,convInfo:c,backend:n});else{let f=new jv(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=Ae({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var XV={kernelName:hs,backendName:"webgl",kernelFunc:qV},KV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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} - ${a};
|
|
|
|
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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${s}) {
|
|
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);
|
|
}
|
|
`}},ZV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,d=s?2:3,u=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${u}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${d}]) - 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) / ${a}.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 < ${e.outChannels}; d2++) {
|
|
|
|
if (${s}) {
|
|
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);
|
|
}
|
|
`}},YV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=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} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${a} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},JV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,d=a-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${d});
|
|
|
|
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) / ${s}.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 < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${a} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function QV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:d,filterShape:u}=a,p=C.convertConv2DDataFormat(l),c=C.computeConv2DInfo(r.shape,u,i,1,o,d,!1,p),h=new KV(c);return n.runWebGLProgram(h,[r,s],"float32")}var ej={kernelName:Fp,backendName:"webgl",kernelFunc:QV};function tj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:d,dimRoundingMode:u}=a,p=C.convertConv2DDataFormat(d),c=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new ZV(c);return n.runWebGLProgram(h,[r,s],"float32")}var nj={kernelName:fs,backendName:"webgl",kernelFunc:tj};function aj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,d=C.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new HV(d);return n.runWebGLProgram(u,[r,s],"float32")}var rj={kernelName:bu,backendName:"webgl",kernelFunc:aj};function sj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,d=C.computeConv3DInfo(r.shape,l,i,1,o),u=new YV(d);return n.runWebGLProgram(u,[r,s],"float32")}var ij={kernelName:$p,backendName:"webgl",kernelFunc:sj};function oj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,d=C.computeConv3DInfo(l,s.shape,o,1,i),u=new JV(d);return n.runWebGLProgram(u,[r,s],"float32")}var lj={kernelName:Dp,backendName:"webgl",kernelFunc:oj},uj=Tv+`
|
|
return cos(x);
|
|
`,dj=qe({opSnippet:uj}),pj={kernelName:ms,backendName:"webgl",kernelFunc:dj},cj=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,hj=qe({opSnippet:cj}),fj={kernelName:co,backendName:"webgl",kernelFunc:hj},mj=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[d]=t,[u,p]=n;this.outputShape=[d,u,p,l];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[g,x,w]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
|
|
const float height_ratio = float(${f});
|
|
const float width_ratio = float(${g});
|
|
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 >= ${s}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${A};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${w};
|
|
if( in_x < 0.0 || in_x > ${m} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${c} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Aj=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:d}=a,u=new mj(r.shape,s.shape,o,l,d);return n.runWebGLProgram(u,[r,s,i],"float32")},yj={kernelName:ho,backendName:"webgl",kernelFunc:Aj},Xv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${Gv(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${lt(a)} coords = getOutputCoords();
|
|
int end = ${qv(a,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${qv(a,"coords")} = idx;
|
|
val += getX(${Gv(a,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function Gv(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 ${e} is not yet supported`)}function qv(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function gj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,d=C.getAxesPermutation([s],l),u=r;d!=null&&(u=hn({inputs:{x:r},backend:n,attrs:{perm:d}}));let p=C.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=u.shape[p],h=Ln({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new Xv(u.shape,!1,o),A=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new Xv(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(d!=null){let m=C.getUndoAxesPermutation(d),f=hn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var xj={kernelName:As,backendName:"webgl",kernelFunc:gj};function bj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),d=n.readSync(s.dataId),u=cv(l,d,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),d=n.bufferSync(s),u=UL(l,d,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var vj={kernelName:Op,backendName:"webgl",kernelFunc:bj},wj=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 kj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],d=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=d*s,h=u/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new wj(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var Ij={kernelName:fo,backendName:"webgl",kernelFunc:kj},Kv=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,d=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(a?A=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?A=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:A=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${d}, ${u});
|
|
const ivec2 pads = ivec2(${o}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${f};
|
|
int q = d2 - d1 * ${f};
|
|
|
|
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 < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${p};
|
|
|
|
if (xR < 0 || xR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${g}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},Zv=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,d=e.padInfo.left,u=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,A=f,y=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
|
|
vec4 xTexelC${b*2};
|
|
int xTexelC${b*2}Ready;
|
|
vec4 xC${b};`;for(let b=0;b<m;b++){for(let v=0;v<f;v++)y+=`
|
|
xTexelC${v*2} = vec4(0.0);
|
|
xTexelC${v*2}Ready = 0;
|
|
xC${v} = vec4(0.0);`;y+=`
|
|
xR = xRCorner + ${b*c};
|
|
if (xR >=0 && xR < ${i}) {
|
|
`;for(let v=0;v<(A+1)/2;v++){let N=v*2,T=N*h;if(y+=`
|
|
xC = xCCorner + ${T};
|
|
`,p===1){if(N<f&&(d%2==1?(y+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) {
|
|
xTexelC${T} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${T}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T}Ready = 1;
|
|
}
|
|
`,h===1&&T>0?y+=`
|
|
xC${N} = vec4(xTexelC${T-2}.zw, xTexelC${T}.xy);
|
|
`:y+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${N} = vec4(previous.zw, xTexelC${T}.xy);
|
|
} else {
|
|
xC${N} = vec4(0.0, 0.0, xTexelC${T}.xy);
|
|
}
|
|
`):y+=`
|
|
if (xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) {
|
|
xTexelC${T} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${T}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T}Ready = 1;
|
|
}
|
|
|
|
xC${N} = xTexelC${T};
|
|
`,T+1<f)){let R=d%2==0?k.nearestLargerEven(h):h;h%2==0&&d%2==1||h%2!=0&&d%2!=1?(y+=`
|
|
xCOffset = xC + ${d%2} + ${R};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) {
|
|
xTexelC${T+2} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${T+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T+2}Ready = 1;
|
|
}
|
|
`,h>1&&(y+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) {
|
|
xTexelC${T} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${T}Ready = 1;
|
|
}
|
|
`),y+=`
|
|
xC${N+1} = vec4(xTexelC${T}.zw, xTexelC${T+2}.xy);
|
|
`):R===1?y+=`
|
|
xC${N+1} = xTexelC${T};
|
|
`:y+=`
|
|
xCOffset = xC + ${R};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) {
|
|
xTexelC${T+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${T+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T+2}Ready = 1;
|
|
}
|
|
|
|
xC${N+1} = xTexelC${T+2};
|
|
`}}else T<f&&(d%2==1?(y+=`
|
|
xCOffset = xC + 1 - ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${T}Ready == 0) {
|
|
xTexelC${T} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${T}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${T+2}Ready == 0) {
|
|
xTexelC${T+2} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= ${o}) {
|
|
xTexelC${T+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T+2}Ready = 1;
|
|
}
|
|
|
|
xC${N} = vec4(xTexelC${T}.zw, xTexelC${T+2}.zw);
|
|
`,T+1<f&&(y+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${N+1} = vec4(xTexelC${T+2}.xy, final.xy);
|
|
`)):(y+=`
|
|
if(xC >= 0 && xC < ${o} && xTexelC${T}Ready == 0) {
|
|
xTexelC${T} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${T}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${T}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${T+2}Ready == 0) {
|
|
xTexelC${T+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${T+2}.zw = vec2(0.);
|
|
}
|
|
xTexelC${T+2}Ready = 1;
|
|
}
|
|
|
|
xC${N} = vec4(
|
|
xTexelC${T}.xy, xTexelC${T+2}.xy);
|
|
`,T+1<f&&(y+=`
|
|
xC${N+1} = vec4(xTexelC${T}.zw, xTexelC${T+2}.zw);
|
|
`)));N<f&&(y+=`
|
|
wTexel = getW(${b}, ${T}, d1, q);
|
|
dotProd += xC${N} * vec4(wTexel.xz, wTexel.xz);
|
|
`,T+1<f&&(y+=`
|
|
wTexel = getW(${b}, ${T+1}, d1, q);
|
|
dotProd += xC${N+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}y+=`
|
|
}
|
|
`}let g="",x="";n&&(a?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:g=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,x="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${g}
|
|
|
|
const ivec2 strides = ivec2(${u}, ${p});
|
|
const ivec2 pads = ivec2(${l}, ${d});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${s};
|
|
int q = d2 - d1 * ${s};
|
|
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);
|
|
|
|
${y}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${w}
|
|
${x}
|
|
setOutput(result);
|
|
}
|
|
`}};function Sj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:d}=a,u=l;u==null&&(u=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=C.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!0),c;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new Zv(p):c=new Kv(p),n.runWebGLProgram(c,[r,s],"float32")}var Nj={kernelName:ys,backendName:"webgl",kernelFunc:Sj},Tj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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 * ${s} + 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} - ${a};
|
|
|
|
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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Ej=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
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) / ${a}.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 < ${o}; dm++) {
|
|
int d2 = d1 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Cj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,filterShape:u}=a,p=C.computeConv2DInfo(r.shape,u,i,o,l,d,!0),c=new Tj(p);return n.runWebGLProgram(c,[r,s],"float32")}var Rj={kernelName:zp,backendName:"webgl",kernelFunc:Cj};function Mj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,inputShape:u}=a,p=C.computeConv2DInfo(u,s.shape,i,o,l,d,!0),c=new Ej(p);return n.runWebGLProgram(c,[r,s],"float32")}var Fj={kernelName:_p,backendName:"webgl",kernelFunc:Mj},$j=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 Dj(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new $j(s),l=n.runWebGLProgram(o,[i],i.dtype),d=Ae({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),d}var Oj={kernelName:Pp,backendName:"webgl",kernelFunc:Dj},zj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:d}=e,{top:u,left:p}=a;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${s});
|
|
const ivec2 pads = ivec2(${u}, ${p});
|
|
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 < ${i}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${o}; w++) {
|
|
int wIn = wBeg + w * ${d};
|
|
|
|
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 _j(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,d=C.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new zj(d);u=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:u},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(u),c}var Pj={kernelName:vu,backendName:"webgl",kernelFunc:_j};function Lj(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(r,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:d,steps:u}=C.getEinsumComputePath(o,l),p=u.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let A of u[f]){let{permutationIndices:y,expandDims:g}=C.getEinsumPermutation(h,l[A]),x;C.isIdentityPermutation(y)?x=s[A]:(x=hn({inputs:{x:s[A]},backend:n,attrs:{perm:y}}),m.push(x));let w=x.shape.slice();for(let b=0;b<g.length;++b)w.splice(g[b],0,1);k.arraysEqual(x.shape,w)||(x=Ae({inputs:{x},backend:n,attrs:{shape:w}}),m.push(x)),c===null?c=x:(c=y1({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(d[f]>=0&&(c=bh({inputs:{x:c},backend:n,attrs:{axis:d[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var Wj={kernelName:Bp,backendName:"webgl",kernelFunc:Lj},Bj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Vj=`
|
|
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;
|
|
`,jj=qe({opSnippet:Bj,packedOpSnippet:Vj}),Uj={kernelName:mo,backendName:"webgl",kernelFunc:jj},Hj="return (b >= 1.0) ? a : a * (b + 1.0);",Gj=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,qj=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ad(Gj,a.shape,r.shape):new _l(Hj,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},Xj={kernelName:Vp,backendName:"webgl",kernelFunc:qj},Kj=`
|
|
return vec4(equal(a, b));
|
|
`,Zj="return float(a == b);",Yj=tn({opSnippet:Zj,packedOpSnippet:Kj,dtype:"bool"}),Jj={kernelName:yo,backendName:"webgl",kernelFunc:Yj},Qj=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${C.ERF_P};
|
|
float a1 = ${C.ERF_A1};
|
|
float a2 = ${C.ERF_A2};
|
|
float a3 = ${C.ERF_A3};
|
|
float a4 = ${C.ERF_A4};
|
|
float a5 = ${C.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,eU=qe({opSnippet:Qj}),tU={kernelName:Ao,backendName:"webgl",kernelFunc:eU},Yv="return exp(x);",Jv=qe({opSnippet:Yv,packedOpSnippet:Yv,cpuKernelImpl:qL}),nU={kernelName:xs,backendName:"webgl",kernelFunc:Jv};function v1(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var aU={kernelName:go,backendName:"webgl",kernelFunc:v1},Qv="return exp(x) - 1.0;",rU=qe({opSnippet:Qv,packedOpSnippet:Qv,cpuKernelImpl:XL}),sU={kernelName:xo,backendName:"webgl",kernelFunc:rU},ew=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="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) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${a});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${a}; 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) / ${s};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function tw(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,d=new ew("real",l,t),u=new ew("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(d,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=Wr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function iU(e){let{inputs:t,backend:n}=e,{input:a}=t;return tw(a,!1,n)}var oU={kernelName:jp,backendName:"webgl",kernelFunc:iU},lU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
uniform float value;
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function w1(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new lU(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var uU={kernelName:wu,backendName:"webgl",kernelFunc:w1},dU=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;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},pU={kernelName:bo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new dU(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},nw="return floor(x);",cU=qe({opSnippet:nw,packedOpSnippet:nw,cpuKernelImpl:KL}),hU={kernelName:bs,backendName:"webgl",kernelFunc:cU},fU=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,mU=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,AU=tn({opSnippet:fU,packedOpSnippet:mU,dtype:"int32"}),yU={kernelName:vs,backendName:"webgl",kernelFunc:AU},gU=class{constructor(e){this.variableNames=["A"];let t=pn(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},xU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=pn(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}.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;
|
|
}
|
|
`}},vU={kernelName:sc,backendName:"webgl",kernelFunc:bU},Ll;function bU(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,d]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[d,l],p=[d,l,s];(o||i)&&(Ll==null&&(Ll=document.createElement("canvas").getContext("2d")),Ll.canvas.width=l,Ll.canvas.height=d,Ll.drawImage(r,0,0,l,d),r=Ll.canvas);let c=n.makeTensorInfo(u,"int32");n.texData.get(c.dataId).usage=Jn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=J().getBool("WEBGL_PACK")?new xU(p):new gU(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function wU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dataFormat:u,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=C.convertConv2DDataFormat(u),A=C.computeConv2DInfo(r.shape,s.shape,l,p,d,c,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=Uv({x:r,filter:s,convInfo:A,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=Hv({x:r,filter:s,convInfo:A,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let w=i!=null,b=o!=null,v=h==="leakyrelu",N=h?gh(h,!1):null,T=new jv(A,w,N,b,v),R=[r,s];if(i&&R.push(i),o&&R.push(o),v){let $=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));R.push($),g.push($)}y=n.runWebGLProgram(T,R,"float32")}let x=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var kU={kernelName:ni,backendName:"webgl",kernelFunc:wU};function IU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=C.computeConv2DInfo(r.shape,s.shape,l,f,d,p,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=c?gh(c,y):null,x=[r,s],w=i!=null,b=o!=null,v=c==="leakyrelu";if(w&&x.push(i),b&&x.push(o),v){let R=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(R),m.push(R)}let N;y?N=new Zv(A,w,g,b,v):N=new Kv(A,w,g,b,v);let T=n.runWebGLProgram(N,x,"float32");return m.forEach(R=>n.disposeIntermediateTensorInfo(R)),T}var SU={kernelName:ai,backendName:"webgl",kernelFunc:IU},NU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=lt(t.length),r=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${a} strides = ${a}(${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 * ${s};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function TU(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,d,u]=C.prepareAndValidate(a,r),p=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/d,d]}}),h=new NU(i,u,[l,d]),m=n.runWebGLProgram(h,[c,p],c.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),f}var EU={kernelName:wo,backendName:"webgl",kernelFunc:TU},RU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),a=CU(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function CU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function MU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],d=C.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],c=Ae({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Ae({inputs:{x:s},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}});p.push(c),p.push(h);let m=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let g=n.bufferSync(h),x=n.bufferSync(c),w=ZL(x,g,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(d.outputShape,w.dtype,w.values)}let f=new RU(c.shape,m),A=n.runWebGLProgram(f,[c,h],c.dtype);p.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:d.outputShape}});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var FU={kernelName:vo,backendName:"webgl",kernelFunc:MU},$U="return float(a > b);",DU=`
