mirror of https://github.com/vladmandic/human
5038 lines
1.3 MiB
5038 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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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,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 r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Yu.nextTensorId++}nextVariableId(){return Yu.nextVariableId++}clone(e){let t=D.runKernel(ps,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return D.runKernel(es,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(Xh(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 r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Mm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Mm(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=Xh(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let b=g.map(x=>{if(x.rank!=null)return x;let{dataId:w,shape:_,dtype:N}=x;return this.makeTensorFromDataId(w,_,N)});if(r){let x=this.getTensorsForGradient(p,f,b);n=this.saveTensorsForBackwardMode(x)}return b}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=Mm(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=Ef(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&Ia(e[0])&&(a=e.map(o=>Xu(o)));let s=r.write(a,t,n),i=new et(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=J0(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new et(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new _u(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*wm(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 _u||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*wm(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(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=Ef(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((c,u)=>{if(c==null){let h=n[u],d=zd(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return c}),r(l.length>1?l:l[0],a,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=Tm(e),n=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!n.has(s.id)&&s.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!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 a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof et,()=>"The result y returned by f() must be a tensor.");let s=c9(this.state.activeTape,t,a);if(!r&&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[a.id]=n==null?_9(a.shape):n,h9(i,s,l=>this.tidy(l),b9);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return F(Na(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof et),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),F(n.value instanceof et,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(Na(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),c=Array.isArray(l)?l:[l];F(c.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(c.every(h=>h instanceof et),()=>"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 c.forEach((h,d)=>{u[d]=()=>h}),u};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:r})}}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=qu(),n=await this.backend.time(e);return n.wallMs=qu()-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 o5;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}};Yu.nextTensorId=0;Yu.nextVariableId=0;function _9(e){let t=_m(Ot(e),"float32");return D.makeTensor(t,e,"float32")}function l5(){let e=n5();if(e._tfengine==null){let t=new L2(e);e._tfengine=new Yu(t)}return n9(e._tfengine.ENV),m9(()=>e._tfengine),e._tfengine}var D=l5();function b9(e,t){let n={a:e,b:t};return D.runKernel(ma,n)}var Zh={};ze(Zh,{isBrowser:()=>u5,isMobile:()=>v9});function k9(){return typeof navigator!="undefined"&&navigator!=null}function v9(){if(k9()){let e=navigator.userAgent||navigator.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(e)||/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(e.substr(0,4))}return!1}function u5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Or=Q();Or.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. 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Actual: ${a}.
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Actual: ${a}.
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c,u,h;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let d=iN([t,n],s,r,e,o);u=d[0],h=d[1]}else if(e==="same"){u=Math.ceil(t/r),h=Math.ceil(n/a);let d=Math.max(0,(u-1)*r+s-t),p=Math.max(0,(h-1)*a+i-n),f=Math.floor(d/2),m=d-f,A=Math.floor(p/2),y=p-A;c={top:f,bottom:m,left:A,right:y,type:"SAME"}}else if(e==="valid")c={top:0,bottom:0,left:0,right:0,type:"VALID"},u=Math.ceil((t-s+1)/r),h=Math.ceil((n-i+1)/a);else if(typeof e=="object"){let d=l==="channelsLast"?e[1][0]:e[2][0],p=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];c={top:d,bottom:p,left:f,right:m,type:d===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},u=ni((t-s+d+p)/r+1,o),h=ni((n-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:h}}function sN(e,t,n,r,a,s,i,o,l,c,u){let h,d,p,f;if(typeof e=="number"){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let m=oN([t,n,r,1],o,1,a,e,u);d=m[0],p=m[1],f=m[2]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/s),f=Math.ceil(r/i);let m=(d-1)*a+o-t,A=(p-1)*s+l-n,y=(f-1)*i+c-r,g=Math.floor(m/2),b=m-g,x=Math.floor(A/2),w=A-x,_=Math.floor(y/2),N=y-_;h={top:x,bottom:w,left:_,right:N,front:g,back:b,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),p=Math.ceil((n-l+1)/s),f=Math.ceil((r-c+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:d,outHeight:p,outWidth:f}}function ni(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 Ca(e){let[t,n,r]=Hd(e);return t===1&&n===1&&r===1}function Pr(e,t){return Ca(e)||Ca(t)}function V5(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function lN(e,t){let n={x:R(e,"x","reshape","string_or_numeric")},r={shape:t};return D.runKernel(Io,n,r)}var q=O({reshape_:lN});function uN(e,t,n,r,a){let s=R(e,"x","avgPool","float32"),i=1;F(Pr(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. 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|
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with dtype ${s.dtype}. `)}),n.length===1)return Sr(n[0]);let r=n,a={axis:t};return D.runKernel(Xi,r,a)}var ct=O({concat_:hN});function dN(e){let t={x:R(e,"x","sigmoid")};return D.runKernel($s,t)}var Qn=O({sigmoid_:dN});function pN(e,t,n){let r=R(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return D.runKernel(Eo,a,s)}var Me=O({slice_:pN});function fN(e){let t={x:R(e,"x","tanh")};return D.runKernel(Ws,t)}var Uo=O({tanh_:fN});function mN(e,t,n,r,a,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(n,"lstmBias","basicLSTMCell"),c=R(r,"data","basicLSTMCell"),u=R(a,"c","basicLSTMCell"),h=R(s,"h","basicLSTMCell"),d=ct([c,h],1),p=qe(d,o),f=oe(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Me(f,[0,0],y),b=Me(f,[0,A],y),x=Me(f,[0,A*2],y),w=Me(f,[0,A*3],y),_=oe(L(Qn(g),Uo(b)),L(u,Qn(oe(i,x)))),N=L(Uo(_),Qn(w));return[_,N]}var u8=O({basicLSTMCell_:mN});function AN(e,t,n){let r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.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(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return D.runKernel(su,s,i)}var Su=O({batchToSpaceND_:AN});function yN(e){let t;return e.rank===0||e.rank===1?t=q(e,[1,1,1,e.size]):e.rank===2?t=q(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function gN(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;r!=null&&(u=R(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${c.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}.`),js(i,o,l,u,c,s)}var K2=O({batchNorm2d_:xN});function wN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"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}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.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}.`),js(i,o,l,u,c,s)}var Z2=O({batchNorm3d_:wN});function _N(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"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}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.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}.`),js(i,o,l,u,c,s)}var J2=O({batchNorm4d_:_N});function bN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return D.runKernel(_h,s,i)}var Y2=O({bincount_:bN});function vN(e,t){let n=R(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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=q(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return Sr(n);let i={x:n},o={reps:s};return D.runKernel(ya,i,o)}var Tu=O({broadcastTo_:vN});function kN(e){let t={x:R(e,"x","ceil")};return D.runKernel(ts,t)}var Hf=O({ceil_:kN});function IN(e,t,n){let r=R(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return D.runKernel(Aa,a,s)}var fn=O({clipByValue_:IN});function NN(e){return ct(e,0)}var Q2=O({concat1d_:NN});function SN(e,t){return ct(e,t)}var td=O({concat2d_:SN});function TN(e,t){return ct(e,t)}var e0=O({concat3d_:TN});function EN(e,t){return ct(e,t)}var t0=O({concat4d_:EN});function CN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","conv2d"),l=R(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(Ht(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];F(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),F(Pr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:c,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=D.runKernel(ns,d,p);return u?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Zr=O({conv2d_:CN});function RN(e,t,n,r,a="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1]])),F(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(Ht(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(Pr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=q(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=q(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=Zr(d,h,[1,n],r,"NHWC",[1,s],i);return u?q(p,[p.shape[2],p.shape[3]]):q(p,[p.shape[0],p.shape[2],p.shape[3]])}var nd=O({conv1d_:RN});function FN(e,t,n,r,a,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=q(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 ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),F(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(Ht(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=D.runKernel(rs,d,p);return c?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Ym=O({conv2DBackpropInput_:FN});function MN(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return Ym(n,i,o,r,a,"NHWC",s)}var rd=O({conv2dTranspose_:MN});function $N(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=q(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(Pr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=D.runKernel(ou,u,h);return c?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var jf=O({conv3d_:$N});function DN(e,t,n,r,a){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=q(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],c=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 ${n.shape[3]}.`),F(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=D.runKernel(Ih,u,h);return o?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var j5=O({conv3DBackpropInput_:DN});function ON(e,t,n,r,a){let s=R(e,"x","conv3dTranspose"),i=R(t,"filter","conv3dTranspose");return j5(n,s,i,r,a)}var c8=O({conv3dTranspose_:ON});function zN(e){let t={x:R(e,"x","cos")};return D.runKernel(as,t)}var Eu=O({cos_:zN});function PN(e){let t={x:R(e,"x","cosh")};return D.runKernel(Ki,t)}var ad=O({cosh_:PN});function LN(e,t=0,n=!1,r=!1){let a={x:R(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return D.runKernel(ss,a,s)}var sd=O({cumsum_:LN});function WN(e,t,n,r=!1){let a=R(e,"x","denseBincount"),s=R(t,"weights","denseBincount");F(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),F(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return D.runKernel(Nh,i,o)}var n0=O({denseBincount_:WN});function BN(e,t,n="NHWC"){let r=R(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${a} and ${t} for depthToSpace with input shape