|
|
return vec4(greaterThan(a, b));
|
|
`,OU=tn({opSnippet:$U,packedOpSnippet:DU,cpuKernelImpl:YL,dtype:"bool"}),zU={kernelName:ko,backendName:"webgl",kernelFunc:OU},_U="return float(a >= b);",PU=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,LU=tn({opSnippet:_U,packedOpSnippet:PU,dtype:"bool"}),WU={kernelName:ks,backendName:"webgl",kernelFunc:LU};function BU(e){let{inputs:t,backend:n}=e,{input:a}=t;return tw(a,!0,n)}var VU={kernelName:Up,backendName:"webgl",kernelFunc:BU},jU="return float(!isnan(x) && !isinf(x));",UU=qe({opSnippet:jU,dtype:"bool"}),HU={kernelName:Io,backendName:"webgl",kernelFunc:UU},GU="return float(isinf(x));",qU=qe({opSnippet:GU,dtype:"bool"}),XU={kernelName:So,backendName:"webgl",kernelFunc:qU},KU="return float(isnan(x));",ZU=qe({opSnippet:KU,dtype:"bool"}),YU={kernelName:No,backendName:"webgl",kernelFunc:ZU},JU="return float(a < b);",QU=`
|
|
return vec4(lessThan(a, b));
|
|
`,eH=tn({opSnippet:JU,packedOpSnippet:QU,cpuKernelImpl:JL,dtype:"bool"}),tH={kernelName:To,backendName:"webgl",kernelFunc:eH},nH="return float(a <= b);",aH=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,rH=tn({opSnippet:nH,packedOpSnippet:aH,dtype:"bool"}),sH={kernelName:Eo,backendName:"webgl",kernelFunc:rH};function iH(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=QL(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var oH={kernelName:Gp,backendName:"webgl",kernelFunc:iH},lH=`if (x < 0.0) return NAN;
|
|
return log(x);`,uH=`
|
|
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;
|
|
`,dH=qe({opSnippet:lH,packedOpSnippet:uH,cpuKernelImpl:eW}),pH={kernelName:Ns,backendName:"webgl",kernelFunc:dH},cH="return log(1.0 + x);",hH=qe({opSnippet:cH}),fH={kernelName:Co,backendName:"webgl",kernelFunc:hH},mH="return float(a >= 1.0 && b >= 1.0);",AH=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,yH=tn({opSnippet:mH,packedOpSnippet:AH,dtype:"bool"}),gH={kernelName:Ro,backendName:"webgl",kernelFunc:yH},xH="return float(!(x >= 1.0));",bH=qe({opSnippet:xH}),vH={kernelName:ku,backendName:"webgl",kernelFunc:bH},wH="return float(a >= 1.0 || b >= 1.0);",kH=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,IH=tn({opSnippet:wH,packedOpSnippet:kH,dtype:"bool"}),SH={kernelName:Iu,backendName:"webgl",kernelFunc:IH},NH=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${s}; j <= ${s}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},TH=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${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 - ${s};
|
|
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 = - ${s}; j <= ${s}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
|
|
|
|
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 * ${o};
|
|
setOutput(result);
|
|
}
|
|
`}},EH=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,d=J().getBool("WEBGL_PACK_NORMALIZATION")?new TH(r.shape,s,i,o,l):new NH(r.shape,s,i,o,l);return n.runWebGLProgram(d,[r],r.dtype)},CH={kernelName:Su,backendName:"webgl",kernelFunc:EH},RH=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,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 = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${a}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${a})
|
|
* 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);
|
|
}
|
|
`}},MH=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:d,beta:u}=a,p=new RH(r.shape,o,l,d,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},FH={kernelName:qp,backendName:"webgl",kernelFunc:MH};function $H(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ni(i,e.dtype,"max",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function aw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),d=l,u=C.getAxesPermutation(d,o),p=u!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let g=n.texData.get(h.dataId).values,x=new Array(o);for(let v=0;v<x.length;v++)x[v]=r.shape[u[v]];let w=m1(g,r.shape,r.dtype,u,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=w}else h=xh(r,u,n);d=C.getInnerMostAxes(d.length,o)}C.assertAxesAreInnerMostDims("max",d,o);let[m,f]=C.computeOutAndReduceShapes(h.shape,d),A=m;i&&(A=C.expandShapeToKeepDim(m,l));let y;if(c){let g=n.texData.get(h.dataId).values,x=tW(g,k.sizeFromShape(f),A,r.dtype);y=n.makeTensorInfo(A,r.dtype);let w=n.texData.get(y.dataId);w.values=x}else y=$H(h,f,A,n);return p&&n.disposeIntermediateTensorInfo(h),y}var DH={kernelName:Ts,backendName:"webgl",kernelFunc:aw},OH=wv+`
|
|
return max(a, b);
|
|
`,zH=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+yh+`
|
|
return result;
|
|
`,_H=tn({opSnippet:OH,packedOpSnippet:zH,cpuKernelImpl:nW}),PH={kernelName:Es,backendName:"webgl",kernelFunc:_H};function LH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Cl(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;k.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let u=C.computePool2DInfo(r.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Ln({inputs:{x:r},backend:n});let p=new yd(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WH={kernelName:Cs,backendName:"webgl",kernelFunc:LH};function BH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:d}=a,u=[1,1,1],p=C.computePool3DInfo(r.shape,s,i,u,o,d,l),c=new g1(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var VH={kernelName:Nu,backendName:"webgl",kernelFunc:BH},jH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
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 += ${a}) {
|
|
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 < ${s}; 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 * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},UH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,d=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=d-1-e.padInfo.left,h=o*l*d-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${p}, ${c});
|
|
|
|
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 < ${o};
|
|
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 += ${s}) {
|
|
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 < ${d};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.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} * ${d} +
|
|
wR * ${d} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function HH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:d,dimRoundingMode:u}=a,p=[1,1,1],c=C.computePool3DInfo(i.shape,o,l,p,d,u),h=new g1(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new UH(c),A=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var GH={kernelName:Kp,backendName:"webgl",kernelFunc:HH};function qH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Cl([s,i],"maxPoolGrad");let{filterSize:l,strides:d,pad:u,dimRoundingMode:p}=a,c=C.computePool2DInfo(o.shape,l,d,1,u,p),h=!0,m=new yd(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),A=new jH(c),y=n.runWebGLProgram(A,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var XH={kernelName:Xp,backendName:"webgl",kernelFunc:qH};function KH(e,t,n,a){let r=new yd(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new yd(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var ZH={kernelName:Zp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let d=[1,1];k.assert(C.eitherStridesOrDilationsAreOne(s,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${d}'`);let u=C.computePool2DInfo(a.shape,r,s,d,i),[p,c]=KH(a,o,u,l);return[p,c]}};function YH(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Ni(i,"float32","mean",a),l=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var JH={kernelName:Rs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),d=l,u=C.getAxesPermutation(d,o),p=u!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let N=0;N<w.length;N++)w[N]=a.shape[u[N]];let b=m1(x,a.shape,a.dtype,u,w);m=i.makeTensorInfo(w,a.dtype);let v=i.texData.get(m.dataId);v.values=b}else m=xh(a,u,i);h.push(m),d=C.getInnerMostAxes(d.length,o)}C.assertAxesAreInnerMostDims("sum",d,o);let[f,A]=C.computeOutAndReduceShapes(m.shape,d),y=f;r&&(y=C.expandShapeToKeepDim(f,l));let g=YH(m,A,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return g}};function QH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),d=l,u=C.getAxesPermutation(d,o),p=r;u!=null&&(p=hn({inputs:{x:r},backend:n,attrs:{perm:u}}),d=C.getInnerMostAxes(d.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",d,o);let[c,h]=C.computeOutAndReduceShapes(p.shape,d),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),A=Ni(f,f.dtype,"min",n),y;if(i){let g=C.expandShapeToKeepDim(c,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(p),y}var eG={kernelName:Ms,backendName:"webgl",kernelFunc:QH},tG=wv+`
|
|
return min(a, b);
|
|
`,nG=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+yh+`
|
|
return result;
|
|
`,aG=tn({opSnippet:tG,packedOpSnippet:nG,cpuKernelImpl:aW}),rG={kernelName:Fs,backendName:"webgl",kernelFunc:aG},sG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((d,u)=>d[0]+e[u]+d[1]);let a=e.length,r=lt(a),s=t.map(d=>d[0]).join(","),i=t.map((d,u)=>d[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
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 = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${a}; 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};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},iG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=lt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=cn("rc",a),l=cn("source",a),d=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${p};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${p};
|
|
}
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${o[a-1]} += 1;
|
|
if(${d}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}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 - ${p}) +
|
|
gte * ((end - 1) * 2 - source + ${p});
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${o[a-1]} += 1;
|
|
if(${d}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${o[a-1]} += 1;
|
|
if(${d}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},oG=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iG(a.shape,r,s):new sG(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},lG={kernelName:$s,backendName:"webgl",kernelFunc:oG},uG=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,dG=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+yh+`
|
|
return result;
|
|
`,pG=tn({opSnippet:uG,packedOpSnippet:dG}),cG={kernelName:Mo,backendName:"webgl",kernelFunc:pG},hG=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
|
|
uniform float seed;
|
|
|
|
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}));
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},fG=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,mG=`
|
|
// 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;
|
|
`,rw=tn({opSnippet:fG,packedOpSnippet:mG,checkOutOfBounds:!0}),AG={kernelName:gs,backendName:"webgl",kernelFunc:rw},sw="return a - b;",iw=tn({opSnippet:sw,packedOpSnippet:sw,supportsComplex:!0,cpuKernelImpl:hW}),yG={kernelName:Ys,backendName:"webgl",kernelFunc:iw};function ow(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=aw({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),d=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),u=iw({inputs:{a:r,b:d},backend:n}),p=Jv({inputs:{x:u},backend:n}),c=bh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Ae({inputs:{x:c},backend:n,attrs:{shape:l}}),m=rw({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var gG={kernelName:Ks,backendName:"webgl",kernelFunc:ow};function xG(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:ow({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),d=l.shape[0],u=l.shape[1],p=new hG(d,u,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var bG={kernelName:Yp,backendName:"webgl",kernelFunc:xG},lw="return -x;";function vG(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=sW(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Ol(a.shape,lw):r=new Lr(a.shape,lw),n.runWebGLProgram(r,[a],a.dtype)}var wG={kernelName:Fo,backendName:"webgl",kernelFunc:vG},kG=Wa.nonMaxSuppressionV3Impl;function IG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,d=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=kG(d,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var SG={kernelName:Do,backendName:"webgl",kernelFunc:IG},NG=Wa.nonMaxSuppressionV4Impl;function TG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:d}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=NG(u,p,i,o,l,d);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var EG={kernelName:Oo,backendName:"webgl",kernelFunc:TG},CG=Wa.nonMaxSuppressionV5Impl;function RG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:d}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=l,f=d,{selectedIndices:A,selectedScores:y}=CG(u,p,c,h,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var MG={kernelName:zo,backendName:"webgl",kernelFunc:RG},FG=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${a}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},$G=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),d=new FG(l,s,i,o),u=Ae({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(d,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let c=[...r.shape,s],h=Ae({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},DG={kernelName:Os,backendName:"webgl",kernelFunc:$G};function Sh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=xd({inputs:{input:a},backend:n}),s=Sh({inputs:{x:r},backend:n}),i=Ih({inputs:{input:a},backend:n}),o=Sh({inputs:{x:i},backend:n}),l=Wr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return w1({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var OG={kernelName:el,backendName:"webgl",kernelFunc:Sh};function uw(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=xd({inputs:{input:a},backend:n}),s=uw({inputs:{x:r},backend:n}),i=Ih({inputs:{input:a},backend:n}),o=Sh({inputs:{x:i},backend:n}),l=Wr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return w1({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var zG={kernelName:_o,backendName:"webgl",kernelFunc:uw};function _G(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return v1({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=v1({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),d=Vv({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),d}var PG={kernelName:Po,backendName:"webgl",kernelFunc:_G},LG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,d)=>l[0]+e[d]+l[1]);let a=e.length,r=lt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,d)=>l[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
uniform float value;
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},WG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=lt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=cn("rc",a),l=cn("source",a),d=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
|
|
if(${d}) {
|
|
`,a===1?"":`}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
|
|
if(${d}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
|
|
${p[m]}
|
|
if (${c}) {
|
|
result[${m}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${m}] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`;h+=a===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},dw=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WG(r.shape,s,i):new LG(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},BG={kernelName:zs,backendName:"webgl",kernelFunc:dw},VG=`
|
|
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);
|
|
`,jG=`
|
|
// 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));
|
|
`+yh+`
|
|
return result;
|
|
`,UG=tn({opSnippet:VG,packedOpSnippet:jG}),HG={kernelName:_s,backendName:"webgl",kernelFunc:UG};function GG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],d=k.parseAxisParam(s,r.shape),u=d,p=C.getAxesPermutation(u,o),c=r;p!=null&&(c=hn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=C.getInnerMostAxes(u.length,o),l.push(c)),C.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:A,outDtype:y}=iW(c.shape,c.dtype,m,u);h=n.makeTensorInfo(A,y,f)}else{let[m,f]=C.computeOutAndReduceShapes(c.shape,u),A=k.sizeFromShape(f),y=Ae({inputs:{x:c},backend:n,attrs:{shape:[-1,A]}}),g=dc(r.dtype),x=Ni(y,g,"prod",n);h=Ae({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=C.expandShapeToKeepDim(h.shape,d);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var qG={kernelName:Lo,backendName:"webgl",kernelFunc:GG},pw=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=oW(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},XG={kernelName:Tu,backendName:"webgl",kernelFunc:pw},KG="return 1.0 / x;",ZG=qe({opSnippet:KG}),YG={kernelName:Wo,backendName:"webgl",kernelFunc:ZG},JG=ba+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,QG=`
|
|
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;
|
|
`,eq=qe({opSnippet:JG,packedOpSnippet:QG}),tq={kernelName:Ls,backendName:"webgl",kernelFunc:eq},nq=ba+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,aq=`
|
|
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;
|
|
`,rq=qe({opSnippet:nq,packedOpSnippet:aq}),sq={kernelName:Bs,backendName:"webgl",kernelFunc:rq},iq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.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 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);
|
|
}
|
|