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${r.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${s} and ${t} for depthToSpace with input shape
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${r.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);let o={x:r},l={blockSize:t,dataFormat:n};return D.runKernel(Ji,o,l)}var Gf=O({depthToSpace_:BN});function VN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","depthwiseConv2d"),l=R(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(Ht(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h={x:c,filter:l},d={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},p=D.runKernel(is,h,d);return u?q(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Ho=O({depthwiseConv2d_:VN});function UN(e){let t={x:R(e,"x","diag")};return D.runKernel(Eh,t)}var h8=O({diag_:UN});function HN(e,t,n,r,a=[1,1],s="NHWC"){let i=R(e,"x","dilation2d"),o=R(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,c=!1;i.rank===3&&(l=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=D.runKernel(lu,u,h);return c?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var qf=O({dilation2d_:HN});function jN(e,t){let n=e.length,r=[];for(let a=0;a<n;a++){let s=n-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&i===1&&r.unshift(s)}return r}function zt(e,t){let n=[];for(let r=0;r<t.length;r++){let a=e[e.length-r-1],s=t.length-r-1,i=t[s];(a==null||a===1&&i>1)&&n.unshift(s)}return n}function At(e,t){let n=[],r=Math.max(e.length,t.length);for(let a=0;a<r;a++){let s=e[e.length-a-1];s==null&&(s=1);let i=t[t.length-a-1];if(i==null&&(i=1),s===1)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}function GN(e,t){let n=R(e,"a","equal"),r=R(t,"b","equal");[n,r]=vt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(eo,a)}var wa=O({equal_:GN});function qN(e,t,n){let r=R(t,"a","where"),a=R(n,"b","where"),s=R(e,"condition","where","bool"),i=At(r.shape,a.shape),o=Tu(r,i),l=Tu(a,i);s.rank===1&&F(s.shape[0]===r.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&tn(s.shape,l.shape,"Error in where: ");let c={condition:s,t:o,e:l};return D.runKernel(So,c)}var mn=O({where_:qN});function XN(e){let t={x:R(e,"x","zerosLike")};return D.runKernel(Po,t)}var He=O({zerosLike_:XN});function KN(e,t){let n=R(e,"a","div"),r=R(t,"b","div");[n,r]=vt(n,r);let a=Ne(n,r),s=He(a),i=wa(r,s);return mn(i,s,a)}var Xf=O({divNoNan_:KN});function ZN(e,t){let n=R(e,"t1","dot"),r=R(t,"t2","dot");F((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let a=n.rank===1?n.size:n.shape[1],s=r.rank===1?r.size:r.shape[0];if(F(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=q(n,[1,-1]),o=q(r,[-1,1]),l=qe(i,o);return q(l,[])}else if(n.rank===1&&r.rank===2){let i=q(n,[1,-1]),o=q(r,[r.shape[0],r.shape[1]]),l=qe(i,o);return q(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=q(r,[-1,1]),o=qe(n,i);return q(o,[o.size])}else{let i=q(r,[r.shape[0],r.shape[1]]);return qe(n,i)}}var r0=O({dot_:ZN});function JN(e){let t={x:R(e,"x","elu")};return D.runKernel(Yi,t)}var jo=O({elu_:JN});function YN(e){let t=R(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=ye(t,"float32"));let n={x:t};return D.runKernel(Qi,n)}var Kf=O({erf_:YN});function QN(e){let t={x:R(e,"x","exp")};return D.runKernel(ls,t)}var Ln=O({exp_:QN});function eS(e,t=0){let n=R(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return D.runKernel(to,r,a)}var kn=O({expandDims_:eS});function tS(e){let t={x:R(e,"x","expm1")};return D.runKernel(no,t)}var Zf=O({expm1_:tS});function nS(e,t){let n=R(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return D.runKernel(ya,r,a)}var _a=O({tile_:nS});function rS(e,t,n,r="float32"){t==null&&(t=e);let a=We([e,t],r),s=e<=t?e:t;for(let o=0;o<s;++o)a.set(1,o,o);let i=q(a.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return _a(kn(i,0),[n[0],1,1]);if(n.length===2)return _a(kn(kn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return _a(kn(kn(kn(i,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var Jf=O({eye_:rS});function Cu(e,t,n){let r={shape:e,value:t,dtype:n};return D.runKernel(uu,{},r)}function aS(e){let t={x:R(e,"x","floor")};return D.runKernel(us,t)}var Go=O({floor_:aS});function sS(e,t,n=0,r=0){let a=R(e,"x","gather"),s=R(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return D.runKernel(ao,i,o)}var Gs=O({gather_:sS});function iS(e,t){let n=R(e,"a","greater"),r=R(t,"b","greater");[n,r]=vt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(io,a)}var er=O({greater_:iS});function oS(e,t){let n=R(e,"a","greaterEqual"),r=R(t,"b","greaterEqual");[n,r]=vt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(ds,a)}var ba=O({greaterEqual_:oS});function lS(e){let t={input:R(e,"input","imag")};return D.runKernel(Dh,t)}var id=O({imag_:lS});function uS(e){let t={x:R(e,"x","isFinite")};return D.runKernel(oo,t)}var a0=O({isFinite_:uS});function cS(e){let t={x:R(e,"x","isInf")};return D.runKernel(lo,t)}var s0=O({isInf_:cS});function hS(e){let t={x:R(e,"x","isNaN")};return D.runKernel(uo,t)}var i0=O({isNaN_:hS});function dS(e,t=.2){let n={x:R(e,"x","leakyRelu")},r={alpha:t};return D.runKernel(fs,n,r)}var Ru=O({leakyRelu_:dS});function pS(e,t){let n=R(e,"a","less"),r=R(t,"b","less");[n,r]=vt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(co,a)}var od=O({less_:pS});function fS(e,t){let n=R(e,"a","lessEqual"),r=R(t,"b","lessEqual");[n,r]=vt(n,r),At(n.shape,r.shape);let a={a:n,b:r};return D.runKernel(ho,a)}var qs=O({lessEqual_:fS});function o0(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return D.runKernel(Oh,{},r)}function mS(e,t=5,n=1,r=1,a=.5){let s=R(e,"x","localResponseNormalization");F(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
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rank ${s.rank}.`),F(Ht(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=q(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},c={depthRadius:t,bias:n,alpha:r,beta:a},u=D.runKernel(du,l,c);return o?q(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Yf=O({localResponseNormalization_:mS});function AS(e){let t={x:R(e,"x","log")};return D.runKernel(ms,t)}var In=O({log_:AS});function yS(e){let t={x:R(e,"x","log1p")};return D.runKernel(po,t)}var ld=O({log1p_:yS});function d8(e){return F(Na(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=R(t,"x","tf.grad","string_or_numeric"),a=n!=null?R(n,"dy","tf.grad"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(r),[r],a);return a!=null&&tn(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),jd(i),i[0]})}}function p8(e){return F(Na(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 r=Qu(t,"args","tf.grads","string_or_numeric"),a=n!=null?R(n,"dy","tf.grads"):null;return D.tidy(()=>{let{value:s,grads:i}=D.gradients(()=>e(...r),r,a);return a!=null&&tn(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),jd(i),i})}}function f8(e){return F(Na(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof et,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof et,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=D.gradients(()=>e(t),[t],n);return jd(r),{grad:r[0],value:a}}}function m8(e){return F(Na(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(a=>a instanceof et),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof et,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=D.gradients(()=>e(...t),t,n);return n!=null&&tn(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),jd(r.grads),r}}function l0(e,t){F(Na(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(c=>c instanceof _u),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in D.registeredVariables)t.push(D.registeredVariables[c])}let r=n?t.filter(c=>!c.trainable):null,a=t.length;t=t.filter(c=>c.trainable),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=D.gradients(e,t,null,s);F(o.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),r!=null&&r.forEach(c=>l[c.name]=null),{value:i,grads:l}}function Er(e){return D.customGrad(e)}function jd(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 gS(e){let t={x:R(e,"x","neg")};return D.runKernel(Ao,t)}var _t=O({neg_:gS});function xS(e){let t={x:R(e,"x","softplus")};return D.runKernel(Fo,t)}var qo=O({softplus_:xS});function wS(e){let t=R(e,"x","logSigmoid");return Er(n=>({value:_t(qo(_t(n))),gradFunc:r=>L(r,Qn(_t(n)))}))(t)}var u0=O({logSigmoid_:wS});function _S(e,t=null,n=!1){let r={x:R(e,"x","max")},a={reductionIndices:t,keepDims:n};return D.runKernel(As,r,a)}var Wn=O({max_:_S});function bS(e,t){let n=R(e,"a","sub"),r=R(t,"b","sub");[n,r]=vt(n,r);let a={a:n,b:r};return D.runKernel(Ls,a)}var we=O({sub_:bS});function vS(e,t=null,n=!1){let r=R(e,"x","sum");r.dtype==="bool"&&(r=ye(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return D.runKernel(Os,a,s)}var Te=O({sum_:vS});function kS(e,t=-1){let n=R(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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xC=O({softmaxCrossEntropy_:gC}),$8={fft:Lu,ifft:Jo,rfft:Wu,irfft:_d},D8={hammingWindow:gE,hannWindow:Q5,frame:ex,stft:bE},St={flipLeftRight:NE,resizeNearestNeighbor:ix,resizeBilinear:sx,rotateWithOffset:TE,cropAndResize:kE,nonMaxSuppression:CE,nonMaxSuppressionAsync:PE,nonMaxSuppressionWithScore:WE,nonMaxSuppressionWithScoreAsync:VE,nonMaxSuppressionPadded:HE,nonMaxSuppressionPaddedAsync:GE},_0={bandPart:ZE,gramSchmidt:YE,qr:eC},O8={absoluteDifference:rC,computeWeightedLoss:ra,cosineDistance:sC,hingeLoss:oC,huberLoss:uC,logLoss:hC,meanSquaredError:pC,sigmoidCrossEntropy:AC,softmaxCrossEntropy:xC},Qr=class extends L5{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Fe(a),t?r:(r.dispose(),null)}get iterations(){return 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Qr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=D.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:U(()=>Cu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;U(()=>{let i=oe(s,ot(a));s.assign(i);let o=oe(L(Ne(a,Kt(oe(i,D.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Fe(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 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m=oe(L(Ne(p,oe(Kt(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Fe(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Fe(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),U(()=>{this.accBeta1.assign(Yr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Yr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.