`}},oq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]},
|
|
${d[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.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 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 lq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new oq(r.shape,l,d,s,i):new iq(r.shape,l,d,s,i);return n.runWebGLProgram(u,[r],"float32")}var uq={kernelName:Ws,backendName:"webgl",kernelFunc:lq},dq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],d=o[0]/l[0],u=o[1]/l[1],p=1/d,c=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(c)*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(${d});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${m});
|
|
|
|
// 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 >= ${s}) {
|
|
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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${a-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 pq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new dq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var cq={kernelName:ec,backendName:"webgl",kernelFunc:pq},hq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},fq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]},
|
|
${d[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.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 = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function mq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new fq(r.shape,l,d,s,i):new hq(r.shape,l,d,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var Aq={kernelName:Eu,backendName:"webgl",kernelFunc:mq},yq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],d=o[0]/l[0],u=o[1]/l[1],p=1/d,c=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${d});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${m});
|
|
|
|
// 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 >= ${s}) {
|
|
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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${o[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function gq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new yq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var xq={kernelName:Qp,backendName:"webgl",kernelFunc:gq},bq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=lt(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},vq=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-${n} tensor is not yet supported`);this.outputShape=e;let a=cn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${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() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${o(a.slice())};
|
|
if(${r}){
|
|
result.g = ${l(a.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${d(a.slice())};
|
|
if(${r}) {
|
|
result.a = ${u(a.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function d(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,g)=>c(g,h)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function wq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Ln({inputs:{x:r},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vq(r.shape,o):new bq(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var kq={kernelName:Vs,backendName:"webgl",kernelFunc:wq},Iq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=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=`
|
|
uniform vec4 params;
|
|
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 < ${a} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},Sq={kernelName:tl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Iq(a.shape,s),[d,u]=C.getImageCenter(i,a.shape[1],a.shape[2]),p=l.getCustomSetupFunc(d,u,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,p)}},Nq=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Tq=qe({opSnippet:Nq}),Eq={kernelName:js,backendName:"webgl",kernelFunc:Tq},Cq="return inversesqrt(x);",Rq=qe({opSnippet:Cq,cpuKernelImpl:lW}),Mq={kernelName:Us,backendName:"webgl",kernelFunc:Rq},cw=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(r.length),l=lt(s.length),d="";n===1?d="i":n===2&&(d="i, j");let u=`getIndices(${d})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${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 < ${t}; j++) {
|
|
int index = round(${u});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${c};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function Fq(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:d,strides:u,outputSize:p}=C.calculateShapes(s,r,i),c=[p/d,d];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,d]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new cw(l,o,h.shape.length,m.shape.length,u,c),y=n.runWebGLProgram(A,[m,h,f],m.dtype),g=Ae({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var $q={kernelName:Vo,backendName:"webgl",kernelFunc:Fq},Dq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let d=0;d<t.length;d++)l.push(`${i[d]}`),d<e&&o.push(`${i[d]}`);a=o.join(),r=l.join()}let s=lt(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${a});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function Oq(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new Dq(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ia(r.dtype,s.dtype))}var zq={kernelName:jo,backendName:"webgl",kernelFunc:Oq},_q=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${C.SELU_SCALEALPHA};
|
|
float scale = ${C.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Pq=qe({opSnippet:_q}),Lq={kernelName:Uo,backendName:"webgl",kernelFunc:Pq},Wq="return 1.0 / (1.0 + exp(-1.0 * x));",Bq=qe({opSnippet:Wq}),Vq={kernelName:Gs,backendName:"webgl",kernelFunc:Bq},jq=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Uq=qe({opSnippet:jq}),Hq={kernelName:qo,backendName:"webgl",kernelFunc:Uq},Gq=Tv+`
|
|
return sin(x);
|
|
`,qq=qe({opSnippet:Gq}),Xq={kernelName:Hs,backendName:"webgl",kernelFunc:qq},Kq=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Zq=qe({opSnippet:Kq}),Yq={kernelName:Go,backendName:"webgl",kernelFunc:Zq},Jq=`
|
|
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;
|
|
`,Qq=qe({opSnippet:Jq}),eX={kernelName:Xo,backendName:"webgl",kernelFunc:Qq},tX=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let d=[],u=dw({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=C.getReshaped(u.shape,s,o,!1),c=C.getPermuted(p.length,s.length,!1),h=C.getReshapedPermuted(u.shape,s,o,!1),m=Ae({inputs:{x:u},backend:n,attrs:{shape:p}}),f=hn({inputs:{x:m},backend:n,attrs:{perm:c}}),A=Ae({inputs:{x:f},backend:n,attrs:{shape:h}});return d.push(u),d.push(m),d.push(f),d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},nX={kernelName:Cu,backendName:"webgl",kernelFunc:tX};function aX(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),d=n.readSync(s.dataId),u=n.readSync(i.dataId)[0],[p,c,h,m,f]=dW(o,a.shape,a.dtype,l,r.dtype,d,u);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(A=>Number(A)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var rX={kernelName:tc,backendName:"webgl",kernelFunc:aX};function sX(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[d,u,p]=pW(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(u,a.dtype,d),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var iX={kernelName:nc,backendName:"webgl",kernelFunc:sX};function oX(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:d,strides:u,outputSize:p}=C.calculateShapes(s,r,o),c=!1,h=new cw(d,l,r.shape.length,s.shape.length,u,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var lX={kernelName:ac,backendName:"webgl",kernelFunc:oX};function uX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=C.prepareSplitSize(r,s,o),d=r.shape.length,u=new Array(d).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let m=gd({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=c,m})}var dX={kernelName:Ko,backendName:"webgl",kernelFunc:uX},pX="return sqrt(x);",cX=qe({opSnippet:pX}),hX={kernelName:qs,backendName:"webgl",kernelFunc:cX},fX="return x * x;",mX=qe({opSnippet:fX}),AX={kernelName:Ru,backendName:"webgl",kernelFunc:mX},hw="return (a - b) * (a - b);",yX=tn({opSnippet:hw,packedOpSnippet:hw}),gX={kernelName:Zs,backendName:"webgl",kernelFunc:yX};function xX({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=ba+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Lr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var bX={kernelName:Nr,backendName:"webgl",kernelFunc:xX},vX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=lt(n.length),s=lt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,d)=>(o++,n.length===1?`coords * strides[${d}] + begin[${d}]`:`coords[${o-1}] * strides[${d}] + begin[${d}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function wX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:d,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=ln.sliceInfo(r.shape,s,i,o,l,d,u,p,c),x=Ae({inputs:{x:r},backend:n,attrs:{shape:y}}),w;if(h){let v=gd({inputs:{x},backend:n,attrs:{begin:m,size:A}});w=Ae({inputs:{x:v},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(v)}else if(g.some(v=>v===0))w=n.makeTensorInfo(g,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let v=n.texData.get(x.dataId).values,N=We(x.shape,x.dtype,v),T=cW(g,N,f,m);w=n.makeTensorInfo(g,x.dtype,T.values)}else{let v=new vX(m,f,g);w=n.runWebGLProgram(v,[x],x.dtype)}let b=Ae({inputs:{x:w},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),b}var kX={kernelName:Zo,backendName:"webgl",kernelFunc:wX},IX="return tan(x);",SX=qe({opSnippet:IX}),NX={kernelName:Js,backendName:"webgl",kernelFunc:SX},TX=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,EX=qe({opSnippet:TX}),CX={kernelName:Qs,backendName:"webgl",kernelFunc:EX},MX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=lt(this.rank),r=RX(e);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function RX(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function fw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,d=We(r.shape,r.dtype,l),u=fW(d,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new MX(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var FX={kernelName:Sr,backendName:"webgl",kernelFunc:fw};function $X(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,d]=mW(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(d.shape,d.dtype,d.values)]}var DX={kernelName:Yo,backendName:"webgl",kernelFunc:$X},OX=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${o} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 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 (${o} == 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(${e}));
|
|
|
|
if (${i} == 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 zX(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:d}=a,[u,p,c,h]=r.shape,[m,f]=d!=null?d:[p,c],A=[u,m,f,h],y=new OX(p,c,i,o,l,A);return n.runWebGLProgram(y,[r,s],"float32")}var _X={kernelName:Jo,backendName:"webgl",kernelFunc:zX};function PX(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Cl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:d}=AW(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([d.length],"int32",d)]}var LX={kernelName:rc,backendName:"webgl",kernelFunc:PX};function WX(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],d=new Array(o-1),u=0;for(let f=0;f<o;f++)f!==s&&(d[u++]=i.shape[f]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){c[s]=f;let A=gd({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});m[f]=y,p.push(A)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var BX={kernelName:Qo,backendName:"webgl",kernelFunc:WX},VX=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",d=Math.floor(n/4)*4,u=n%4,p=`
|
|
sumValue += dot(values, segFilter);
|
|
`,c="";r%n>0&&(c=`
|
|
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 = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
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(
|
|
${s})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${s})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${d}; 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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${d};
|
|
if (${u===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
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===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
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function jX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],d=0,u=C.getAxesPermutation([d],o),p=r;u!=null&&(p=hn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),d=C.getInnerMostAxes(1,o)[0]);let c=C.segment_util.computeOutShape(p.shape,d,i),h=k.sizeFromShape([p.shape[d]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=dc(r.dtype),A=(w,b,v,N,T)=>{let R=w.shape[0],$=w.shape[1],z=C.segment_util.segOpComputeOptimalWindowSize($,T),P={windowSize:z,inSize:$,batchSize:R,numSegments:T},V=new VX(P,b),j=n.compileAndRun(V,[w,v],N);if(l.push(j),j.shape[1]===T)return j;let U=pw({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),X=fw({inputs:{x:U},backend:n,attrs:{reps:[$/z]}});return l.push(U),l.push(X),A(j,b,X,N,T)},y=A(m,"unsortedSegmentSum",s,f,i),g=Ae({inputs:{x:y},backend:n,attrs:{shape:c}}),x=g;if(u!=null){l.push(g);let w=C.getUndoAxesPermutation(u);x=hn({inputs:{x},backend:n,attrs:{perm:w}})}return l.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var UX={kernelName:Mu,backendName:"webgl",kernelFunc:jX},HX=[CH,FH,fB,AB,xB,wB,IB,TB,CB,MB,OB,_B,WB,jB,ZB,GB,QB,aV,tV,oV,uV,pV,mV,wV,IV,RV,FV,zV,LV,KW,UV,ej,nj,XV,ij,lj,rj,pj,fj,yj,xj,vj,Ij,Rj,Fj,Nj,Oj,Pj,Wj,Uj,Xj,Jj,tU,nU,aU,sU,oU,uU,pU,hU,yU,vU,kU,SU,EU,FU,zU,WU,XW,VU,VV,HU,XU,YU,YW,tH,sH,oH,fH,pH,gH,vH,SH,DH,VH,WH,GH,XH,ZH,PH,JH,eG,rG,lG,cG,bG,nB,wG,SG,EG,MG,NV,DG,zG,PG,BG,HG,QW,qG,XG,TV,AG,YG,sq,tq,rB,uq,cq,Aq,xq,kq,Sq,Eq,Mq,$q,zq,Lq,Vq,Hq,Xq,Yq,bV,gG,eX,nX,rX,iX,lX,dX,hX,AX,gX,bX,kX,yG,pB,NX,CX,FX,DX,_X,cB,LX,BX,UX,OG];for(let e of HX)ri(e);var Sn;(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"})(Sn||(Sn={}));var bd;(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"})(bd||(bd={}));var mw;function GX(e){mw=e.wasm.cwrap(ti,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let T=n.dataIdMap.get(i.dataId);if(T.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${T.shape.length}.`);m=T.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,A=bd[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],g=d?s.shape[1]:s.shape[2],x=r.shape[0],w=n.makeOutput([x,y,g],r.dtype),b=n.dataIdMap.get(w.dataId).id,v=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return mw(c,v,r.shape.length,h,N,s.shape.length,l,d,A,m,f,p||0,b),w}var XX={kernelName:ti,backendName:"wasm",setupFunc:GX,kernelFunc:qX};function fn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),d=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,d),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var KX=fn(eo);function mn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:d,b:u}=l,p=o.dataIdMap.get(d.dataId).id,c=o.dataIdMap.get(u.dataId).id,h=n!=null?n:d.dtype,m=C.assertAndGetBroadcastShape(d.shape,u.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let A=new Uint8Array(new Int32Array(d.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(f.dataId).id,x=()=>a(p,A,d.shape.length,c,y,u.shape.length,Sn[d.dtype],g);if(t&&d.dtype==="float32")return x(),f;let w=C.getBroadcastDims(d.shape,m),b=C.getBroadcastDims(u.shape,m),v=w.every((T,R)=>T===R),N=b.every((T,R)=>T===R);if(v&&N)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${d.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var ZX=!0,YX=mn(kr,ZX),Aw;function JX(e){Aw=e.wasm.cwrap(os,null,["array","number","number","number"])}function QX(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return Aw(s,r.length,Sn[a.dtype],i),a}var eK={kernelName:os,backendName:"wasm",setupFunc:JX,kernelFunc:QX};function Nh(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var tK={kernelName:Is,backendName:"wasm",kernelFunc:Nh},yw;function nK(e){yw=e.wasm.cwrap(ei,null,["number","array","number","number","number","array","number"])}function Th(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=rK(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=aK(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Nh({inputs:t,backend:n});return m.shape=o,m}let d=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(d.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return yw(u,h,l.shape.length,Sn[l.dtype],p,c,s.length),d}function aK(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function rK(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var sK={kernelName:ei,backendName:"wasm",kernelFunc:Th,setupFunc:nK};function Br(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=C.getAxesPermutation(i,r),l=null,d=!1;if(o!=null){let u=new Array(r);for(let c=0;c<u.length;c++)u[c]=a[o[c]];i=C.getInnerMostAxes(i.length,r),l=Th({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(d=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:d}}var gw;function iK(e){gw=e.wasm.cwrap(ao,null,["number, number, number"])}function oK(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:c}=Br(i,r,t);if(c){let g=t.dataIdMap.get(d.dataId).id;l=d,o=g}let h=l.shape.length;C.assertAxesAreInnerMostDims("all",u,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,u),A=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;gw(o,A,g)}if(c&&t.disposeData(d.dataId),s){let g=C.expandShapeToKeepDim(y.shape,p);y.shape=g}return y}var lK={kernelName:ao,backendName:"wasm",setupFunc:iK,kernelFunc:oK},xw;function uK(e){xw=e.wasm.cwrap(ro,null,["number, number, number"])}function dK(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:c}=Br(i,r,t);if(c){let g=t.dataIdMap.get(d.dataId).id;l=d,o=g}let h=l.shape.length;C.assertAxesAreInnerMostDims("any",u,h);let[m,f]=C.computeOutAndReduceShapes(l.shape,u),A=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;xw(o,A,g)}if(c&&t.disposeData(d.dataId),s){let 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AK(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:d}=n,u=C.computePool2DInfo(r.shape,i,o,1,l,d),p=u.filterHeight,c=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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Please use 'channelsLast'.`);let P=a.makeOutput(m.outShape,"float32"),V=a.dataIdMap.get(P.dataId).id;return Sw(i,r.shape[0],r.shape[1],r.shape[2],o,f,A,y,g,x,w,z,b,v,N,T,R,$,V),P}var RK={kernelName:hs,backendName:"wasm",setupFunc:EK,kernelFunc:CK},Nw;function MK(e){Nw=e.wasm.cwrap(fs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function FK(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:d,inputShape:u}=a,p=1,c=C.convertConv2DDataFormat(l),h=C.computeConv2DInfo(u,s.shape,i,p,o,d,!1,c),{batchSize:m,filterHeight:f,filterWidth:A,inChannels:y,inHeight:g,inWidth:x,outChannels:w,outHeight:b,outWidth:v,strideHeight:N,strideWidth:T}=h,R=f-1-h.padInfo.top,$=A-1-h.padInfo.left,z=h.dataFormat==="channelsLast",P=k.computeStrides(h.inShape),V=k.computeStrides(r.shape),[j,U,X]=k.computeStrides(s.shape),G=P[0],ee=z?P[1]:P[2],Y=z?P[2]:1,re=z?1:P[1],ne=V[0],ie=z?V[1]:V[2],Q=z?V[2]:1,de=z?1:V[1],oe=t.makeOutput(h.inShape,"float32"),ye=t.dataIdMap.get(oe.dataId).id,ce=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(s.dataId).id;return Nw(ce,Ie,m,f,A,g,x,y,b,v,w,N,T,R,$,j,U,X,G,ee,Y,re,ne,ie,Q,de,ye),oe}var $K={kernelName:fs,backendName:"wasm",setupFunc:MK,kernelFunc:FK},DK=fn(ms),k1;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(k1||(k1={}));var Tw;function OK(e){Tw=e.wasm.cwrap(ho,null,["number","number","number","number","array","number","number","number","number","number"])}function zK(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:d}=n,u=l.shape[0],[p,c]=i,h=[u,p,c,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=Eh({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let A=m.id,y=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(d.dataId).id,x=t.makeOutput(h,"float32"),w=t.dataIdMap.get(x.dataId).id,b=new Uint8Array(new Int32Array(o.shape).buffer);return Tw(A,y,g,u,b,p,c,k1[r],s,w),f!=null&&t.disposeData(f.dataId),x}var _K={kernelName:ho,backendName:"wasm",setupFunc:OK,kernelFunc:zK},Ew;function PK(e){Ew=e.wasm.cwrap(As,null,["number","number","number","number","number","number"])}function LK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let d=C.getAxesPermutation([s],l),u=r;d!==null&&(u=Th({inputs:{x:r},attrs:{perm:d},backend:n}));let p=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[p],l);let c=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(c.dataId).id;Ew(m,i?1:0,o?1:0,h,f,Sn[r.dtype]);let A=c;if(d!==null){let y=C.getUndoAxesPermutation(d);A=Th({inputs:{x:c},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(c.dataId)}return A}var WK={kernelName:As,backendName:"wasm",setupFunc:PK,kernelFunc:LK},Cw;function BK(e){Cw=e.wasm.cwrap(fo,null,["number","number","number","array","number","array","array","number","number"])}function VK(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],d=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=d*s,h=u/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=t.makeOutput(m,"float32"),A=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),g=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),w=t.dataIdMap.get(f.dataId).id;return Cw(A,s,i==="NHWC"?1:0,y,r.shape.length-1,g,x,m.length,w),f}var jK={kernelName:fo,backendName:"wasm",setupFunc:BK,kernelFunc:VK},Rw;function UK(e){Rw=e.wasm.cwrap(ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function HK(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:d,pad:u,dimRoundingMode:p}=n,c=d==null?[1,1]:d,h=C.computeConv2DInfo(r.shape,s.shape,l,c,u,p,!0),m=h.filterHeight,f=h.filterWidth,A=h.padInfo.top,y=h.padInfo.right,g=h.padInfo.bottom,x=h.padInfo.left,w=h.dilationHeight,b=h.dilationWidth,v=h.strideHeight,N=h.strideWidth,T=h.inChannels,R=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let z=a.makeOutput(h.outShape,"float32"),P=a.dataIdMap.get(z.dataId).id;return Rw(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,A,y,g,x,$,w,b,v,N,T,R,P),z}var GK={kernelName:ys,backendName:"wasm",setupFunc:UK,kernelFunc:HK},qK=!1,XK=mn(yo,qK,"bool"),KK=fn(xs);function I1(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),va({inputs:{x:r},backend:a,attrs:{shape:o}})}var ZK={kernelName:go,backendName:"wasm",kernelFunc:I1};function YK(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var JK={kernelName:wu,backendName:"wasm",kernelFunc:YK},Mw;function QK(e){Mw=e.wasm.cwrap(bo,null,["number","number","number","number","number","number"])}function eZ(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,d,u]=a.shape;return Mw(s,o,l,d,u,i),r}var tZ={kernelName:bo,backendName:"wasm",kernelFunc:eZ,setupFunc:QK},nZ=fn(bs),aZ=!1,rZ=mn(vs,aZ),Fw;function sZ(e){Fw=e.wasm.cwrap(ws,null,["number","number","number","number","number","number","number"])}function iZ(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:d}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=d!=null?t.dataIdMap.get(d.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let A=t.dataIdMap.get(f.dataId).id;return Fw(u,p,c,h,m,r,A),f}var oZ={kernelName:ws,backendName:"wasm",setupFunc:sZ,kernelFunc:iZ},$w;function lZ(e){$w=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function uZ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(r.shape,s.shape,l,u,d,c),A=bd[h];if(A==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,g=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let Q=a.dataIdMap.get(i.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 (${x})`);w=Q.id}let b=f.filterHeight,v=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,R=f.padInfo.bottom,$=f.padInfo.left,z=f.dilationHeight,P=f.dilationWidth,V=f.strideHeight,j=f.strideWidth,U=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let re=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(re.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return $w(y,G,ee,Y,g,b,v,w,N,T,R,$,X,z,P,V,j,U,x,A,ie,m||0,ne),re}var dZ={kernelName:ni,backendName:"wasm",setupFunc:lZ,kernelFunc:uZ},Dw;function pZ(e){Dw=e.wasm.cwrap(ai,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 cZ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=C.computeConv2DInfo(r.shape,s.shape,l,u,d,c,!0),A=bd[h];if(A==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,g=a.dataIdMap.get(s.dataId).id,x=f.outChannels,w=0;if(i!=null){let Q=a.dataIdMap.get(i.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 (${x})`);w=Q.id}let b=f.filterHeight,v=f.filterWidth,N=f.padInfo.top,T=f.padInfo.right,R=f.padInfo.bottom,$=f.padInfo.left,z=f.dilationHeight,P=f.dilationWidth,V=f.strideHeight,j=f.strideWidth,U=f.inChannels,X=f.padInfo.type==="SAME"?1:0,G=f.batchSize,ee=f.inHeight,Y=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. 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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}}},tae=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=tae++,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=dr(n)+"_"+Vh(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 a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ka(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new B(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Nn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Nn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ur(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,x=y.nodeIndex,w=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(w)}for(let y of this.inputs){let g=y.sourceLayer,x=y.nodeIndex,w=y.tensorIndex;ja(x===0,"input layer has >1 nodes"),ja(w===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(w)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof Vl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,g,x,w,b,v)=>{(w==null||b==null||v==null)&&(w=y.sourceLayer,b=y.nodeIndex,v=y.tensorIndex);let N=w.inboundNodes[b];if(x.indexOf(N)!==-1)throw new ka(`The tensor ${y.name} at layer "${w.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(Ga.nodeKey(w,b)),w.id in s||(s[w.id]=Object.keys(s).length),x.indexOf(N)===-1&&x.push(N);let T=N.inboundLayers.length;for(let R=0;R<T;R++){let $=N.inputTensors[R],z=N.inboundLayers[R],P=N.nodeIndices[R],V=N.tensorIndices[R];o($,g,x,z,P,V)}for(g.push(N);x.indexOf(N)>=0;)x.splice(x.indexOf(N),1);i.push(N)},l=[],d=[];for(let y of this.outputs)o(y,l,d);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];g=Math.max(g,x),a[y.outboundLayer.id]=g,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let w=0;w<y.inboundLayers.length;w++){let b=y.inboundLayers[w],v=y.nodeIndices[w],N=b.inboundNodes[v],T=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,T),n[N.id]=N}}let p={};for(let y in t){let g=t[y];g in p||(p[g]=[]),p[g].push(n[y])}let c={};for(let y in a){let g=a[y];g in c||(c[g]=[]),c[g].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(Mh);this.layers=[];for(let y of h){let g=c[y];g.sort((x,w)=>{let b=s[x.id],v=s[w.id];return b<v?-1:b>v?1:0});for(let x of g)x instanceof Ga&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(y=>parseInt(y,10)).sort(Mh);let m=this.inputs.slice(),f=[];for(let y of h)for(let g of p[y]){let x=g.outboundLayer;if(x!=null){for(let w of g.inputTensors)if(m.indexOf(w)===-1)throw new ka(`Graph disconnected: cannot obtain value for tensor ${w} at layer "${x.name}". 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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={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new B(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new B(`${s.length} of ${a} weights are not set: ${s}`)}ey(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${ly}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=oy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return W(()=>{e=ft(e);let n=new $i;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Dd(this.outputs,n,t)})}computeMask(e,t){return W(()=>{e=ft(e);let n;return t==null?n=Ti(null,e.length):n=ft(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=jh(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],d=o.name+"_0_0";n[d]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Mh);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let d=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(d.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],A=l.nodeIndices[m],y=l.tensorIndices[m],g=`${f.name}_${A}_${y}`,x=n[g];u.push(x)}let p=d.computeOutputShape(Nn(u)),c=jh(p),h=d.inboundNodes.indexOf(l);for(let m=0;m<c.length;m++){let f=`${d.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],d=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${d}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];ja(o in n),r.push(n[o])}return Nn(r)}runInternalGraph(e,t){t==null&&(t=Ti(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],d=e[o],u=t[o];n[l.id]=[d,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Mh);for(let o of a){let l=this.nodesByDepth[o];for(let d of l){let u=d.outboundLayer,p=d.inputTensors,c=d.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,A,y,g;if(d.callArgs!=null&&(m=d.callArgs),h.length===1){let[x,w]=h[0];m.mask==null&&(m.mask=w),y=ft(u.call(x,m)),g=ft(u.computeMask(x,w)),f=[x],A=[w]}else f=h.map(x=>x[0]),A=h.map(x=>x[1]),m.mask==null&&(m.mask=A),y=ft(u.call(f,m)),g=ft(u.computeMask(f,A));if(u.activityRegularizer)throw new Oe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let w=c[x],b=y[x],v=g[x];n[w.id]=[b,v]}}}}let r=[],s=[],i=[];for(let o of this.outputs){ja(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,d]=n[o.id];i.push(l.shape),r.push(l),s.push(d)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof Ga?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=Ga.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`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 B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return W(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=Ga.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],c=Ga.nodeKey(s,u),h={};if(this.containerNodes.has(c)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let A=p.inboundLayers[f],y=p.nodeIndices[f],g=p.tensorIndices[f],x=Ga.nodeKey(A,y),w=t[x];w==null&&(w=0),m.push([A.name,w,g,h])}l.push(m)}}}let d={};d.name=s.name,d.className=i,d.config=o,d.inboundNodes=l,n.push(d)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Ga.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let d=t[l];d==null&&(d=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,d,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Ga.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let d=t[l];d==null&&(d=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,d,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,A){f.name in s?s[f.name].push(A):s[f.name]=[A]}function o(f,A){let y=[],g;for(let x of A){let w=x[0],b=x[1],v=x[2];if(g=x[3]==null?{}:x[3],!(w in r)){i(f,A);return}let N=r[w];if(N.inboundNodes.length<=b){i(f,A);return}let T=N.inboundNodes[b];y.push(T.outputTensors[v])}y.length>0&&f.apply(Nn(y),g)}function l(f){let A=f.name,y=Ta(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[A]=y,f.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let d=t.name,u=t.layers;for(let f of u)l(f);for(;!ste(s);)for(let f of u){let A=r[f.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let p=[],c=[],h=t.inputLayers;for(let f of h){let A=f[0],y=f[1],g=f[2];ja(A in r);let x=r[A].inboundNodes[y].outputTensors;p.push(x[g])}let m=t.outputLayers;for(let f of m){let A=f[0],y=f[1],g=f[2];ja(A in r);let x=r[A].inboundNodes[y].outputTensors;c.push(x[g])}return new e({inputs:p,outputs:c,name:d})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function f4(e,t){return Oae(e,t,"classWeight")}async function m4(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=W(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await r.data());Ee(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function A4(e,t,n){if(n instanceof Le)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function Pae(e){if(e.length===3)throw new Oe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Wae(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.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,s,i;if(r)if(g4(n.validationData))k.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 A=Pae(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),d;r?d=l.slice().concat(l.map(A=>"val_"+A)):d=l.slice();let u=n4(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=a4(u,p,n.epochs,null,null,Lae(t,n),null,r,d);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let A={};await c.onEpochBegin(m);let y=0,g=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};vy.className="ThresholdedReLU";ae.registerClass(vy);var wy=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new fy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=_e(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}};wy.className="Softmax";ae.registerClass(wy);function Gl(e,t,n){if(typeof e=="number")return Ti(e,t);if(e.length!==t)throw new B(`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 Ea(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function qa(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+Ur([n-t,0]);else if(a==="same")e=e*t;else throw new B(`Unsupport padding mode: ${a}.`);return e}function ky(e,t){return W(()=>(Ct(t),t==="channelsFirst"?Ze(e,[0,2,3,1]):e))}function W4(e,t){return W(()=>(Ct(t),t==="channelsFirst"?Ze(e,[0,2,3,4,1]):e))}function ire(e,t,n,a=1,r="valid",s,i=1){return W(()=>{if(s==null&&(s=wa()),Ct(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ze(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=bc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Sa(o,n)),o})}function B4(e,t,n,a=[1,1],r="valid",s,i,o=null){return W(()=>{if(s==null&&(s=wa()),Ct(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=ky(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=_r.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ze(l,[0,3,1,2])),l})}function ore(e,t,n,a=[1,1,1],r="valid",s,i){return W(()=>{if(s==null&&(s=wa()),Ct(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=W4(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=pA(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Sa(o,n)),s==="channelsFirst"&&(o=Ze(o,[0,4,1,2,3])),o})}var Iy=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Iy.verifyArgs(t),this.rank=e,Gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(t.kernelSize,e,"kernelSize"),this.strides=Gl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Qn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=qr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=At(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=yt(t.biasRegularizer),this.activityRegularizer=yt(t.activityRegularizer),this.dilationRate=Gl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(ja("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!F1(e.kernelSize,"number",1,3))throw new B(`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:Gr(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},_d=class extends Iy{constructor(e,t){super(e,t);this.kernel=null,_d.verifyArgs(t),this.filters=t.filters,Gt(this.filters,"filters"),this.kernelInitializer=At(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=yt(t.kernelRegularizer)}build(e){e=at(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 W(()=>{e=_e(e);let n,a=this.bias==null?null:this.bias.read(),r=S6(this.activation.getClassName());if(r!=null&&this.rank===2)n=B4(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=ire(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=B4(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=ore(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=at(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 s=Ea(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:It(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Lt(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 B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Pd=class extends _d{constructor(e){super(2,e);Pd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!F1(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Pd.className="Conv2D";ae.registerClass(Pd);var Ld=class extends _d{constructor(e){super(3,e);Ld.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 B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Ld.className="Conv3D";ae.registerClass(Ld);var Sy=class extends Pd{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{let n=_e(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],d=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=qa(o,p,d,this.padding),m=qa(l,c,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,1]));let A=vc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Ze(A,[0,3,1,2])),this.bias!=null&&(A=Sa(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=at(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=qa(t[a],o,s,this.padding),t[r]=qa(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Sy.className="Conv2DTranspose";ae.registerClass(Sy);var Ny=class extends Ld{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==5)throw new B("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{let n=_e(e);if(n.shape.length!==5)throw new B(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],d=a[s],u=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],A=this.strides[2],y=qa(l,m,p,this.padding),g=qa(d,f,c,this.padding),x=qa(u,A,h,this.padding),w=[r,y,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,4,1]));let b=Db(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Ze(b,[0,4,1,2,3])),this.bias!