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)}};Td.className="Adam";Ea(Td);var Ed=class extends Qr{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],U(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),r==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);U(()=>{let n=we(1,this.accBeta1),r=Ne(-this.learningRate,oe(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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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)}};Ed.className="Adamax";Ea(Ed);var Bu=class extends Qr{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 r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=D.registeredVariables[t];U(()=>{let s=oe(L(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Vt(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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c=oe(L(i,this.decay),L(ot(s),1-this.decay)),u=oe(L(o,this.momentum),Ne(L(s,this.learningRate),Kt(oe(c,this.epsilon))));i.assign(c),o.assign(u);let h=we(r,u);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Fe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Fe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Fe(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function LC(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function WC(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function BC(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function VC(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function UC(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function HC(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function jC(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}function GC(e,t,n=0){let r=[];if(typeof t=="number")F(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var cx={};ze(cx,{collectGatherOpShapeInfo:()=>JC,computeOutShape:()=>ZC,segOpComputeOptimalWindowSize:()=>KC});function KC(e,t){let n=!1,r;for(e<=sA?(r=e,n=!0):r=Od(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=Od(e,r+1);return r}function ZC(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function JC(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${a}, ${a}], but got ${r}`);if(r<0&&(r+=a),r>s)throw new Error(`batchDims (${r}) must be less than rank(x) (
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${s}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let h=0;h<r;++h)if(e.shape[h]!==t.shape[h])throw new Error(`x.shape[${h}]: ${e.shape[h]} should be equal to indices.shape[${h}]: ${t.shape[h]}.`);let i=e.shape[n],o=[],l=1,c=1,u=1;for(let h=0;h<r;++h)o.push(e.shape[h]),l*=e.shape[h];for(let h=r;h<n;h++)o.push(e.shape[h]),c*=e.shape[h];for(let h=r;h<a;h++)o.push(t.shape[h]);for(let h=n+1;h<s;h++)o.push(e.shape[h]),u*=e.shape[h];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:i,outputShape:o}}function qC(e){try{return e.map(t=>Ld(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function XC(e){return e.map(t=>Xu(t))}var Mr={};ze(Mr,{nonMaxSuppressionV3Impl:()=>tx,nonMaxSuppressionV4Impl:()=>nx,nonMaxSuppressionV5Impl:()=>rx,whereImpl:()=>K5});function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var YC=Mr.whereImpl,Md=class extends nu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ah(this,Tr())}nextDataId(){return Md.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Q().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 r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let a=n.map(s=>k.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,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,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}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 r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return C.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return Tr().makeTensorFromDataId(r,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. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ve([e],"where");let t=this.readSync(e.dataId);return YC(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Md.nextDataId=0;var mm={};ze(mm,{addImpl:()=>dx,bincountImpl:()=>iA,bincountReduceImpl:()=>px,ceilImpl:()=>fx,concatImpl:()=>oA,expImpl:()=>mx,expm1Impl:()=>Ax,floorImpl:()=>yx,gatherV2Impl:()=>gx,greaterImpl:()=>xx,lessImpl:()=>wx,linSpaceImpl:()=>_x,logImpl:()=>bx,maxImpl:()=>vx,maximumImpl:()=>kx,minimumImpl:()=>Ix,multiplyImpl:()=>lA,negImpl:()=>Nx,notEqualImpl:()=>Sx,prodImpl:()=>Tx,rangeImpl:()=>cA,rsqrtImpl:()=>Ex,simpleAbsImpl:()=>hx,sliceImpl:()=>Zd,squaredDifferenceImpl:()=>Cx,stridedSliceImpl:()=>Rx,subImpl:()=>Fx,tileImpl:()=>Mx,topKImpl:()=>$x,transposeImpl:()=>uA,uniqueImpl:()=>Dx});function hx(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var QC=e=>{let{x:t}=e.inputs,n=e.backend;ve(t,"abs");let r=new 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l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",s),imag:n.makeTensorInfo(a.shape,"float32",i)},o}var tR={kernelName:bh,backendName:"cpu",kernelFunc:En};function Jd(e,t,n="float32"){if(n==="complex64"){let a=Jd(e,t,"float32"),s=Jd(e,t,"float32");return En({inputs:{real:a,imag:s},backend:e})}let r=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Lr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var nR={kernelName:ps,backendName:"cpu",kernelFunc:Lr};function si(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var rR={kernelName:Vh,backendName:"cpu",kernelFunc:si};function Ra(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Lr({inputs:{x:a},backend:n});let i=Jd(n,a.shape,a.dtype),o=Ra({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=En({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=si({inputs:{input:a},backend:n}),o=Ra({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(a.dtype,s)){let i=Lr({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(a.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=n.data.get(a.dataId).values,o=k.toTypedArray([0],a.dtype),[l,c]=Rt((u,h)=>u!==h?1:0)(a.shape,[],i,o,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var aR={kernelName:es,backendName:"cpu",kernelFunc:Ra};function jt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;ve([i,o],e);let 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UR={kernelName:Ns,backendName:"cpu",kernelFunc:Lx},Wx=st(Ss,e=>Math.max(0,e)),HR={kernelName:Ss,backendName:"cpu",kernelFunc:Wx},Bx=st(Es,e=>Math.min(Math.max(0,e),6)),jR={kernelName:Es,backendName:"cpu",kernelFunc:Bx};function fA(e,t,n,r,a){if(n==="linear")return Lr({inputs:{x:t},backend:e});if(n==="relu")return Wx({inputs:{x:t},backend:e});if(n==="elu")return zx({inputs:{x:t},backend:e});if(n==="relu6")return Bx({inputs:{x:t},backend:e});if(n==="prelu")return Lx({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return Px({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function yt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=k.sizeFromShape(a.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. 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a=r.map(o=>n.data.get(o.dataId).values),s=We(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var tF={kernelName:Za,backendName:"cpu",kernelFunc:eF};function nF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"all");let o=k.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=sr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=k.sizeFromShape(d),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,b=m[g];for(let x=0;x<p;++x){let w=m[g+x];b=b&&w}f[y]=b}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var rF={kernelName:yh,backendName:"cpu",kernelFunc:nF};function aF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"any");let o=k.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=sr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=k.sizeFromShape(d),f=k.makeZerosTypedArray(k.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,b=m[g];for(let x=0;x<p;++x){let w=m[g+x];b=b||w}f[y]=b}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var sF={kernelName:gh,backendName:"cpu",kernelFunc:aF};function iF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ve(a,"argMax");let i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=sr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),p=k.makeZerosTypedArray(d,"int32"),f=k.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],b=0;for(let x=0;x<f;++x){let w=m[y+x];w>g&&(g=w,b=x)}p[A]=b}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var oF={kernelName:Ja,backendName:"cpu",kernelFunc:iF};function lF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ve(a,"argMin");let i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=sr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),p=k.makeZerosTypedArray(d,"int32"),f=k.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],b=0;for(let x=0;x<f;++x){let w=m[y+x];w<g&&(g=w,b=x)}p[A]=b}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var uF={kernelName:ru,backendName:"cpu",kernelFunc:lF},cF=st(Ui,e=>Math.asin(e)),hF={kernelName:Ui,backendName:"cpu",kernelFunc:cF},dF=st(Hi,e=>Math.asinh(e)),pF={kernelName:Hi,backendName:"cpu",kernelFunc:dF},fF=st(ji,e=>Math.atan(e)),mF={kernelName:ji,backendName:"cpu",kernelFunc:fF},AF=Rt((e,t)=>Math.atan2(e,t)),yF=jt(qi,AF),gF={kernelName:qi,backendName:"cpu",kernelFunc:yF},xF=st(Gi,e=>Math.atanh(e)),wF={kernelName:Gi,backendName:"cpu",kernelFunc:xF};function mA(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,c=a.dilationWidth,u=a.effectiveFilterHeight,h=a.effectiveFilterWidth,d=a.padInfo.top,p=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=We(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],b=a.outShape[3];for(let x=0;x<a.batchSize;++x){let w=x*y,_=x*r[0];for(let N=0;N<a.inChannels;++N)for(let T=0;T<a.outHeight;++T){let 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V=G-N,K=m.get(A,P,G,y);K>M&&(M=K,a?z=s?((A*r.inHeight+P)*r.inWidth+G)*r.inChannels+y:(P*r.inWidth+G)*r.inChannels+y:z=B*d+V)}}i.set(z,A,g,_,y)}}return i}function Hx(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,b=We(a.outShape,n),x=b.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],_=a.outShape[2]*a.outShape[3]*a.outShape[4],N=a.outShape[3]*a.outShape[4],T=a.outShape[4];for(let E=0;E<a.batchSize;++E){let M=E*w,z=E*r[0];for(let P=0;P<a.inChannels;++P)for(let B=0;B<a.outDepth;++B){let G=B*i-m,V=G;for(;V<0;)V+=c;let K=Math.min(a.inDepth,d+G),X=M+B*_;for(let ee=0;ee<a.outHeight;++ee){let J=ee*o-A,ae=J;for(;ae<0;)ae+=u;let Y=Math.min(a.inHeight,p+J),ue=X+ee*N;for(let ne=0;ne<a.outWidth;++ne){let de=ne*l-y,he=de;for(;he<0;)he+=h;let me=Math.min(a.inWidth,f+de),Ae=ue+ne*T,ke=g,Ee=0,Ce=0;for(let Ke=V;Ke<K;Ke+=c){let Ue=z+Ke*r[1];for(let rt=ae;rt<Y;rt+=u){let it=Ue+rt*r[2];for(let je=he;je<me;je+=h){let lt=it+je*r[3],ut=e[lt+P];if(s==="max"&&ut>ke?ke=ut:s==="avg"&&(Ee+=ut,Ce++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Oe=Ae+P;x[Oe]=s==="avg"?Ee/Ce:ke}}}}return b}function _F(e,t){let n=We(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,b=g;for(;b<0;)b+=i;let x=Math.min(t.inDepth,c+g);for(let w=0;w<t.outHeight;++w){let _=w*a-p,N=_;for(;N<0;)N+=o;let T=Math.min(t.inHeight,u+_);for(let E=0;E<t.outWidth;++E){let M=E*s-f,z=M;for(;z<0;)z+=l;let P=Math.min(t.inWidth,h+M),B=Number.NEGATIVE_INFINITY,G=-1;for(let V=b;V<x;V+=i){let K=V-g;for(let X=N;X<T;X+=o){let ee=X-_;for(let J=z;J<P;J+=l){let ae=J-M,Y=e.get(m,V,X,J,A);Y>=B&&(B=Y,G=K*u*h+ee*u+ae)}}}n.set(G,m,y,w,E,A)}}}return n}function bF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ve(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=k.computeStrides(a.shape),f=mA(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var vF={kernelName:Ya,backendName:"cpu",kernelFunc:bF};function kF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;ve(a,"avgPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=Hx(h,a.shape,a.dtype,k.