==null&&(b=Sa(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=at(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],d=this.strides[0],u=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=qa(t[a],d,i,this.padding),t[r]=qa(t[r],u,o,this.padding),t[s]=qa(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ny.className="Conv3DTranspose";ae.registerClass(Ny);var V4=class extends _d{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 B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("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 B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=yt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=At(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=yt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=at(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${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 B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Ft({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return W(()=>{e=_e(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ze(e,[0,2,3,1])),n=CA(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Sa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ze(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=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseConstraint),e.pointwiseConstraint=Lt(this.pointwiseConstraint),e}};V4.className="SeparableConv";var Ty=class extends V4{constructor(e){super(2,e)}};Ty.className="SeparableConv2D";ae.registerClass(Ty);var e0=class extends _d{constructor(e){super(1,e);e0.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"&&!F1(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};e0.className="Conv1D";ae.registerClass(e0);var Ey=class extends Ge{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 W(()=>{if(e=_e(e),this.dataFormat==="channelsLast"){let n=Fh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Fh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Fh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Fh(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}};Ey.className="Cropping2D";ae.registerClass(Ey);var Cy=class extends Ge{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,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Tte(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 W(()=>{let n=_e(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ze(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ze(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="UpSampling2D";ae.registerClass(Cy);function lre(e,t,n=[1,1],a="valid",r,s){return W(()=>{r==null&&(r=wa()),Ct(r);let i=ky(e,r);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=fl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ze(i,[0,3,1,2])),i})}var Ry=class extends Iy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=At(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=yt(e.depthwiseRegularizer)}build(e){if(e=at(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 W(()=>{e=_e(e);let n=lre(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Sa(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ea(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ea(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=It(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseRegularizer),e}};Ry.className="DepthwiseConv2D";ae.registerClass(Ry);function j4(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function U4(e,t,n,a=!1,r,s,i=!1,o=!1){return W(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let d=[1,0].concat(Ia(2,l));if(t=Ze(t,d),s!=null)throw new Oe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=un(r,-1)),r=Ze(r,d)),a&&(t=zn(t,0),r!=null&&(r=zn(r,0)));let u=[],p,c=n,h=t.shape[0],m=ua(t),f;r!=null&&(f=ua(r));for(let y=0;y<h;++y){let g=m[y],x=W(()=>e(g,c));if(r==null)p=x[0],c=x[1];else{let w=W(()=>{let b=f[y],v=On(b).sub(b),N=x[0].mul(b).add(c[0].mul(v)),T=c.map((R,$)=>x[1][$].mul(b).add(R.mul(v)));return{output:N,newStates:T}});p=w.output,c=w.newStates}o&&u.push(p)}let A;return o&&(A=_n(u,1)),[p,A,c]})}var Ha=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new t0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("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 Ft({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 Ia(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){J1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return W(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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 Oe("Constants support is not implemented in RNN yet.");J1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Ft({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Oe("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`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=s.map(i=>new Ft({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new ur("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("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(a=>Rt([n,a])):this.states_=[Rt([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Rt([n,a])):this.states_[0]=Rt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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()):Ee(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new B(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Ut(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=j4(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ft({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Na){let o=[e].concat(s),l=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=d,u}else return super.apply(e,t)}call(e,t){return W(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=_e(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new B(`RNN Layer has ${s} 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 i={training:a},o=U4((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],d=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?d:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return W(()=>{let t=Rt(e.shape);return t=ke(t,[1,2]),t=Ed(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?P1(t,[1,n]):t):this.cell.stateSize>1?[P1(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()===Ha.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ta(a,n);return new e(Object.assign(t,{cell:r}))}};Ha.className="RNN";ae.registerClass(Ha);var Md=class extends Ge{},n0=class extends Md{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,Gt(this.units,"units"),this.activation=qr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Bl([1,Ur([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ur([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(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 W(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>On(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>On(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Ua(_(e,s),this.kernel.read()):r=Ua(e,this.kernel.read()),this.bias!=null&&(r=Sa(r,this.bias.read())),i!=null&&(n=_(n,i));let o=se(r,Ua(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};n0.className="SimpleRNNCell";ae.registerClass(n0);var My=class extends Ha{constructor(e){e.cell=new n0(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};My.className="SimpleRNN";ae.registerClass(My);var a0=class extends Md{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 B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Gt(this.units,"units"),this.activation=qr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=qr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Bl([1,Ur([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ur([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(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 W(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=_(e,r[0]));let d=Ua(e,this.kernel.read());this.useBias&&(d=Sa(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=_(a,s[0]));let u=this.recurrentKernel.read(),[p,c]=rn(u,[2*this.units,this.units],u.rank-1),h=Ua(a,p),[m,f,A]=rn(d,3,d.rank-1),[y,g]=rn(h,2,h.rank-1);i=this.recurrentActivation.apply(se(m,y)),o=this.recurrentActivation.apply(se(f,g));let x=Ua(_(o,a),c);l=this.activation.apply(se(A,x));let w=se(_(i,a),_(se(1,wt(i)),l));return[w,w]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};a0.className="GRUCell";ae.registerClass(a0);var Fy=class extends Ha{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 a0(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="GRU";ae.registerClass(Fy);var Wd=class extends Md{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,Gt(this.units,"units"),this.activation=qr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=qr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=At(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Bl([1,Ur([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ur([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=at(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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends pa{apply(i,o){let l=r.apply([s]),d=new Dh().apply([s]),u=r.apply([s*2]);return O6(O6(l,d),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return W(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,d,u;0<this.dropout&&this.dropout<1&&(e=_(e,s[0]));let p=Ua(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=_(a,i[0])),p=se(p,Ua(a,this.recurrentKernel.read())),this.useBias&&(p=Sa(p,this.bias.read()));let[c,h,m,f]=rn(p,4,p.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),d=se(_(l,r),_(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let A=_(u,this.activation.apply(d));return[A,A,d]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Gr(this.activation),recurrentActivation:Gr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),recurrentConstraint:Lt(this.recurrentConstraint),biasConstraint:Lt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Wd.className="LSTMCell";ae.registerClass(Wd);var $y=class extends Ha{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Wd(e),super(e)}call(e,t){return W(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};$y.className="LSTM";ae.registerClass($y);var t0=class extends Md{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return W(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){J1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ri(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ta(r,n));return new e({cells:a})}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 Q1(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}ey(t)}};t0.className="StackedRNNCells";ae.registerClass(t0);function Xr(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>_6(t(),n),i=()=>Rd(s,t,a);return!r||r<=1?Ut(i().clone()):Array(r).fill(void 0).map(i).map(o=>Ut(o.clone()))}var ure=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},H4=class extends Ha{constructor(e){if(e.unroll)throw new Oe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Oe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ft({ndim:5})]}call(e,t){return W(()=>{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,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 W(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Rt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){W(()=>{if(!this.stateful)throw new ur("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new B("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(()=>Rt(r)):this.states_=[Rt(r)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(r)):this.states_[0]=Rt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`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()):Ee(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],d=e[o?4:3],u=Ea(l,a[0],r,s[0],i[0]),p=Ea(d,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};H4.className="ConvRNN2D";var r0=class extends Wd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Gt(this.filters,"filters"),this.kernelSize=Gl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Gt(o,"kernelSize")),this.strides=Gl(a||1,2,"strides"),this.strides.forEach(o=>Gt(o,"strides")),this.padding=r||"valid",Qn(this.padding),this.dataFormat=s||"channelsLast",Ct(this.dataFormat),this.dilationRate=Gl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Gt(o,"dilationRate"))}build(e){var t;e=at(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,d=this.filters;o=new(t=class extends pa{apply(u,p){let c=l.apply([d]),h=Dn([d]),m=l.apply([d*2]);return W1([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return W(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Xr({ones:()=>On(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,re,ne)=>!re||!re[ne]?Y:_(re[ne],Y),d=l(a,o,0),u=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Xr({ones:()=>On(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),A=l(r,h,2),y=l(r,h,3),g=3,[x,w,b,v]=rn(this.kernel.read(),i,g),[N,T,R,$]=this.useBias?rn(this.bias.read(),i):[null,null,null,null];d=this.inputConv(d,x,N,this.padding),u=this.inputConv(u,w,T,this.padding),p=this.inputConv(p,b,R,this.padding),c=this.inputConv(c,v,$,this.padding);let[z,P,V,j]=rn(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,z),f=this.recurrentConv(f,P),A=this.recurrentConv(A,V),y=this.recurrentConv(y,j);let U=this.recurrentActivation.apply(se(d,m)),X=this.recurrentActivation.apply(se(u,f)),G=se(_(X,s),_(U,this.activation.apply(se(p,A)))),ee=_(this.recurrentActivation.apply(se(c,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ure(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=ar(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Sa(r,n,this.dataFormat):r}recurrentConv(e,t){return ar(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};r0.className="ConvLSTM2DCell";ae.registerClass(r0);var Dy=class extends H4{constructor(e){let t=new r0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Dy.className="ConvLSTM2D";ae.registerClass(Dy);var s0=class extends Ge{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=_e(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Rd(()=>_6(n,this.rate,r,this.seed),()=>n,a)}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()}};s0.className="Dropout";ae.registerClass(s0);var Oy=class extends s0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Oy.className="SpatialDropout1D";ae.registerClass(Oy);var zy=class extends Ge{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,Gt(this.units,"units"),this.activation=qr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=At(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=At(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=yt(e.kernelRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=at(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=at(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=_e(e),a=S6(this.activation.getClassName()),r;return a!=null?r=Ua(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Ua(n,this.kernel.read()),this.bias!=null&&(r=Sa(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Gr(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Dense";ae.registerClass(zy);var _y=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=at(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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W(()=>(e=_e(e),Mte(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="RepeatVector";ae.registerClass(Ly);var Wy=class extends Ge{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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else r*=l}let i=jr(e);if(s!==null){if(r===0||i%r!=0)throw new B(n);a[s]=i/r}else if(i!==r)throw new B(n);return a}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 W(()=>{this.invokeCallHook(e,t);let 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="Concatenate";ae.registerClass(Ky);function Bd(e,t){for(;e<0;)e+=t;return e}function dre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Oe("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 Oe("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return W(()=>{let i;if(a>r){i=a-r;let l=[];for(let d=0;d<i;++d)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let d=0;d<i;++d)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,d=s[1]===t.shape.length-1;o=e.matMul(t,l,d)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let d=[];for(let u=l;u<l+i;++u)d.push(u);o=o.squeeze(d)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Zy=class extends Oi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 Oe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new B(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Yy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return W(()=>{this.invokeCallHook(e,t);let n=_e(e);return Rd(()=>$h(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Yy.className="GaussianNoise";ae.registerClass(Yy);var Jy=class extends Ge{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 W(()=>{this.invokeCallHook(e,t);let n=_e(e);return this.rate>0&&this.rate<1?Rd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul($h(n.shape,1,a))},()=>n,t.training||!1):n})}};Jy.className="GaussianDropout";ae.registerClass(Jy);var Qy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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Ge{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=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.movingMeanInitializer=At(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=At(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer)}build(e){e=at(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ft({ndim:e.length,axes:{[t]:n}})];let 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extends Ge{constructor(e){if(e==null&&(e={}),super(e),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=At(e.betaInitializer||"zeros"),this.gammaInitializer=At(e.gammaInitializer||"ones"),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=at(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!==Vr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=_e(e),a=n.shape,r=a.length;return W(()=>{let s=!0,{mean:i,variance:o}=Rc(n,this.axis,s),l=Ti(1,r);for(let m of this.axis)l[m]=a[m];let d=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=d(this.gamma.read()),p=d(this.beta.read()),c=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(c.push(a[m]),h.push(1)):(c.push(1),h.push(a[m]));return i=i.tile(c),o=o.tile(c),u=u.tile(h),p=p.tile(h),Vd(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};t2.className="LayerNormalization";ae.registerClass(t2);function fre(e,t,n){return W(()=>{if(e.rank!==4)throw new B(`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 B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=wa()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=Yu(e,t,n,o):i=Gu(e,t,n,o),r==="channelsFirst"&&(i=Ze(i,[0,3,1,2])),i})}function G4(e,t,n,a,r,s){return W(()=>{Ct(r),C6(s),Qn(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=wa()),s==null&&(s="max"),e=W4(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=kA(e,t,n,o):i=oA(e,t,n,o),r==="channelsFirst"&&(i=Ze(i,[0,4,1,2,3])),i})}var q4=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),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 B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Gt(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 B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Qn(this.padding),this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){e=at(e);let t=Ea(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return W(()=>{this.invokeCallHook(e,t),e=Ed(_e(e),2);let n=this.poolingFunction(_e(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},a2=class extends q4{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ct(r),Qn(a),i0(e,t,n,a,r,"max")}};a2.className="MaxPooling1D";ae.registerClass(a2);var r2=class extends q4{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return 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t=Ea(t,this.poolSize[0],this.padding,this.strides[0]),n=Ea(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 W(()=>(this.invokeCallHook(e,t),this.poolingFunction(_e(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}},s2=class extends X4{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ct(r),Qn(a),i0(e,t,n,a,r,"max")}};s2.className="MaxPooling2D";ae.registerClass(s2);var i2=class extends X4{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ct(r),Qn(a),i0(e,t,n,a,r,"avg")}};i2.className="AveragePooling2D";ae.registerClass(i2);var K4=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),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 B(`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];Gt(this.poolSize,"poolSize"),Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Qn(this.padding),this.inputSpec=[new Ft({ndim:5})]}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ea(t,this.poolSize[0],this.padding,this.strides[0]),n=Ea(n,this.poolSize[1],this.padding,this.strides[1]),a=Ea(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return W(()=>(this.invokeCallHook(e,t),this.poolingFunction(_e(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}},o2=class extends K4{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ct(r),Qn(a),G4(e,t,n,a,r,"max")}};o2.className="MaxPooling3D";ae.registerClass(o2);var l2=class extends K4{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ct(r),Qn(a),G4(e,t,n,a,r,"avg")}};l2.className="AveragePooling3D";ae.registerClass(l2);var Z4=class extends Ge{constructor(e){super(e);this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Oe}},u2=class extends Z4{constructor(e){super(e||{})}call(e,t){return W(()=>{let n=_e(e);return kt(n,1)})}};u2.className="GlobalAveragePooling1D";ae.registerClass(u2);var d2=class extends Z4{constructor(e){super(e||{})}call(e,t){return W(()=>{let n=_e(e);return Kn(n,1)})}};d2.className="GlobalMaxPooling1D";ae.registerClass(d2);var Y4=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Oe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},p2=class extends Y4{call(e,t){return W(()=>{let n=_e(e);return this.dataFormat==="channelsLast"?kt(n,[1,2]):kt(n,[2,3])})}};p2.className="GlobalAveragePooling2D";ae.registerClass(p2);var c2=class extends Y4{call(e,t){return W(()=>{let n=_e(e);return this.dataFormat==="channelsLast"?Kn(n,[1,2]):Kn(n,[2,3])})}};c2.className="GlobalMaxPooling2D";ae.registerClass(c2);var J4=class extends Ge{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 a=t.layer,r=Ta(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},h2=class extends J4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=at(e),e.length<3)throw new B(`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=at(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return W(()=>(e=_e(e),U4((n,a)=>[_e(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};h2.className="TimeDistributed";ae.registerClass(h2);function mre(e){Ci(Nte,"BidirectionalMergeMode",e)}var Are="concat",f2=class extends J4{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ta(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ta(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Are:e.mergeMode,mre(this.mergeMode),e.weights)throw new Oe("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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Nn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=j4(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let d=n.map(u=>new 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|
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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),ha(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,Ut(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,a)=>this.write(n,t[a]))}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 a=0;a<this.size();a++)e.push(a)}if(e.length===0)return oa([],[0].concat(this.elementShape));let n=this.readMany(e);return ha(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),_n(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return oa([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return ha(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),ot(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,ua(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,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
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,s=[];W(()=>{t=H(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],d=[0,l,0],u=[1,e[o],r];s[o]=H(Re(t,d,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Ud=class{constructor(e,t,n,a=-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}`);ha(t,r.shape,"TensorList shape mismatch: "),Ut(r)}),this.idTensor=Se(0),this.maxNumElements=a,Ut(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Ud([...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.`);ha(e,this.elementShape,"TensorList shape mismatch: ");let a=jd(this.elementShape,this.tensors,e);return W(()=>{let r=this.tensors.map(s=>H(s,a));return _n(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=jd(this.elementShape,this.tensors,e),a=this.tensors.pop();return ha(a.shape,e,"TensorList shape mismatch: "),H(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ha(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Ut(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.`);ha(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=jd(this.elementShape,this.tensors,t);return H(this.tensors[e],a)}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.`);ha(this.elementShape,t.shape,"TensorList shape mismatch: "),Ut(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}`);ha(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=jd(this.elementShape,this.tensors,n);return e.length===0?oa([],[0].concat(a)):W(()=>{let r=e.map(s=>H(this.tensors[s],a));return _n(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);ha(this.elementShape,t,"TensorList shape mismatch: ");let n=jd(this.elementShape,this.tensors,t);return this.size()===0?oa([],[0].concat(n)):W(()=>{let a=this.tensors.map(r=>H(r,n));return ot(a,0)})}};function lse(e,t,n){let a=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);ha(r,t,"TensorList shape mismatch: ");let s=ua(e);return new Ud(s,t,a)}function use(e,t,n){return new Ud([],e,t,n)}function dse(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new Ud([],n,e.dtype,a),i=ua(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function pse(e,t,n){let a=0,r=t.map(u=>(a+=u,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=T2(s,n),o=a===0?0:e.size/a,l=W(()=>{let u=[];e=H(e,[1,a,o]);for(let p=0;p<t.length;++p){let c=p===0?0:r[p-1],h=[0,c,0],m=[1,t[p],o];u[p]=H(Re(e,h,m),i)}return e.dispose(),u}),d=new Ud([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)d.setItem(u,l[u]);return d}var cse=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let d=s;for(;l[0];){let u=d;d=await 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a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),d=I("name",e,t,n),u=new ose(d,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,Se(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let 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a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=use(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=lse(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=pse(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function C8(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=a==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let 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a=I("boxes",e,t,n),r=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var mse=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=E2(e,t,n),d=await Ye.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,l);return[d.selectedIndices,d.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=E2(e,t,n),l=I("padToMaxOutputSize",e,t,n),d=await Ye.nonMaxSuppressionPaddedAsync(a,r,s,i,o,l);return[d.selectedIndices,d.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=E2(e,t,n);return[await Ye.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=fe(I("condition",e,t,n),"bool"),r=[await zA(a)];return 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implemented`)}},wse=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Be(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[_b(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Ze(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[d,u]=I("args",e,t,n);return[_r.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:d,activation:r,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not 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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 $8(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,d=Object.keys(e).map(c=>Wn(c)[0]),u=[];a!=null&&(u=a.map(c=>Wn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((F8(c)||Cse(c)||Rse(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&d.indexOf(c.name)===-1&&u.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function Mse(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>Wn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.has(u.name)&&s.push(u)});let l=new Set,d=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||d.push(u),u.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>l.has(c.name))&&s.push(p)})}return d}var Fse=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],$se=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Dse=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function F8(e){return Fse.indexOf(e.op)>=0}function Cse(e){return $se.indexOf(e.op)>=0}function Rse(e){return Dse.indexOf(e.op)>=0}var C2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new C2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${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(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=$8(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return Mse(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[Wn(u)[0]]),r=t.map(u=>Wn(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},d={};return W(()=>{let u=new M8(this.weightMap,l,d,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=Wn(m),y=[];y[A]=e[m],p[f]=y});let c=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let A=R8(f,p,u,this._resourceManager);if(k.isPromise(A))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);p[f.name]=A,this.checkTensorForDisposal(f.name,f,p,u,c,r,h)}}return this.parent==null&&u.dispose(c),t.map(m=>An(m,p,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Lre(o.name,n,a);l!=null&&l.forEach(d=>{if(d&&!d.kept&&!r.has(d.id)){let u=i[d.id];u===1?(d.dispose(),delete i[d.id]):u!=null&&i[d.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new M8(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>An(p,i,s)),l=o.map(p=>p.id),d=Object.keys(e).map(p=>e[p].id),u=new Set([...l,...d,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(c=>{c&&!c.kept&&!c.isDisposed&&!u.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(g=>this.graph.nodes[Wn(g)[0]]),i=n.map(g=>Wn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:d,dynamicNode:u,syncInputs:p}=$8(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,w]=Wn(g),b=[];b[w]=e[g],h[x]=b});let m={},f=this.getFrozenTensorIds(h),A={};for(;c.length>0;){let g=this.processStack(s,c,t,h,A,f,i,m,l);await Promise.all(g)}u==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!F8(g)&&!An(g.name,h,t)).map(g=>g.name);if(y.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${d}]. ${g}`)}return h}processStack(e,t,n,a,r,s,i,o,l){let d=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let p="";if(u.node.op==="Enter"&&I("isConstant",u.node,a,n)&&([p]=cr(u.node.name,n)),a[u.node.name]==null){let c=R8(u.node,a,n,this._resourceManager);p||([p]=cr(u.node.name,n));let h=n.currentContext;k.isPromise(c)?d.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l),m))):(a[p]=c,this.checkTensorForDisposal(p,u.node,a,n,s,i,o),this.processChildNodes(u.node,t,n,a,r,l))}else this.processChildNodes(u.node,t,n,a,r,l)}return d}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=cr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!An(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!An(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Wn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Wn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Wn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Ose=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},zse="?tfjs-format=file",_se="model.json",D8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ose}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=wn.browserHTTPRequest(e,this.loadOptions);else{let t=wn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(wn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=wn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new C2(S8.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=S8.Instance.transformGraph(e.modelInitializer);this.initializer=new C2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=wn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Le)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function qt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${_se}${zse}`);let n=new D8(e,t);return await n.load(),n}var Pse="3.6.0",O8={};Fe(O8,{CSVDataset:()=>_8,Dataset:()=>ql,FileDataSource:()=>P8,TextLineDataset:()=>z8,URLDataSource:()=>L8,array:()=>Lse,csv:()=>Bse,func:()=>Vse,generator:()=>jse,microphone:()=>Hse,version_data:()=>Gse,webcam:()=>Use,zip:()=>Wse});var qse=Yi(Bg()),Xse=Yi(Bg());function Kse(e,t){return d0(e,t)}function d0(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Xl(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],l=d0(o,t,n,a);s[i]=l}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function Zse(e,t=B8){return W8(e,t)}function W8(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Xl(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(d=>d[i]),l=W8(o,t,n);s[i]=l}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function B8(e){return e===null?null:Xl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function V8(e,t){let n=new Map;d0(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return d0(e,t,n)}function Xl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Le))}function Jse(e){return e==null||Yse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Le||k.isTypedArray(e)}function Yse(e){return e===null||typeof e!="object"&&typeof e!="function"}function eie(e){return Kse(e,Qse)}function Qse(e){return e instanceof Le?{value:e.clone(),recurse:!1}:Xl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var j8=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},R2=class extends j8{constructor(){super(R2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};R2.INITIAL_CAPACITY=32;function U8(e){return new tie(e)}function M2(e){return new nie(e)}function aie(e,t){return new H8(e,t)}function sie(e,t=Kr.FAIL){return new rie(e,t)}var Xt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new cie(this,e)}filter(e){return new die(this,e)}map(e){return new pie(this,e)}mapAsync(e){return new G8(this,e)}serialMapAsync(e){return new G8(this,e).serial()}flatmap(e){return new hie(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new uie(this,e,t)}columnMajorBatch(e,t=!0,n=B8){return this.rowMajorBatch(e,t).map(a=>Zse(a,n))}concatenate(e,t){return new H8(U8([this,e]),t)}take(e){return e<0||e==null?this:new lie(this,e)}skip(e){return e<0||e==null?this:new oie(this,e)}prefetch(e){return new q8(this,e)}shuffle(e,t){return new fie(this,e,t)}serial(){return new iie(this)}},tie=class extends Xt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:eie(e),done:!1}}},nie=class extends Xt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},iie=class extends Xt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},oie=class extends Xt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ee(e.value)}return this.upstream.next()}},lie=class extends Xt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},uie=class extends Xt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},die=class extends Xt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},pie=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ya.getTensorsInContainer(e.value),n=this.transform(e.value),a=ya.getTensorsInContainer(n);for(let r of t)ya.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},cie=class extends Xt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},G8=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ya.getTensorsInContainer(e.value),n=await this.transform(e.value),a=ya.getTensorsInContainer(n);for(let r of t)ya.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},F2=class extends Xt{constructor(){super();this.outputQueue=new R2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},hie=class extends F2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ya.getTensorsInContainer(e.value),n=this.transform(e.value),a=ya.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)ya.isTensorInList(r,a)||r.dispose();return!0}},H8=class extends Xt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Kr;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Kr||(Kr={}));var rie=class extends Xt{constructor(e,t=Kr.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof Xt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await V8(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Kr.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Kr.SHORTEST:return{value:null,done:!0};case Kr.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},q8=class extends Xt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new j8(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},fie=class extends q8{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Xse.alea(n||k.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},ql=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