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var IF={kernelName:au,backendName:"cpu",kernelFunc:kF};function NF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;ve([a,s],"avgPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,b=u.dilationWidth,x=u.effectiveFilterDepth,w=u.effectiveFilterHeight,_=u.effectiveFilterWidth,N=x-1-u.padInfo.front,T=_-1-u.padInfo.left,E=w-1-u.padInfo.top,M=We(s.shape,"float32"),z=1/(f*m*A),P=n.bufferSync(a);for(let B=0;B<u.batchSize;++B)for(let G=0;G<u.inChannels;++G)for(let V=0;V<u.inDepth;++V)for(let K=0;K<u.inHeight;++K)for(let X=0;X<u.inWidth;++X){let ee=V-N,J=K-E,ae=X-T,Y=0;for(let ue=0;ue<x;ue+=y){let ne=(ee+ue)/h;if(!(ne<0||ne>=u.outDepth||Math.floor(ne)!==ne))for(let de=0;de<w;de+=g){let he=(J+de)/d;if(!(he<0||he>=u.outHeight||Math.floor(he)!==he))for(let me=0;me<_;me+=b){let Ae=(ae+me)/p;Ae<0||Ae>=u.outWidth||Math.floor(Ae)!==Ae||(Y+=P.get(B,ne,he,Ae,G))}}}M.set(Y*z,B,V,K,X,G)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var SF={kernelName:wh,backendName:"cpu",kernelFunc:NF};function TF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ve([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,b=g-1-u.padInfo.left,x=y-1-u.padInfo.top,w=We(i.shape,"float32"),_=1/(p*f),N=n.data.get(a.dataId).values,T=We(a.shape,"float32",N);for(let E=0;E<u.batchSize;++E)for(let M=0;M<u.inChannels;++M)for(let z=0;z<u.inHeight;++z)for(let P=0;P<u.inWidth;++P){let B=z-x,G=P-b,V=0;for(let K=0;K<y;K+=m){let X=(B+K)/h;if(!(X<0||X>=u.outHeight||Math.floor(X)!==X))for(let ee=0;ee<g;ee+=A){let J=(G+ee)/d;J<0||J>=u.outWidth||Math.floor(J)!==J||(V+=T.get(E,X,J,M))}}w.set(V*_,E,z,P,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var EF={kernelName:xh,backendName:"cpu",kernelFunc:TF};function CF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,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([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),A=f.length,y=p.length,g=d.length,b=h.length,x=0,w=0,_=0,N=0;for(let T=0;T<u.length;++T)m[T]=f[x++]+(u[T]-h[w++])*p[_++]/Math.sqrt(d[N++]+c),x>=A&&(x=0),w>=b&&(w=0),_>=y&&(_=0),N>=g&&(N=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var RF={kernelName:hs,backendName:"cpu",kernelFunc:CF};function FF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=yt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=sr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=ii({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var MF={kernelName:su,backendName:"cpu",kernelFunc:FF};function $F(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=iA(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var DF={kernelName:_h,backendName:"cpu",kernelFunc:$F},OF=st(Aa,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),zF={kernelName:Aa,backendName:"cpu",kernelFunc:OF},PF=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(k.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},LF={kernelName:iu,backendName:"cpu",kernelFunc:PF};function pl(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var WF={kernelName:Dh,backendName:"cpu",kernelFunc:pl};function fl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>k.sizeFromShape(m.shape)>0);if(o.length===1)return Lr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(x=>si({inputs:{input:x},backend:n})),A=o.map(x=>pl({inputs:{input:x},backend:n})),y=fl({inputs:m,backend:n,attrs:{axis:s}}),g=fl({inputs:A,backend:n,attrs:{axis:s}}),b=En({inputs:{real:y,imag:g},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),A.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),b}let c=o.map(m=>{let A=k.sizeFromShape(m.shape.slice(s));return yt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=oA(u,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var BF={kernelName:Xi,backendName:"cpu",kernelFunc:fl};function jx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r;ve([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,b=d.dataFormat==="channelsLast",x=new $t(d.outShape,a.dtype),w=k.computeStrides(a.shape),_=k.computeStrides(s.shape),N=w[0],T=b?w[1]:w[2],E=b?w[2]:1,M=b?1:w[1],z=x.strides[0],P=b?x.strides[1]:x.strides[2],B=b?x.strides[2]:1,G=b?1:x.strides[1],V=n.data.get(a.dataId).values,K=n.data.get(s.dataId).values,X=x.values;for(let ee=0;ee<d.batchSize;++ee){let J=ee*N,ae=ee*z;for(let Y=0;Y<d.outHeight;++Y){let ue=ae+Y*P,ne=Y*d.strideHeight-g;for(let de=0;de<p;++de){let he=ne+de*m;if(he<0||he>=d.inHeight)continue;let me=de*_[0],Ae=J+he*T;for(let ke=0;ke<d.outWidth;++ke){let Ee=ue+ke*B,Ce=ke*d.strideWidth-y;for(let Oe=0;Oe<f;++Oe){let Ke=Ce+Oe*A;if(Ke<0||Ke>=d.inWidth)continue;let Ue=me+Oe*_[1],rt=Ae+Ke*E,it=Ue;for(let je=0;je<d.inChannels;++je){let lt=V[rt+je*M];for(let ut=0;ut<d.outChannels;++ut)X[Ee+ut*G]+=lt*K[it+ut];it+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,X)}var VF={kernelName:ns,backendName:"cpu",kernelFunc:jx};function UF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r;ve([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new $t(d.filterShape,"float32"),b=d.padInfo.left,x=d.padInfo.top,w=n.data.get(a.dataId).values,_=n.data.get(s.dataId).values,N=new $t(a.shape,a.dtype,w),T=new $t(s.shape,s.dtype,_);for(let E=0;E<m;++E){let M=Math.max(0,Math.ceil((x-E)/p)),z=Math.min(d.outHeight,(d.inHeight+x-E)/p);for(let P=0;P<A;++P){let B=Math.max(0,Math.ceil((b-P)/f)),G=Math.min(d.outWidth,(d.inWidth+b-P)/f);for(let V=0;V<d.inChannels;++V)for(let K=0;K<d.outChannels;++K){let X=0;for(let ee=0;ee<d.batchSize;++ee)for(let J=M;J<z;++J){let ae=E+J*p-x;for(let Y=B;Y<G;++Y){let ue=P+Y*f-b;y?X+=N.get(ee,ae,ue,V)*T.get(ee,J,Y,K):X+=N.get(ee,V,ae,ue)*T.get(ee,K,J,Y)}}g.set(X,E,P,V,K)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var HF={kernelName:vh,backendName:"cpu",kernelFunc:UF};function jF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;ve([a,s],"conv2dBackpropInput");let h=k.computeStrides(s.shape),d=k.computeStrides(a.shape),p=C.convertConv2DDataFormat(c),f=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new $t(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[b,x,w]=h,{batchSize:_,filterHeight:N,filterWidth:T,inChannels:E,inHeight:M,inWidth:z,outChannels:P,outHeight:B,outWidth:G,strideHeight:V,strideWidth:K}=f;p=f.dataFormat;let X=N-1-f.padInfo.top,ee=T-1-f.padInfo.left,J=p==="channelsLast",ae=m.strides[0],Y=J?m.strides[1]:m.strides[2],ue=J?m.strides[2]:1,ne=J?1:m.strides[1],de=d[0],he=J?d[1]:d[2],me=J?d[2]:1,Ae=J?1:d[1];for(let ke=0;ke<_;++ke)for(let Ee=0;Ee<E;++Ee)for(let Ce=0;Ce<M;++Ce){let Oe=Ce-X,Ke=Math.max(0,Math.ceil(Oe/V)),Ue=Math.min(B,(N+Oe)/V);for(let rt=0;rt<z;++rt){let it=rt-ee,je=Math.max(0,Math.ceil(it/K)),lt=Math.min(G,(T+it)/K),ut=0;for(let Ye=Ke;Ye<Ue;++Ye){let _n=Ye*V-Oe;for(let Xt=je;Xt<lt;++Xt){let bn=Xt*K-it,Gn=de*ke+he*Ye+me*Xt,dn=b*(N-1-_n)+x*(T-1-bn)+w*Ee;for(let rn=0;rn<P;++rn){let qn=y[Gn+Ae*rn],kr=g[dn+rn];ut+=qn*kr}}}let On=ae*ke+Y*Ce+ue*rt+ne*Ee;A[On]=ut}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var GF={kernelName:rs,backendName:"cpu",kernelFunc:jF};function qF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;ve([a,s],"conv3d");let c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=c,y=A.front,g=A.left,b=A.top,x=new $t(c.outShape,a.dtype),w=n.data.get(a.dataId).values,_=n.data.get(s.dataId).values,N=x.values,T=k.computeStrides(a.shape),E=k.computeStrides(s.shape);for(let M=0;M<c.batchSize;++M){let z=M*T[0],P=M*x.strides[0];for(let B=0;B<c.outDepth;++B){let G=P+B*x.strides[1],V=B*c.strideDepth-y;for(let K=0;K<u;++K){let X=V+K*p;if(X<0||X>=c.inDepth)continue;let ee=K*E[0],J=z+X*T[1];for(let ae=0;ae<c.outHeight;++ae){let Y=G+ae*x.strides[2],ue=ae*c.strideHeight-b;for(let ne=0;ne<h;++ne){let de=ue+ne*f;if(de<0||de>=c.inHeight)continue;let he=ee+ne*E[1],me=J+de*T[2];for(let Ae=0;Ae<c.outWidth;++Ae){let ke=Y+Ae*c.outChannels,Ee=Ae*c.strideWidth-g;for(let Ce=0;Ce<d;++Ce){let Oe=Ee+Ce*m;if(Oe<0||Oe>=c.inWidth)continue;let Ke=he+Ce*E[2],Ue=me+Oe*c.inChannels,rt=Ke;for(let it=0;it<c.inChannels;++it){let je=w[Ue+it];for(let lt=0;lt<c.outChannels;++lt)N[ke+lt]+=je*_[rt+lt];rt+=c.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var XF={kernelName:ou,backendName:"cpu",kernelFunc:qF};function KF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;ve([a,s],"conv3dBackpropFilterV2");let c=k.computeStrides(a.shape),u=k.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new $t(h.filterShape,"float32"),b=g.values,[x,w,_,N]=g.strides,T=n.data.get(s.dataId).values,[E,M,z,P]=u,B=n.data.get(a.dataId).values,[G,V,K,X]=c,ee=h.padInfo.front,J=h.padInfo.left,ae=h.padInfo.top;for(let Y=0;Y<m;++Y){let ue=Math.max(0,Math.ceil((ee-Y)/d)),ne=Math.min(h.outDepth,(h.inDepth+ee-Y)/d),de=Y*x;for(let he=0;he<A;++he){let me=Math.max(0,Math.ceil((ae-he)/p)),Ae=Math.min(h.outHeight,(h.inHeight+ae-he)/p),ke=he*w+de;for(let Ee=0;Ee<y;++Ee){let Ce=Math.max(0,Math.ceil((J-Ee)/f)),Oe=Math.min(h.outWidth,(h.inWidth+J-Ee)/f),Ke=Ee*_+ke;for(let Ue=0;Ue<h.inChannels;++Ue){let rt=Ue*N+Ke;for(let it=0;it<h.outChannels;++it){let je=0;for(let lt=0;lt<h.batchSize;++lt){let ut=lt*G,On=lt*E;for(let Ye=ue;Ye<ne;++Ye){let _n=(Y+Ye*d-ee)*V+ut,Xt=Ye*M+On;for(let bn=me;bn<Ae;++bn){let Gn=(he+bn*p-ae)*K+_n,dn=bn*z+Xt;for(let rn=Ce;rn<Oe;++rn){let qn=(Ee+rn*f-J)*X+Gn,kr=rn*P+dn;je+=B[qn+Ue]*T[kr+it]}}}}b[rt+it]=je}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var ZF={kernelName:kh,backendName:"cpu",kernelFunc:KF};function JF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;ve([a],"conv3dBackpropInputV2");let c=k.computeStrides(a.shape),u=k.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),d=new $t(h.inShape,"float32"),p=d.values,[f,m,A,y]=d.strides,g=n.data.get(a.dataId).values,[b,x,w,_]=c,N=n.data.get(s.dataId).values,[T,E,M,z]=u,{batchSize:P,filterDepth:B,filterHeight:G,filterWidth:V,inChannels:K,inDepth:X,inHeight:ee,inWidth:J,outChannels:ae,outDepth:Y,outHeight:ue,outWidth:ne,strideDepth:de,strideHeight:he,strideWidth:me}=h,Ae=B-1-h.padInfo.front,ke=G-1-h.padInfo.top,Ee=V-1-h.padInfo.left;for(let Ce=0;Ce<P;++Ce)for(let Oe=0;Oe<K;++Oe)for(let Ke=0;Ke<X;++Ke){let Ue=Ke-Ae,rt=Math.max(0,Math.ceil(Ue/de)),it=Math.min(Y,(B+Ue)/de);for(let je=0;je<ee;++je){let lt=je-ke,ut=Math.max(0,Math.ceil(lt/he)),On=Math.min(ue,(G+lt)/he);for(let Ye=0;Ye<J;++Ye){let _n=Ye-Ee,Xt=Math.max(0,Math.ceil(_n/me)),bn=Math.min(ne,(V+_n)/me),Gn=0;for(let dn=rt;dn<it;++dn){let rn=dn*de-Ue;for(let qn=ut;qn<On;++qn){let kr=qn*he-lt;for(let vn=Xt;vn<bn;++vn){let Si=vn*me-_n,Pl=b*Ce+x*dn+w*qn+_*vn,cr=T*(B-1-rn)+E*(G-1-kr)+M*(V-1-Si)+z*Oe;for(let Xn=0;Xn<ae;++Xn){let hr=g[Pl+Xn],Ti=N[cr+Xn];Gn+=hr*Ti}}}}p[f*Ce+m*Ke+A*je+y*Ye+Oe]=Gn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var YF={kernelName:Ih,backendName:"cpu",kernelFunc:JF},QF=st(as,e=>Math.cos(e)),eM={kernelName:as,backendName:"cpu",kernelFunc:QF},tM=st(Ki,e=>Math.cosh(e)),nM={kernelName:Ki,backendName:"cpu",kernelFunc:tM};function rM(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=We([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,b=n.data.get(i.dataId).values,x=n.data.get(a.dataId).values,w=k.computeStrides(a.shape),_=k.computeStrides(y.shape);for(let N=0;N<f;N++){let T=N*4,E=g[T],M=g[T+1],z=g[T+2],P=g[T+3],B=b[N];if(B>=u)continue;let G=m>1?