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|
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Bn(async()=>(await n.iterator()).columnMajorBatch(e,t,mie),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Bn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Bn(async()=>(await t.iterator()).filter(a=>W(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Bn(async()=>(await t.iterator()).map(n=>W(()=>e(n))),this.size)}mapAsync(e){let t=this;return Bn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Bn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Bn(async()=>{let a=M2(async()=>({value:await t.iterator(),done:!1}));return aie(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Bn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=qse.alea(t||k.now().toString());return Bn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Bn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ql.MAX_BUFFER_SIZE=1e4;function Bn(e,t=null){return new class extends ql{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Lse(e){return Bn(async()=>U8(e),e.length)}function Wse(e){if(!Xl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Bn(async()=>{let n=await V8(e,a=>{if(a instanceof ql)return{value:a.iterator(),recurse:!1};if(Xl(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return sie(n,Kr.SHORTEST)},t)}function mie(e){if(e===null)return null;let t=e[0];return Jse(t)?{value:Aie(e),recurse:!1}:{value:null,recurse:!0}}function Aie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Le?_n(e):oa(e)}var z8=class extends ql{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},p0='"',Hd=Symbol("out"),X8=Symbol("field"),c0=Symbol("quote"),$2=Symbol("quoteafterquote"),K8=Symbol("quoteinquote"),_8=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 z8(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.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&&k.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((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!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={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let d=Number(o);if(isNaN(d))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=d;else switch(i.dtype){case"float32":l=d;break;case"int32":l=Math.floor(d);break;case"bool":l=this.getBoolean(o);break;default:l=d}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=Hd;for(let i=0;i<r;i++)switch(s){case Hd:switch(e.charAt(i)){case p0:a=i+1,s=c0;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Hd;break;default:s=X8,a=i;break}break;case X8:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Hd,a=i+1;break;default:}break;case c0:switch(e.charAt(i)){case p0:s=$2;break;default:}break;case $2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Hd,a=i+1;break;case p0:s=c0;break;default:s=K8;break}break;case K8:switch(e.charAt(i)){case p0:s=c0;break;default:}break;default:}if(s===$2?n.push(e.substring(a,r-1)):n.push(e.substring(a)),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}},Z8=class extends Xt{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(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Z8(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),oa(n,t)}},Y8=class extends Xt{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=Et([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=xa([s,r,o,i],[1,4])}else this.cropBox=xa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().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 Y8(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=li.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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Q8{constructor(e,t){super();this.upstream=e,this.impl=new gie(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},gie=class extends F2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},bie=class extends Xt{decodeUTF8(){return new xie(this)}},xie=class extends Q8{constructor(e){super();this.upstream=e,this.impl=new vie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},vie=class extends F2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=cI();this.decoder=new 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Eie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Cie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Rie=[33,133,362,263,1,78,308],hoe=Eie.map(e=>qd[e]),foe=Cie.map(e=>qd[e]),moe=Rie.map(e=>qd[e]);var _2=Xa.leftEyeLower0,P2=Xa.rightEyeLower0,Yl={leftBounds:[_2[0],_2[_2.length-1]],rightBounds:[P2[0],P2[P2.length-1]]},y0={count:468,mouth:13,symmetryLine:[13,Xa.midwayBetweenEyes[0]]},hk={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Jl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function g0(e,t,n,a){for(let r=0;r<z2.length;r++){let{key:s,indices:i}=z2[r],o=Xa[`${n}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let d=i[l];e[o[l]]=[t[d][0],t[d][1],(t[d][2]+e[o[l]][2])/2]}}}var L2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=Gd({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?A0(a,[0,0]):m0,l=a!==0?i.map(p=>[...lk(p,o),p[2]]):i,d=a!==0?ok(r):m0,u=[...Kl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(p=>[Math.round(p[0]+Zr(u,d[0])),Math.round(p[1]+Zr(u,d[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[Yl.leftBounds[0]][2],a=t[Yl.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=f0(h0(D2([t[a],t[r]]),this.irisEnlarge)),o=Gd(i),l=Ye.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&Aa.flags.IS_BROWSER&&(l=Ye.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<Jl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],d=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],l/this.irisSize*a[1]+n.startPoint[1],d])}return{rawCoords:s,iris:s.slice(Jl.index)}}getAdjustedIrisCoords(t,n,a){let r=t[Xa[`${a}EyeUpper0`][Jl.upperCenter]][2],s=t[Xa[`${a}EyeLower0`][Jl.lowerCenter]][2],i=(r+s)/2;return n.map((o,l)=>{let d=i;return l===2?d=r:l===4&&(d=s),[o[0],o[1],d]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=ak({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),l=h0(o),d=f0(l),u=this.storedBoxes[i].landmarks.arraySync(),p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...d,confidence:p,landmarks:u}}}r&&r.boxes&&r.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=W(()=>this.storedBoxes.map((i,o)=>{let l,d=0,u;if(n.face.detector.rotation&&n.face.mesh.enabled&&Aa.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=y0.count?y0.symmetryLine:hk.symmetryLine;d=O2(i.landmarks[w],i.landmarks[b]);let v=Kl({startPoint:i.startPoint,endPoint:i.endPoint}),N=[v[0]/t.shape[2],v[1]/t.shape[1]],T=Ye.rotateWithOffset(t,d,0,N);u=A0(-d,v),n.face.mesh.enabled?l=Zl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.meshSize,this.meshSize]).div(255):l=Zl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.boxSize,this.boxSize]).div(255)}else{u=m0;let w=t.clone();n.face.mesh.enabled?l=Zl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):l=Zl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l};let[,p,c]=this.meshDetector.execute(l),h=p.dataSync()[0];if(h<n.face.detector.minConfidence)return this.storedBoxes[o].confidence=h,null;let f=H(c,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:b,crop:v}=this.getEyeBox(f,l,Yl.leftBounds[0],Yl.leftBounds[1],!0),{box:N,boxSize:T,crop:R}=this.getEyeBox(f,l,Yl.rightBounds[0],Yl.rightBounds[1]),z=this.irisModel.predict(ot([v,R])).dataSync(),P=z.slice(0,Jl.numCoordinates*3),{rawCoords:V,iris:j}=this.getEyeCoords(P,w,b,!0),U=z.slice(Jl.numCoordinates*3),{rawCoords:X,iris:G}=this.getEyeCoords(U,N,T),ee=this.getLeftToRightEyeDepthDifference(f);Math.abs(ee)<30?(g0(f,V,"left",null),g0(f,X,"right",null)):ee<1?g0(f,V,"left",["EyeUpper0","EyeLower0"]):g0(f,X,"right",["EyeUpper0","EyeLower0"]);let Y=this.getAdjustedIrisCoords(f,j,"left"),re=this.getAdjustedIrisCoords(f,G,"right");f=f.concat(Y).concat(re)}let A=this.transformRawCoords(f,i,d,u),y=i.confidence;if(i=h0(D2(A),1.5),i.confidence=y,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&Aa.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=y0.count?y0.symmetryLine:hk.symmetryLine;d=O2(i.landmarks[w],i.landmarks[b]);let v=Kl({startPoint:i.startPoint,endPoint:i.endPoint}),N=[v[0]/t.shape[2],v[1]/t.shape[1]],T=Ye.rotateWithOffset(t.toFloat(),d,0,N);u=A0(-d,v),l=Zl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.meshSize,this.meshSize]).div(255)}let g={mesh:A,box:i,faceConfidence:h,boxConfidence:i.confidence,image:l},x=f0(i);return x.confidence=i.confidence,x.faceConfidence=h,this.storedBoxes[o]=x,g}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Bt=[null,null,null],B2;async function V2(e,t){let n=await B2.predict(e,t),a=[];for(let r of n||[]){if(!r||r.isDisposedInternal)continue;let s=r.mesh.map(d=>[d[0]/e.shape[2],d[1]/e.shape[1],d[2]/B2.meshSize]),i={};if(r.mesh&&r.mesh.length>0)for(let d of Object.keys(Xa))i[d]=Xa[d].map(u=>r.mesh[u]);let o=r.box?[Math.max(0,r.box.startPoint[0]),Math.max(0,r.box.startPoint[1]),Math.min(e.shape[2],r.box.endPoint[0])-Math.max(0,r.box.startPoint[0]),Math.min(e.shape[1],r.box.endPoint[1])-Math.max(0,r.box.startPoint[1])]:0,l=r.box?[r.box.startPoint[0]/e.shape[2],r.box.startPoint[1]/e.shape[1],(r.box.endPoint[0]-r.box.startPoint[0])/e.shape[2],(r.box.endPoint[1]-r.box.startPoint[1])/e.shape[1]]:[];a.push({confidence:Math.round(100*r.faceConfidence||100*r.boxConfidence||0)/100,boxConfidence:Math.round(100*r.boxConfidence)/100,faceConfidence:Math.round(100*r.faceConfidence)/100,box:o,boxRaw:l,mesh:r.mesh,meshRaw:s,annotations:i,image:r.image}),r.coords&&r.coords.dispose()}return a}async function j2(e){return!Bt[0]&&e.face.enabled||!Bt[1]&&e.face.mesh.enabled||!Bt[2]&&e.face.iris.enabled?(Bt=await Promise.all([!Bt[0]&&e.face.enabled?ck(e):null,!Bt[1]&&e.face.mesh.enabled?qt(Jt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Bt[2]&&e.face.iris.enabled?qt(Jt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Bt[1]||!Bt[1].modelUrl?he("load model failed:",e.face.mesh.modelPath):e.debug&&he("load model:",Bt[1].modelUrl)),e.face.iris.enabled&&(!Bt[2]||!Bt[1].modelUrl?he("load model failed:",e.face.iris.modelPath):e.debug&&he("load model:",Bt[2].modelUrl))):e.debug&&(he("cached model:",Bt[0].model.modelUrl),he("cached model:",Bt[1].modelUrl),he("cached model:",Bt[2].modelUrl)),B2=new L2(Bt[0],Bt[1],Bt[2]),Bt}var fk=zi,mk=qd;var U2={};ma(U2,{load:()=>q2,predict:()=>b0});var Mie=["angry","disgust","fear","happy","sad","surprise","neutral"],Ra,H2=[],x0=Number.MAX_SAFE_INTEGER,G2=[.2989,.587,.114];async function q2(e){return Ra?e.debug&&he("cached model:",Ra.modelUrl):(Ra=await qt(Jt(e.modelBasePath,e.face.emotion.modelPath)),!Ra||!Ra.modelUrl?he("load model failed:",e.face.emotion.modelPath):e.debug&&he("load model:",Ra.modelUrl)),Ra}async function b0(e,t){return Ra?x0<t.face.emotion.skipFrames&&t.videoOptimized&&H2.length>0?(x0++,H2):(t.videoOptimized?x0=0:x0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let a=Ye.resizeBilinear(e,[Ra.inputs[0].shape[2],Ra.inputs[0].shape[1]],!1),[r,s,i]=rn(a,3,3);a.dispose();let o=_(r,G2[0]),l=_(s,G2[1]),d=_(i,G2[2]);r.dispose(),s.dispose(),i.dispose();let u=yc([o,l,d]);o.dispose(),l.dispose(),d.dispose();let p=W(()=>u.sub(.5).mul(2));u.dispose();let c=[];if(t.face.emotion.enabled){let h=await Ra.predict(p),m=h.dataSync();Ee(h);for(let f=0;f<m.length;f++)m[f]>t.face.emotion.minConfidence&&c.push({score:Math.min(.99,Math.trunc(100*m[f])/100),emotion:Mie[f]});c.sort((f,A)=>A.score-f.score)}p.dispose(),H2=c,n(c)})):null}var X2={};ma(X2,{enhance:()=>Y2,load:()=>K2,match:()=>Ak,predict:()=>k0,similarity:()=>Z2});var ea,v0={age:0},w0=Number.MAX_SAFE_INTEGER;async function K2(e){return ea?e.debug&&he("cached model:",ea.modelUrl):(ea=await qt(Jt(e.modelBasePath,e.face.description.modelPath)),!ea||!ea.modelUrl?he("load model failed:",e.face.description.modelPath):e.debug&&he("load model:",ea.modelUrl)),ea}function Z2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function Ak(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=Z2(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function Y2(e){return W(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Le))return null;let a=[[.05,.15,.85,.85]];return(n.shape.length===3?Ye.cropAndResize(un(n,0),a,[0],[ea.inputs[0].shape[2],ea.inputs[0].shape[1]]):Ye.cropAndResize(n,a,[0],[ea.inputs[0].shape[2],ea.inputs[0].shape[1]])).mul(255)})}async function k0(e,t){return ea?w0<t.face.description.skipFrames&&t.videoOptimized&&v0.age&&v0.age>0?(w0++,v0):(t.videoOptimized?w0=0:w0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let a=Y2(e),r,s={age:0,gender:"unknown",genderConfidence:0,descriptor:[]};t.face.description.enabled&&(r=await ea.predict(a)),Ee(a),r&&(W(()=>{let i=r.find(p=>p.shape[1]===1).dataSync(),o=Math.trunc(200*Math.abs(i[0]-.5))/100;o>t.face.description.minConfidence&&(s.gender=i[0]<=.5?"female":"male",s.genderConfidence=Math.min(.99,o));let l=r.find(p=>p.shape[1]===100).argMax(1).dataSync()[0],d=r.find(p=>p.shape[1]===100).dataSync();s.age=Math.round(d[l-1]>d[l+1]?10*l-100*d[l-1]:10*l+100*d[l+1])/10;let u=r.find(p=>p.shape[1]===1024);s.descriptor=[...u.dataSync()]}),r.forEach(i=>Ee(i))),v0=s,n(s)})):null}var Fie=(e,t)=>{let n=A=>A*180/Math.PI,a=A=>{let y=Math.sqrt(A[0]*A[0]+A[1]*A[1]+A[2]*A[2]);return A[0]/=y,A[1]/=y,A[2]/=y,A},r=(A,y)=>{let g=A[0]-y[0],x=A[1]-y[1],w=A[2]-y[2];return[g,x,w]},s=(A,y)=>{let g=A[1]*y[2]-A[2]*y[1],x=A[2]*y[0]-A[0]*y[2],w=A[0]*y[1]-A[1]*y[0];return[g,x,w]},i=A=>{let[y,g,x,w,b,v,N,T,R]=A,$,z,P;return w<1?w>-1?(P=Math.asin(w),z=Math.atan2(-N,y),$=Math.atan2(-v,b)):(P=-Math.PI/2,z=-Math.atan2(T,R),$=0):(P=Math.PI/2,z=Math.atan2(T,R),$=0),{pitch:2*-$,yaw:2*-z,roll:2*-P}},o=A=>{let y=(x,w,b,v)=>Math.atan2(v-w,b-x);return{pitch:y(A[10][1],A[10][2],A[152][1],A[152][2]),yaw:y(A[33][0],A[33][2],A[263][0],A[263][2]),roll:y(A[33][0],A[33][1],A[263][0],A[263][1])}},l=e.meshRaw;if(!l||l.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1]};let d=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,u=[l[10],l[152],l[234],l[454]].map(A=>[A[0]*t[0]/d,A[1]*t[1]/d,A[2]]),p=a(r(u[1],u[0])),c=a(r(u[3],u[2])),h=a(s(c,p));c=s(p,h);let m=[c[0],c[1],c[2],p[0],p[1],p[2],h[0],h[1],h[2]];return{angle:i(m),matrix:m}},J2=async(e,t)=>{var u,p,c,h,m,f;let n,a,r,s,i,o,l=[];e.state="run:face",n=it();let d=await V2(t,e.config);if(e.perf.face=Math.trunc(it()-n),!d)return[];for(let A of d){if(e.analyze("Get Face"),!A.image||A.image.isDisposedInternal){he("Face object is disposed:",A.image);continue}let y=Fie(A,[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?b0(A.image,e.config):{}:(e.state="run:emotion",n=it(),s=e.config.face.emotion.enabled?await b0(A.image,e.config):{},e.perf.emotion=Math.trunc(it()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?k0(A,e.config):[]:(e.state="run:description",n=it(),o=e.config.face.description.enabled?await k0(A.image,e.config):[],e.perf.embedding=Math.trunc(it()-n)),e.analyze("End Description:"),e.config.async&&([a,r,s,i,o]=await Promise.all([a,r,s,i,o])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((u=A==null?void 0:A.annotations)==null?void 0:u.leftEyeIris)&&((p=A==null?void 0:A.annotations)==null?void 0:p.rightEyeIris)&&(delete A.annotations.leftEyeIris,delete A.annotations.rightEyeIris);let g=((c=A.annotations)==null?void 0:c.leftEyeIris)&&((h=A.annotations)==null?void 0:h.rightEyeIris)?11.7*Math.max(Math.abs(A.annotations.leftEyeIris[3][0]-A.annotations.leftEyeIris[1][0]),Math.abs(A.annotations.rightEyeIris[4][1]-A.annotations.rightEyeIris[2][1])):0;l.push({...A,age:o.age,gender:o.gender,genderConfidence:o.genderConfidence,embedding:o.descriptor,emotion:s,iris:g!==0?Math.trunc(g)/100:0,rotation:y,tensor:e.config.face.detector.return?(m=A.image)==null?void 0:m.squeeze():null}),(f=A.image)==null||f.dispose(),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.perf.face&&delete e.perf.face,e.perf.age&&delete e.perf.age,e.perf.gender&&delete e.perf.gender,e.perf.emotion&&delete e.perf.emotion),l};var rg={};ma(rg,{load:()=>ig,predict:()=>sg});var Xd=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],yk=Xd.length,Kd=Xd.reduce((e,t,n)=>(e[t]=n,e),{}),$ie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Die=$ie.map(([e,t])=>[Kd[e],Kd[t]]),gk=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function xk(e){let t=e.reduce(({maxX:n,maxY:a,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(a,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function bk(e,[t,n],[a,r]){let s=(o,l,d)=>({score:o.score,box:[Math.trunc(o.box[0]*d),Math.trunc(o.box[1]*l),Math.trunc(o.box[2]*d),Math.trunc(o.box[3]*l)],keypoints:o.keypoints.map(({score:u,part:p,position:c})=>({score:u,part:p,position:{x:Math.trunc(c.x*d),y:Math.trunc(c.y*l)}}))});return e.map(o=>s(o,t/a,n/r))}var Q2=class{constructor(t,n){this.priorityQueue=new 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 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Ye.nonMaxSuppressionAsync(l,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),u=d.arraySync();s.dispose(),d.dispose();let p=[];for(let c of u)if(i[c]>=n.hand.minConfidence){let h=Re(l,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=W(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),l.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=W(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let d=l.box.dataSync(),u=d.slice(0,2),p=d.slice(2,4),c=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(Tk({startPoint:u,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function Bie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Ck(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Bie(n)}var Rk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Yr(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function Vie(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function Mk(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(Yr(e[r],Vie(t,s)))}return n}function lg(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=Rk(t[0],t[1]),i=Mk(s,r),o=Rk(-t[0],-t[1]);return Mk(i,o)}function Fk(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-Yr(t[0],n),-Yr(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function ug(e,t){return[Yr(e,t[0]),Yr(e,t[1])]}var jie=5,$k=1.65,Dk=[0,5,9,13,17,1,2],Uie=0,Hie=2,dg=class{constructor(t,n){var a;this.handDetector=t,this.landmarkDetector=n,this.inputSize=(a=this.landmarkDetector)==null?void 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b=this.transformRawCoords(w,h,l,c),v=this.getBoxForHandLandmarks(b);this.storedBoxes[i]=v;let N={landmarks:b,confidence:g,box:{topLeft:v.startPoint,bottomRight:v.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=N0(T0(o),$k),d={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(d)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}};var Ok={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},Jr,Qr,zk;async function cg(e,t){let n=await zk.estimateHands(e,t);if(!n)return[];let a=[];for(let r of n){let s={};if(r.landmarks)for(let l of Object.keys(Ok))s[l]=Ok[l].map(d=>r.landmarks[d]);let 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yg={};ma(yg,{load:()=>xg,predict:()=>bg});var E0=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball 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f.bindTexture(f.TEXTURE_2D,R),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,b,v,0,f.RGBA,f.UNSIGNED_BYTE,null),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.LINEAR),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.framebufferTexture2D(f.FRAMEBUFFER,f.COLOR_ATTACHMENT0,f.TEXTURE_2D,R,0),f.bindTexture(f.TEXTURE_2D,null),f.bindFramebuffer(f.FRAMEBUFFER,null),{fbo:N,texture:R}},g=function(b){return s[b]=s[b]||y(o,l),s[b]},x=function(b=null){var R,$;let v=null,N=null,T=!1;t===0?v=n:v=(R=g(r))==null?void 0:R.texture,t++,a&&!(b&m.INTERMEDIATE)?(N=null,T=t%2==0):(r=(r+1)%2,N=($=g(r))==null?void 0:$.fbo),f.bindTexture(f.TEXTURE_2D,v),f.bindFramebuffer(f.FRAMEBUFFER,N),f.uniform1f(u.uniform.flipY,T?-1:1),f.drawArrays(f.TRIANGLES,0,6)};this.apply=function(b){if(A(b.width,b.height),t=0,n||(n=f.createTexture()),f.bindTexture(f.TEXTURE_2D,n),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.NEAREST),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.NEAREST),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,f.RGBA,f.UNSIGNED_BYTE,b),i.length===0)return x(),c;for(let v=0;v<i.length;v++){a=v===i.length-1;let N=i[v];N.func.apply(this,N.args||[])}return c};let w=function(b){if(h[b])return u=h[b],f.useProgram(u.id),u;let v={};v.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