(z-E)*(h-1)/(m-1):0,V=A>1?(P-M)*(d-1)/(A-1):0;for(let K=0;K<m;K++){let X=m>1?E*(h-1)+K*G:.5*(E+z)*(h-1);if(X<0||X>h-1){for(let ee=0;ee<A;ee++)for(let J=0;J<p;J++){let ae=J+ee*_[2]+K*_[1]+N*_[0];y.values[ae]=c}continue}if(l==="bilinear"){let ee=Math.floor(X),J=Math.ceil(X),ae=X-ee;for(let Y=0;Y<A;Y++){let ue=A>1?M*(d-1)+Y*V:.5*(M+P)*(d-1);if(ue<0||ue>d-1){for(let me=0;me<p;me++){let Ae=me+Y*_[2]+K*_[1]+N*_[0];y.values[Ae]=c}continue}let ne=Math.floor(ue),de=Math.ceil(ue),he=ue-ne;for(let me=0;me<p;me++){let Ae=me+ne*w[2]+ee*w[1]+B*w[0],ke=x[Ae];Ae=me+de*w[2]+ee*w[1]+B*w[0];let Ee=x[Ae];Ae=me+ne*w[2]+J*w[1]+B*w[0];let Ce=x[Ae];Ae=me+de*w[2]+J*w[1]+B*w[0];let Oe=x[Ae],Ke=ke+(Ee-ke)*he,Ue=Ce+(Oe-Ce)*he;Ae=me+Y*_[2]+K*_[1]+N*_[0],y.values[Ae]=Ke+(Ue-Ke)*ae}}}else for(let ee=0;ee<A;++ee){let J=A>1?M*(d-1)+ee*V:.5*(M+P)*(d-1);if(J<0||J>d-1){for(let ue=0;ue<p;ue++){let ne=ue+ee*_[2]+K*_[1]+N*_[0];y.values[ne]=c}continue}let ae=Math.round(J),Y=Math.round(X);for(let ue=0;ue<p;ue++){let ne=ue+ae*w[2]+Y*w[1]+B*w[0],de=ue+ee*_[2]+K*_[1]+N*_[0];y.values[de]=x[ne]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var aM={kernelName:Zi,backendName:"cpu",kernelFunc:rM};function sM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;ve(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=sr({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=C.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=Yn(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(y,g)=>y+f-g-1:(y,g)=>y+g;for(let y=0;y<p.length;y+=f)for(let g=0;g<f;g++){let b=m(y,g);if(g===0)d[b]=i?0:p[b];else{let x=m(y,g-1);d[b]=i?p[x]+d[x]:p[b]+d[x]}}let A=n.makeTensorInfo(c.shape,h,d);if(l!=null){let y=C.getUndoAxesPermutation(l),g=sr({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(c),g}return A}var iM={kernelName:ss,backendName:"cpu",kernelFunc:sM};function oM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=iA(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=px(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var lM={kernelName:Nh,backendName:"cpu",kernelFunc:oM};function uM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,d=c*s,p=u/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),A=0;for(let y=0;y<o;++y)for(let g=0;g<h;++g){let b=Math.floor(g/s),x=g%s;for(let w=0;w<d;++w){let _=Math.floor(w/s),N=w%s,T=(x*s+N)*p;for(let E=0;E<p;++E){let M=E+T+u*(_+c*(b+l*y));m[A++]=f[M]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var cM={kernelName:Ji,backendName:"cpu",kernelFunc:uM};function Gx(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;ve([a,s],"depthwiseConv2DNative");let u=k.computeStrides(a.shape),h=k.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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dM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r;ve([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,A=new $t(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,b=h.outChannels/h.inChannels,x=n.data.get(a.dataId).values,w=new $t(a.shape,a.dtype,x),_=n.data.get(s.dataId).values,N=new $t(s.shape,s.dtype,_);for(let T=0;T<f;++T){let E=Math.max(0,Math.ceil((g-T)/d)),M=Math.min(h.outHeight,(h.inHeight+g-T)/d);for(let z=0;z<m;++z){let P=Math.max(0,Math.ceil((y-z)/p)),B=Math.min(h.outWidth,(h.inWidth+y-z)/p);for(let G=0;G<h.outChannels;++G){let V=Math.trunc(G/b),K=G%b,X=0;for(let ee=0;ee<h.batchSize;++ee)for(let J=E;J<M;++J){let ae=T+J*d-g;for(let Y=P;Y<B;++Y){let ue=z+Y*p-y;X+=w.get(ee,ae,ue,V)*N.get(ee,J,Y,G)}}A.set(X,T,z,V,K)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var 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VM={kernelName:ro,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(r.shape)),[i,o,l,c]=r.shape,u=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*c;for(let p=0;p<o;p++){let f=p*(l*c);for(let m=0;m<l;m++){let A=m*c;for(let y=0;y<c;y++){let g=[i,p,m,y][2],b=Math.round(l-g),x=d+f+A+y,w=u[x];if(b>=0&&b<l){let _=b*c,N=d+f+_+y;w=u[N]}s[x]=w}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},UM=Rt((e,t)=>Math.floor(e/t)),HM=jt(cs,UM,null,"int32"),jM={kernelName:cs,backendName:"cpu",kernelFunc:HM};function GM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=jx({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let 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ui={},vA={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function Am(e,t){ui[e]=t}function Wr(e){if(!(e in ui)){let n=$O(e);if(n!==null)ui[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=ui[e];return t.isContextLost()?(delete ui[e],Wr(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),ui[e])}function DO(e){if(typeof OffscreenCanvas!="undefined"&&e===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function $O(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=DO(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete ui[e]},!1),e===1?t.getContext("webgl",vA)||t.getContext("experimental-webgl",vA):t.getContext("webgl2",vA)}var ic;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(ic||(ic={}));var Un;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Un||(Un={}));var Zt;(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"})(Zt||(Zt={}));function oc(e,t){return[t,e]}function OO(e,t){return e*t}function lc(e){let t=k.sizeFromShape(e),n=Math.ceil(t/4);return k.sizeToSquarishShape(n)}function Al(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function zO(e,t){let[n,r]=Al(e,t);return n*r*4}function kA(e,t){let n=e,r,a,s,i,o,l,c,u,h,d;return Q().getNumber("WEBGL_VERSION")===2?(r=n.R32F,a=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,c=4,u=1,h=n.HALF_FLOAT,d=n.FLOAT):(r=e.RGBA,a=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,c=4,u=4,h=t!=null?t.HALF_FLOAT_OES:null,d=e.FLOAT),l=e.RGBA,{internalFormatFloat:r,internalFormatHalfFloat:a,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:c,defaultNumChannels:u,textureTypeHalfFloat:h,textureTypeFloat:d}}function _e(e,t){let n=t();return Q().getBool("DEBUG")&&PO(e),n}function PO(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+rw(e,t))}var LO=596e-10,WO=65504;function nw(e){return!!(Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||LO<Math.abs(e)&&Math.abs(e)<WO)}function rw(e,t){switch(t){case e.NO_ERROR:return"NO_ERROR";case e.INVALID_ENUM:return"INVALID_ENUM";case e.INVALID_VALUE:return"INVALID_VALUE";case e.INVALID_OPERATION:return"INVALID_OPERATION";case 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 rc(e,t){return aa(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function aw(e,t){let n=aa(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(_e(e,()=>e.shaderSource(n,t)),_e(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 sw(e,t){let n=aa(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(_e(e,()=>e.shaderSource(n,t)),_e(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw BO(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var VO=/ERROR: [0-9]+:([0-9]+):/g;function BO(e,t){let n=VO.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],a=e.split(`
|
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`),s=a.length.toString().length+2,i=a.map((h,d)=>k.rightPad((d+1).toString(),s)+h),o=0;for(let h=0;h<i.length;h++)o=Math.max(i[h].length,o);let l=i.slice(0,r-1),c=i.slice(r-1,r),u=i.slice(r);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${k.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
|
|
`))}function iw(e){return aa(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function ow(e,t){if(_e(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function tp(e,t){if(_e(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function lw(e,t){let n=aa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return _e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),_e(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function uw(e,t){let n=aa(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return _e(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),_e(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function EO(){return Q().getNumber("WEBGL_VERSION")===2?1:4}function cw(e){return aa(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function hw(e,t){let n=Q().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function dw(e){return aa(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function wA(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(_e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),_e(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),_e(e,()=>e.enableVertexAttribArray(o)),!0)}function pw(e,t,n){Iw(e,n),_e(e,()=>e.activeTexture(e.TEXTURE0+n)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function CO(e,t){Iw(e,t),_e(e,()=>e.activeTexture(e.TEXTURE0+t)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function fw(e,t,n){return aa(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function mw(e,t,n){return e.getUniformLocation(t,n)}function Aw(e,t,n,r){_e(e,()=>pw(e,t,r)),_e(e,()=>e.uniform1i(n,r))}function RO(e){_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),_e(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),_e(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function np(e,t,n){_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),_e(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function _A(e,t){_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),_e(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function ac(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+yw(e,t))}function yw(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 ${t}`}}function aa(e,t,n){let r=_e(e,()=>t());if(r==null)throw new Error(n);return r}function Iw(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function oi(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function li(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 rp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[oi(e),...li(e)]),t}function gw(e,t=!1){let n=Q().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,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 r=k.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];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 a=oi(e),s=2,i=2;return e.length&&([s,i]=li(e)),r=a*(s/2)*(i/2),k.sizeToSquarishShape(r).map(o=>o*2)}return k.sizeToSquarishShape(r)}function ap(e){return e%2==0}function sc(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],r=t.slice(-1)[0];if(n===r||ap(n)&&ap(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&ap(e[0])&&ap(t[0])}var sp,ip;function xw(e){if(sp==null){let t=Wr(e);sp=t.getParameter(t.MAX_TEXTURE_SIZE)}return sp}function FO(){sp=null}function MO(){ip=null}function ww(e){if(ip==null){let t=Wr(e);ip=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,ip)}function _w(e){if(e===0)return 0;let t,n=Wr(e);return Vn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Vn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Vn(e,t){return e.getExtension(t)!=null}function bA(e){try{if(Wr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function bw(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!Vn(t,"OES_texture_float"))return!1}else if(!Vn(t,"EXT_color_buffer_float"))return!1;return IA(t)}function vw(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!Vn(t,"OES_texture_float")||!Vn(t,"WEBGL_color_buffer_float"))return!1}else{if(Vn(t,"EXT_color_buffer_float"))return IA(t);let n="EXT_color_buffer_half_float";if(Vn(t,n)){let r=t.getExtension(n);return UO(t,r)}return!1}return IA(t)}function IA(e){let t=kA(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,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 UO(e,t){let n=kA(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,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,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function kw(e){return e!==2?!1:Wr(e).fenceSync!=null}function ml(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 $e=Q();$e.registerFlag("HAS_WEBGL",()=>$e.getNumber("WEBGL_VERSION")>0);$e.registerFlag("WEBGL_VERSION",()=>bA(2)?2:bA(1)?1:0);$e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);$e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>$e.get("WEBGL_VERSION")===2);$e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);$e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);$e.registerFlag("WEBGL_PACK",()=>$e.getBool("HAS_WEBGL"));$e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_CLIP",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);$e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_REDUCE",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_LAZILY_UNPACK",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_CONV_IM2COL",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>xw($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>ww($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=$e.getNumber("WEBGL_VERSION");return e===0?0:_w(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Zh.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>bw($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>vw($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>kw($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>$e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);$e.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}.`)});function ln(){let e,t,n,r,a,s,i,o,l,c;return Q().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="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="",c=`
|
|
#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",r="varying",a="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));
|
|
}
|
|
`,c=`
|
|
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:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function ci(e,t,n="index"){let r=k.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function NA(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 Nw=`
|
|
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;
|
|
}
|
|
`,HO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ic.DENSE;let t=lc(e),n=ln();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${ci(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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;
|
|
}
|
|
`}},jO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ic.DENSE;let t=lc(e),n=ln();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${ci(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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;
|
|
}
|
|
`}},GO=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Un.DOWNLOAD;let t=ln();this.outputShape=e,this.userCode=`
|
|
${Nw}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},qO=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Un.DOWNLOAD;let t=ln();this.outputShape=e,this.userCode=`
|
|
${Nw}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},XO=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=ln(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${NA(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, ${a}.0);
|
|
vec4 values = ${r.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];
|
|
}
|
|
|
|
${r.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}},KO=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=ln(),[a,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 c=0;c<=1;c++){let u=l*2+c;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${c} < ${e[2]}) {
|
|
localCoords[2] += ${c};
|
|
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, ${a}.0);
|
|
values = ${r.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${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=`
|
|
${NA(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${r.output} = ${o};
|
|
}
|
|
`}},k0={};ze(k0,{bindVertexProgramAttributeStreams:()=>Dw,createBufferFromOutputTexture:()=>Pw,createFloat16MatrixTexture:()=>Rw,createFloat16PackedMatrixTexture:()=>$w,createFloat32MatrixTexture:()=>Cw,createIndexBuffer:()=>Ew,createPackedMatrixTexture:()=>Mw,createUnsignedBytesMatrixTexture:()=>Fw,createVertexBuffer:()=>Tw,createVertexShader:()=>Sw,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Ww,downloadFloat32MatrixFromBuffer:()=>Lw,downloadMatrixFromPackedOutputTexture:()=>Vw,downloadPackedMatrixFromBuffer:()=>Bw,getInternalFormatForFloat16MatrixTexture:()=>TA,getInternalFormatForFloat16PackedMatrixTexture:()=>RA,getInternalFormatForFloat32MatrixTexture:()=>SA,getInternalFormatForPackedMatrixTexture:()=>CA,getInternalFormatForUnsignedBytesMatrixTexture:()=>EA,uploadDenseMatrixToTexture:()=>Ow,uploadPixelDataToTexture:()=>zw});function Sw(e){let t=ln(),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 aw(e,n)}function Tw(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 lw(e,t)}function Ew(e){let t=new Uint16Array([0,1,2,2,1,3]);return uw(e,t)}function uc(e,t,n,r,a,s){hw(t,n);let i=cw(e),o=e.TEXTURE_2D;return _e(e,()=>e.bindTexture(o,i)),_e(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),_e(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),_e(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),_e(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),_e(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function SA(e){return e.internalFormatFloat}function Cw(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,SA(r),r.textureFormatFloat,e.FLOAT)}function TA(e){return e.internalFormatHalfFloat}function Rw(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,TA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function EA(e){return e.downloadTextureFormat}function Fw(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,EA(r),e.RGBA,e.UNSIGNED_BYTE)}function CA(e){return e.internalFormatPackedFloat}function Mw(e,t,n,r){let[a,s]=Al(t,n);return uc(e,a,s,CA(r),e.RGBA,e.FLOAT)}function RA(e){return e.internalFormatPackedHalfFloat}function $w(e,t,n,r){let[a,s]=Al(t,n);return uc(e,a,s,RA(r),e.RGBA,r.textureTypeHalfFloat)}function Dw(e,t,n){let r=0,a=3*4,s=3*4+2*4;return _e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),wA(e,t,"clipSpacePos",n,3,s,r)&&wA(e,t,"uv",n,2,s,a)}function Ow(e,t,n,r,a,s){_e(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),_e(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function zw(e,t,n){_e(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?_e(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):_e(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),_e(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Pw(e,t,n,r){let a=e.createBuffer();_e(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return _e(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),_e(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),_e(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function Lw(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function Ww(e,t,n,r){let[a,s]=oc(t,n),i=4,o=new Uint8Array(OO(t*n,i));return _e(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function Bw(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(zO(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function Vw(e,t,n){let r=new Float32Array(t*n*4);return _e(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var ym=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Q().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Am(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(Q().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=rc(this.gl,a),Vn(this.gl,s))this.textureHalfFloatExtension=rc(this.gl,s);else if(Q().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),Vn(this.gl,r))this.colorBufferHalfFloatExtension=rc(this.gl,r);else if(Q().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",Vn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Tw(this.gl),this.indexBuffer=Ew(this.gl),this.framebuffer=dw(this.gl),this.textureConfig=kA(this.gl,this.textureHalfFloatExtension)}get debug(){return Q().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;_e(e,()=>e.finish()),_e(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),_e(e,()=>e.deleteFramebuffer(this.framebuffer)),_e(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),_e(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),_e(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Cw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Rw(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Fw(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),zw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Ow(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),$w(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Mw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(_A(this.gl,this.framebuffer),this.outputTexture=null),_e(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Ww(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Bw(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Lw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=Pw(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Q().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Vw(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=sw(t,e),r=Sw(t),a=iw(t);return _e(t,()=>t.attachShader(a,r)),_e(t,()=>t.attachShader(a,n)),ow(t,a),this.debug&&tp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=Dw(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&_e(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&tp(this.gl,this.program),_e(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?fw(this.gl,e,t):mw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),_e(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(),Aw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=Al(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&tp(this.gl,this.program),ac(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),_e(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),_e(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rc(this.gl,Q().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(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Q().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,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Q().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,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=ZO(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(),np(this.gl,e,this.framebuffer),this.debug&&ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(np(this.gl,this.outputTexture,this.framebuffer),this.debug&&ac(this.gl)):_A(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;np(r,e,this.framebuffer),this.debug&&ac(r),this.outputTexture=e,_e(r,()=>r.viewport(0,0,t,n)),_e(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),_e(this.gl,()=>this.gl.scissor(e,t,n,r))}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 ZO(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Uw}=C;function sz(e,t,n,r){let a=[];e.forEach(p=>{let f=k.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