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|
`),v.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
|
|
`),u=new qie(f,v.VERTEX_IDENTITY,b);let N=Float32Array.BYTES_PER_ELEMENT,T=4*N;return f.enableVertexAttribArray(u.attribute.pos),f.vertexAttribPointer(u.attribute.pos,2,f.FLOAT,!1,T,0*N),f.enableVertexAttribArray(u.attribute.uv),f.vertexAttribPointer(u.attribute.uv,2,f.FLOAT,!1,T,2*N),h[b]=u,u};p.colorMatrix=function(b){let v=new Float32Array(b);v[4]/=255,v[9]/=255,v[14]/=255,v[19]/=255;let N=v[18]===1&&v[3]===0&&v[8]===0&&v[13]===0&&v[15]===0&&v[16]===0&&v[17]===0&&v[19]===0?p.colorMatrix.SHADER.WITHOUT_ALPHA:p.colorMatrix.SHADER.WITH_ALPHA,T=w(N);f.uniform1fv(T.uniform.m,v),x()},p.colorMatrix.SHADER={},p.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
|
|
`),p.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
|
|
`),p.brightness=function(b){let v=(b||0)+1;p.colorMatrix([v,0,0,0,0,0,v,0,0,0,0,0,v,0,0,0,0,0,1,0])},p.saturation=function(b){let v=(b||0)*2/3+1,N=(v-1)*-.5;p.colorMatrix([v,N,N,0,0,N,v,N,0,0,N,N,v,0,0,0,0,0,1,0])},p.desaturate=function(){p.saturation(-1)},p.contrast=function(b){let v=(b||0)+1,N=-128*(v-1);p.colorMatrix([v,0,0,0,N,0,v,0,0,N,0,0,v,0,N,0,0,0,1,0])},p.negative=function(){p.contrast(-2)},p.hue=function(b){b=(b||0)/180*Math.PI;let v=Math.cos(b),N=Math.sin(b),T=.213,R=.715,$=.072;p.colorMatrix([T+v*(1-T)+N*-T,R+v*-R+N*-R,$+v*-$+N*(1-$),0,0,T+v*-T+N*.143,R+v*(1-R)+N*.14,$+v*-$+N*-.283,0,0,T+v*-T+N*-(1-T),R+v*-R+N*R,$+v*(1-$)+N*$,0,0,0,0,0,1,0])},p.desaturateLuminance=function(){p.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},p.sepia=function(){p.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},p.brownie=function(){p.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},p.vintagePinhole=function(){p.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},p.kodachrome=function(){p.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])},p.technicolor=function(){p.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])},p.polaroid=function(){p.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},p.shiftToBGR=function(){p.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},p.convolution=function(b){let v=new Float32Array(b),N=1/o,T=1/l,R=w(p.convolution.SHADER);f.uniform1fv(R.uniform.m,v),f.uniform2f(R.uniform.px,N,T),x()},p.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(`
|
|
`),p.detectEdges=function(){p.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},p.sobelX=function(){p.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},p.sobelY=function(){p.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},p.sharpen=function(b){let v=b||1;p.convolution.call(this,[0,-1*v,0,-1*v,1+4*v,-1*v,0,-1*v,0])},p.emboss=function(b){let v=b||1;p.convolution.call(this,[-2*v,-1*v,0,-1*v,1,1*v,0,1*v,2*v])},p.blur=function(b){let v=b/7/o,N=b/7/l,T=w(p.blur.SHADER);f.uniform2f(T.uniform.px,0,N),x(m.INTERMEDIATE),f.uniform2f(T.uniform.px,v,0),x()},p.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(`
|
|
`),p.pixelate=function(b){let v=b/o,N=b/l,T=w(p.pixelate.SHADER);f.uniform2f(T.uniform.size,v,N),x()},p.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(`
|
|
`)}var M0=2048,Ce,gt,$t;function wg(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof Le)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(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 Le)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Oa(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=r,o=s;if(i>M0&&(i=M0,o=i*s/r),o>M0&&(o=M0,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ce||(Ce==null?void 0:Ce.width)!==i||(Ce==null?void 0:Ce.height)!==o)&&(Ce=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ce==null?void 0:Ce.width)!==i&&(Ce.width=i),(Ce==null?void 0:Ce.height)!==o&&(Ce.height=o));let l=Ce.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):t.filter.flip&&typeof l.translate!="undefined"?(l.translate(r,0),l.scale(-1,1),l.drawImage(e,0,0,r,s,0,0,Ce==null?void 0:Ce.width,Ce==null?void 0:Ce.height),l.setTransform(1,0,0,1,0,0)):l.drawImage(e,0,0,r,s,0,0,Ce==null?void 0:Ce.width,Ce==null?void 0:Ce.height),t.filter.enabled){if((!$t||!gt||Ce.width!==gt.width||(Ce==null?void 0:Ce.height)!==(gt==null?void 0:gt.height))&&(gt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ce==null?void 0:Ce.width,Ce==null?void 0:Ce.height):document.createElement("canvas"),(gt==null?void 0:gt.width)!==(Ce==null?void 0:Ce.width)&&(gt.width=Ce==null?void 0:Ce.width),(gt==null?void 0:gt.height)!==(Ce==null?void 0:Ce.height)&&(gt.height=Ce==null?void 0:Ce.height),$t=Aa.flags.IS_BROWSER?new jk({canvas:gt}):null),!$t)return{tensor:null,canvas:Ce};$t.reset(),$t.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&$t.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&$t.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&$t.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&$t.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&$t.addFilter("hue",t.filter.hue),t.filter.negative&&$t.addFilter("negative"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.vintage&&$t.addFilter("brownie"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.kodachrome&&$t.addFilter("kodachrome"),t.filter.technicolor&&$t.addFilter("technicolor"),t.filter.polaroid&&$t.addFilter("polaroid"),t.filter.pixelate!==0&&$t.addFilter("pixelate",t.filter.pixelate),$t.apply(Ce)}else gt=Ce,$t&&($t=null);let d;if(gt.data){let p=[gt.height,gt.width,3];d=hc(gt.data,p,"int32")}else if(gt instanceof ImageData)d=li.fromPixels(gt);else if(t.backend==="webgl"||t.backend==="humangl"){let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(gt,0,0),d=li.fromPixels(p)}else{let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(gt,0,0);let h=c==null?void 0:c.getImageData(0,0,i,o);d=li.fromPixels(h)}let u=d.toFloat();n=u.expandDims(0),d.dispose(),u.dispose()}let a=t.filter.return?gt:null;return{tensor:n,canvas:a}}var kg={};ma(kg,{all:()=>Kie,body:()=>Gk,canvas:()=>Xie,face:()=>Hk,gesture:()=>Uk,hand:()=>qk,object:()=>Xk,options:()=>_i});var dt={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,videoOptimized:!0,warmup:"full",filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31,minConfidence:.1},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetected:1,minConfidence:.2},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,maxDetected:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:41}};var _i={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!1,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1,calculateHandBox:!0};function F0(e,t,n,a=0,r){e.fillStyle=r.useDepth&&a?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Pi(e,t,n,a,r,s){if(e.beginPath(),s.useCurves){let i=(t+t+a)/2,o=(n+n+r)/2;e.ellipse(i,o,a/2,r/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+a-s.roundRect,n),e.quadraticCurveTo(t+a,n,t+a,n+s.roundRect),e.lineTo(t+a,n+r-s.roundRect),e.quadraticCurveTo(t+a,n+r,t+a-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function Ig(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t)e.strokeStyle=n.useDepth&&a[2]?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&a[2]?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.3)`:n.color,e.lineTo(a[0],parseInt(a[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Yd(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Ig(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function Uk(e,t,n){let a=Gn(_i,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let 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s=0;s<t.length;s++){if(!es[s]&&a.bufferedOutput&&(es[s]={...t[s]}),r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&(Pi(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[s].score}%`,t[s].box[0]+3,1+t[s].box[1]+a.lineHeight,t[s].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[s].score}%`,t[s].box[0]+2,0+t[s].box[1]+a.lineHeight,t[s].box[2]))),a.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)r.fillStyle=a.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:a.color,a.bufferedOutput?(es[s].keypoints[i][0]=(es[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,es[s].keypoints[i][1]=(es[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,F0(r,es[s].keypoints[i][0],es[s].keypoints[i][1],0,a)):F0(r,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,a);if(a.drawLabels&&(r.font=a.font,t[s].keypoints))for(let i of t[s].keypoints)r.fillStyle=a.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:a.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position.x+4,i.position.y+4);if(a.drawPolygons&&t[s].keypoints){let i,o=[];o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),Yd(r,o,a),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),o.length===4&&Ig(r,o,a),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftKnee"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftAnkle"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHeel"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftFoot"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),Yd(r,o,a),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightKnee"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightAnkle"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHeel"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightFoot"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),Yd(r,o,a),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftElbow"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftWrist"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftPalm"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),Yd(r,o,a),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightElbow"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightWrist"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightPalm"),i&&i.score>dt.body.minConfidence&&o.push([i.position.x,i.position.y]),Yd(r,o,a)}}}}async function qk(e,t,n){let a=Gn(_i,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes){r.strokeStyle=a.color,r.fillStyle=a.color;let i;if(!a.calculateHandBox)i=a.useRawBoxes?s.boxRaw:s.box;else if(i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],s.landmarks&&s.landmarks.length>0){for(let o of s.landmarks)o[0]<i[0]&&(i[0]=o[0]),o[1]<i[1]&&(i[1]=o[1]),o[0]>i[2]&&(i[2]=o[0]),o[1]>i[3]&&(i[3]=o[1]);i[2]-=i[0],i[3]-=i[1]}a.useRawBoxes?Pi(r,e.width*i[0],e.height*i[1],e.width*i[2],e.height*i[3],a):Pi(r,i[0],i[1],i[2],i[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",i[0]+3,1+i[1]+a.lineHeight,i[2])),r.fillStyle=a.labelColor,r.fillText("hand",i[0]+2,0+i[1]+a.lineHeight,i[2])),r.stroke()}if(a.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 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a});this.tf=pu,this.draw=kg,this.version=Kk,this.config=Gn(dt,t),this.state="idle",rs(this,Ql,0),rs(this,Jd,!1),rs(this,Qd,!1),rs(this,Li,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>wg(n,this.config),this.classes={facemesh:W2,emotion:U2,faceres:X2,body:this.config.body.modelPath.includes("posenet")?rg:fg,hand:pg,nanodet:yg},this.faceTriangulation=fk,this.faceUVMap=mk,this.sysinfo=Pg()}similarity(t,n){return Z2(t,n)}enhance(t){return Y2(t)}match(t,n,a=0){return Ak(t,n,a)}async load(t={}){this.state="load";let n=it();t&&(this.config=Gn(this.config,t)),aa(this,Li)&&(this.config.debug&&he(`version: ${this.version}`),this.config.debug&&he(`tfjs version: ${this.tf.version_core}`),this.config.debug&&he("platform:",this.sysinfo.platform),this.config.debug&&he("agent:",this.sysinfo.agent),await aa(this,ep).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&he("configuration:",this.config),this.config.debug&&he("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?j2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?q2(this.config):null),this.models.handpose||(this.config.hand.enabled?hg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?ig(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?mg(this.config):null),this.models.nanodet||(this.config.object.enabled?xg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?K2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await j2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await q2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await hg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await ig(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await mg(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await xg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await K2(this.config))),aa(this,Li)&&(this.config.debug&&he("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),rs(this,Li,!1));let a=Math.trunc(it()-n);a>(this.perf.load||0)&&(this.perf.load=a)}async detect(t,n={}){return new Promise(async a=>{this.state="config";let r;this.config=Gn(this.config,n),this.state="check";let s=aa(this,O0).call(this,t);s&&(he(s,t),a({error:s}));let i=it();await aa(this,ep).call(this),await this.load();let o;t&&this.config.videoOptimized&&typeof window!="undefined"&&typeof WorkerGlobalScope!="undefined"&&(typeof HTMLImageElement!="undefined"&&t instanceof HTMLImageElement||typeof Image!="undefined"&&t instanceof Image||typeof ImageData!="undefined"&&t instanceof ImageData||typeof ImageBitmap!="undefined"&&vg instanceof ImageBitmap)&&(he("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),r=it();let l=wg(t,this.config);if(!l||!l.tensor){he("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(it()-r),this.analyze("Get Image:");let d,u,p,c,h;this.config.async?(p=this.config.face.enabled?J2(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",r=it(),p=this.config.face.enabled?await J2(this,l.tensor):[],h=Math.trunc(it()-r),h>0&&(this.perf.face=h)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?sg(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")&&(d=this.config.body.enabled?Ag(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",r=it(),this.config.body.modelPath.includes("posenet")?d=this.config.body.enabled?await sg(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")&&(d=this.config.body.enabled?await Ag(l.tensor,this.config):[]),h=Math.trunc(it()-r),h>0&&(this.perf.body=h)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?cg(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",r=it(),u=this.config.hand.enabled?await cg(l.tensor,this.config):[],h=Math.trunc(it()-r),h>0&&(this.perf.hand=h)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(c=this.config.object.enabled?bg(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",r=it(),c=this.config.object.enabled?await bg(l.tensor,this.config):[],h=Math.trunc(it()-r),h>0&&(this.perf.object=h)),this.analyze("End Object:"),this.config.async&&([p,d,u,c]=await Promise.all([p,d,u,c])),Ee(l.tensor);let m=[];this.config.gesture.enabled&&(r=it(),m=[...Wk(p),...Lk(d),...Vk(u),...Bk(p)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(it()-r)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(it()-i),this.state="idle";let f={face:p,body:d,hand:u,gesture:m,object:c,performance:this.perf,canvas:l.canvas};a(f)})}async warmup(t={}){let n=it();if(t&&(this.config=Gn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await aa(this,z0).call(this):typeof Image!="undefined"?r=await aa(this,_0).call(this):r=await aa(this,P0).call(this),this.config.videoOptimized=a;let s=it();return this.config.debug&&he("Warmup",this.config.warmup,Math.round(s-n),"ms",r),r}};Ql=new WeakMap,Jd=new WeakMap,Qd=new WeakMap,Li=new WeakMap,O0=new WeakMap,ep=new WeakMap,z0=new WeakMap,_0=new WeakMap,P0=new WeakMap;return Yie;})();
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/**
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* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
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|
* Licensed under the Apache License, Version 2.0 (the "License");
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|
* 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 See the LICENSE file. */
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//# sourceMappingURL=human.js.map
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