|
|
`),i=e.map(p=>JO(p,t,r)).join(`
|
|
`),o=t.texShape,l=ln(),c=ez(l),u,h,d=rz(l);return t.isPacked?(u=YO(t.logicalShape,o),h=nz(l)):(u=QO(t.logicalShape,o),h=tz(l)),r&&(d+=az),[d,c,h,s,u,i,n].join(`
|
|
`)}function yl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return iz(e);case 1:return oz(e);case 2:return lz(e);case 3:return uz(e);case 4:return cz(e);case 5:return hz(e);case 6:return dz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function Hw(e){switch(e.shapeInfo.logicalShape.length){case 0:return pz(e);case 1:return fz(e);case 2:return mz(e);case 3:return Az(e);default:return yz(e)}}function JO(e,t,n=!1){let r="";n?r+=Hw(e):r+=yl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=gz(e,t):r+=xz(e,t)),r}function YO(e,t){switch(e.length){case 0:return jw();case 1:return wz(e,t);case 2:return vz(e,t);case 3:return _z(e,t);default:return bz(e,t)}}function QO(e,t){switch(e.length){case 0:return jw();case 1:return kz(e,t);case 2:return Ez(e,t);case 3:return Iz(e,t);case 4:return Nz(e,t);case 5:return Sz(e,t);case 6:return Tz(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function ez(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function tz(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function nz(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function rz(e){return`${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);
|
|
}
|
|
|
|
${Cz}
|
|
${Rz}
|
|
${Fz}
|
|
`}var Cz=`
|
|
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);
|
|
}
|
|
`,Rz=`
|
|
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);
|
|
}
|
|
`,Fz=`
|
|
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);
|
|
}
|
|
`,az=`
|
|
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 jw(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function wz(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 kz(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function _z(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function Iz(e,t){let n=ci(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function bz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
|
|
int b${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 / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec${e.length}(${o});
|
|
}
|
|
`}function Nz(e,t){let n=ci(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function Sz(e,t){let n=ci(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function Tz(e,t){let n=ci(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function vz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Ez(e,t){return k.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function hi(e){return`offset${e}`}function pz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=ln();return`
|
|
vec4 ${n}() {
|
|
return ${r.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function iz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
|
|
float ${n}() {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let[s,i]=e.shapeInfo.texShape,o=hi(t);return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function fz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=ln();return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${a[0]}, ${a[1]}, index);
|
|
return ${s.texture2D}(${t}, uv);
|
|
}
|
|
`}function oz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${gl(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
|
|
float ${n}(int index) {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let i=hi(t);return s===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:a===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function mz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=ln();if(a!=null&&k.arraysEqual(t,a))return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
|
|
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function lz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&k.arraysEqual(t,a)){let h=a[0],d=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let h=xl(e,o),d=["row","col"];return`
|
|
${yl(h)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${wl(d,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
|
|
${gl(e)}
|
|
}
|
|
`;let l=a[0],c=a[1],u=hi(n);return c===1?`
|
|
float ${r}(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 ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(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}, ${c}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Az(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=xl(e,h),f=["b","row","col"];return`
|
|
${Hw(p)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${wl(f,d)});
|
|
}
|
|
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=ln();return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${c}, ${l}, b, row, col);
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`}function uz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let f=xl(e,l),m=["row","col","depth"];return`
|
|
${yl(f)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${wl(m,o)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${a}, ${s}, 1)));
|
|
${gl(e)}
|
|
}
|
|
`;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===s&&d==null)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let p=hi(n);return`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${s} + depth + ${p};
|
|
vec2 uv = uvFromFlat(${u}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function yz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=ln();return`
|
|
vec4 ${a}(${h}) {
|
|
int index = ${d};
|
|
int texR = index / ${l};
|
|
int texC = index - texR * ${l};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
|
|
return ${p.texture2D}(${r}, uv);
|
|
}
|
|
`}function cz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let f=xl(e,o),m=["row","col","depth","depth2"];return`
|
|
${yl(f)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${wl(m,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${s}, ${a}, 1)));
|
|
${gl(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(d===a&&c==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${t[1]*t[2]}, ${t[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let p=hi(n);return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${s} +
|
|
depth * ${a} + depth2;
|
|
vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function hz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let m=xl(e,l),A=["row","col","depth","depth2","depth3"];return`
|
|
${yl(m)}
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${r}(${wl(A,c)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${a})) +
|
|
depth3;
|
|
${gl(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(p===a&&u==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=hi(n);return`
|
|
float ${r}(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 * ${a} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function dz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=k.squeezeShape(t);if(a.length<t.length){let A=xl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${yl(A)}
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${r}(${wl(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${c}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${gl(e)}
|
|
}
|
|
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${c}, ${l}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===i&&h==null)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=hi(n);return`
|
|
float ${r}(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 * ${c} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${p}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function gl(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 ${t}[i];
|
|
}
|
|
}
|
|
`}function gz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=Uw(e.shapeInfo.logicalShape,t.logicalShape),l=ht(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
|
|
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=k.sizeFromShape(e.shapeInfo.logicalShape)===1,m=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(f&&!m)i===1?p=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:p=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${a}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${r}(${d});
|
|
${p}
|
|
}
|
|
`}function xz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"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 ${a}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let c=ht(l),u=Uw(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
|
|
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
|
|
float ${a}() {
|
|
${c} coords = getOutputCoords();
|
|
${d}
|
|
return get${r}(${f});
|
|
}
|
|
`}function ht(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 ${e} is not yet supported`)}function xl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function wl(e,t){return t.map(n=>e[n]).join(", ")}function Mz(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=sz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);Q().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function Gw(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,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!k.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} 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 $z(e,t,n,r,a){Gw(t.inShapeInfos,n),Gw([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),Q().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function Dz(e,t,n){let r="";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;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:Oz,bincountImpl:qw,bincountReduceImpl:zz,ceilImpl:Pz,concatImpl:Lz,expImpl:Wz,expm1Impl:Bz,floorImpl:Vz,gatherV2Impl:Uz,greaterImpl:Hz,lessImpl:jz,linSpaceImpl:Gz,logImpl:qz,maxImpl:Xz,maximumImpl:Kz,minimumImpl:Zz,multiplyImpl:Jz,negImpl:Yz,prodImpl:Qz,rangeImpl:eP,rsqrtImpl:tP,simpleAbsImpl:Xw,sliceImpl:nP,stridedSliceImpl:rP,subImpl:aP,tileImpl:sP,topKImpl:iP,transposeImpl:FA,uniqueImpl:oP}=mm;function Kw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:Kw(e,t)}function lP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var dP=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=un("rc",t),r=ht(t),a=uP(t,e,n),s=cP(t,e[e.length-1],e[e.length-2],n),i=hP(e,n);this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
|
|
if(${a}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function pP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function uP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function cP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
|
|
int r = ${a[0]};
|
|
int c = ${a[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function hP(e,t){let n=e.length,r=pP(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${r[0]}),
|
|
cEdge ? 0. : getA(${r[1]}),
|
|
rEdge ? 0. : getA(${r[2]}),
|
|
rEdge || cEdge ? 0. : getA(${r[3]})`}var Zw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
|
|
${a}
|
|
${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${r}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${r>0?"}":""}
|
|
`}this.userCode=`
|
|
${fP(t)}
|
|
${NA(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function fP(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${ci(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var mP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Yw(t,n),a=Qw(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Jw(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===Zt.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===Zt.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===Zt.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===Zt.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===Zt.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=Yw(n,r),s=Qw(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Jw(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=Q().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 AP(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 Jw(e,t,n,r,a){let s=yP(t,r),i;if(a){let[l,c]=Al(e[0],e[1]);i=l*c}else{let[l,c]=oc(e[0],e[1]);i=l*c}let o=AP(n,s);return i*o}function yP(e,t){switch(e){case Zt.PACKED_2X2_FLOAT32:return CA(t);case Zt.PACKED_2X2_FLOAT16:return RA(t);case Zt.UNPACKED_FLOAT32:return SA(t);case Zt.UNPACKED_FLOAT16:return TA(t);case Zt.PACKED_4X1_UNSIGNED_BYTE:return EA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function gP(e){return Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Zt.PACKED_2X2_FLOAT32:Zt.UNPACKED_FLOAT32:e?Zt.PACKED_2X2_FLOAT16:Zt.UNPACKED_FLOAT16}function Yw(e,t){if(e===Un.UPLOAD)return Zt.PACKED_2X2_FLOAT32;if(e===Un.RENDER||e==null)return gP(t);if(e===Un.DOWNLOAD||e===Un.PIXELS)return Zt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Qw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Fa=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);
|
|
}
|
|
`}},Ar="if (isnan(x)) return x;",xP="return x;",e_="return abs(x);",wP="return (x >= 0.0) ? x : (exp(x) - 1.0);",_P=Ar+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,bP=Ar+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,op="return x;",vP="return x;",kP=`
|
|
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;
|
|
`,IP=`
|
|
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;
|
|
`,NP=`
|
|
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;
|
|
`,_l=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},SP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=un("rc",t),r=ht(t),a=lP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${a});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},TP=Mr.whereImpl,EP=1e-7,CP=1e-4,MA={};function RP(e){return e in MA||(MA[e]={}),MA[e]}var FP=128,MP=600;function $P(){return Q().global.screen==null?1024:Q().global.screen.height*Q().global.screen.width*window.devicePixelRatio*MP/1024/1024}var Vu=class extends nu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!Q().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(Q().getNumber("WEBGL_VERSION"));this.binaryCache=RP(Q().getNumber("WEBGL_VERSION")),this.gpgpu=new ym(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new mP(this.gpgpu),this.numMBBeforeWarning=$P(),this.texData=new Ah(this,Tr())}nextDataId(){return Vu.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((Q().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Q().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 r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Un.UPLOAD,refCount:1}),r}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,r,a){if(Q().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Un.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new _l(i,op):h=new Fa(i,op);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=k.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=C.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=k.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new _l(r,op):p=new Fa(r,op);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&Q().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...lc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=k.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Tr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>k.decodeString(r))}catch(r){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(!nw(n))throw Q().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=k.sizeFromShape(t);if(Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...lc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=Q().getBool("WEBGL_PACK")&&r===!0,i=s?rp(t):t,o=s?new qO(i):new GO(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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 Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(Q().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:r,usage:a,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(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return Q().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Tr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=FP){let n=this.getCPUBackend();return!Q().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&k.sizeFromShape(r.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 TP(e.shape,t)}packedUnaryOp(e,t,n){let r=new _l(e.shape,t),a=this.compileAndRun(r,[e],n);return Tr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=Xw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(Q().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,e_,e.dtype);let t=new Fa(e.shape,e_),n=this.compileAndRun(t,[e]);return Tr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let a=n.map(s=>k.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Tr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new SP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new dP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[oi(e.shape),...li(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[oi(t),...li(t)],s=new Zw(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=rp(r),i;n?i=new jO(s):i=new HO(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===ic.DENSE){let f=lc(e.outputShape);i.texShape=f.map(m=>m*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 m=this.texData.get(f.dataId);if(m.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=Q().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:m.values};e.packedInputs&&(m.isPacked=!0,m.shape=f.shape)}else if(!!m.isPacked!=!!e.packedInputs)f=m.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),m=this.texData.get(f.dataId);else if(m.isPacked&&!sc(m.shape,f.shape)){let A=f,y=f.shape;f.shape=m.shape,f=this.packedReshape(f,y),o.push(f),m=this.texData.get(f.dataId),A.shape=y}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:m,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=Dz(e,l,c),h=this.getAndSaveBinary(u,()=>Mz(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;if(d&&(p=this.startTimer()),$z(this.gpgpu,h,l,c,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)})),!Q().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Q().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=U(()=>{if(!Q().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Q().getBool("DEBUG");Q().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Q().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EP:CP}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=k.now());let u=t.texShape;if(u==null&&(u=gw(n,o),t.texShape=u),a!=null){let h=rp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=Al(u[0],u[1]),d=new KO(h,[f,p],m)):d=new XO(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Un.PIXELS:this.texData.get(A.dataId).usage=Un.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),b=this.texData.get(g.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=k.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=DP(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};Vu.nextDataId=0;function DP(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 r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var I0="3.1.0";function N0(){Q().set("WEBGL_FORCE_F16_TEXTURES",!0)}Zh.isBrowser()&&vu("webgl",()=>new Vu,2);var z8={forceHalfFloat:N0},t_=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,bl=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));
|
|
}
|
|
`}},lp=`
|
|
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;
|
|
`,cc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||k.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${ht(a)} coords = getOutputCoords();
|
|
`,a===1)s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=un("coords",a);s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[a-2]} + 1) >= ${this.outputShape[a-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[a-1]} + 1) >= ${this.outputShape[a-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 Cn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var OP={kernelName:ps,backendName:"webgl",kernelFunc:Cn};function Ma(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Cn({inputs:{x:r},backend:n}),l=Cn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var zP={kernelName:bh,backendName:"webgl",kernelFunc:Ma},n_="return (a < 0.) ? b * a : a;",r_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function PP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(r_,a.shape,i.shape):new bl(n_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var LP={kernelName:fs,backendName:"webgl",kernelFunc:PP},a_="return (a < 0.) ? b * a : a;",s_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function WP(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(s_,r.shape,a.shape):new bl(a_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var BP={kernelName:Ns,backendName:"webgl",kernelFunc:WP},i_="if (isnan(x)) return x;",VP=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,UP=`
|
|
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 Je({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new _l(i.shape,t):u=new Fa(i.shape,e),o.runWebGLProgram(u,[i],l)}}function Jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(b=>{let[x,w]=b,_={dataId:x.dataId,dtype:x.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:c.shape},T=new bl(e,l.shape,c.shape);return u.runWebGLProgram(T,[_,N],Yn(x.dtype,w.dtype))}),g=Ma({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||Yn(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),b=u.texData.get(g.dataId);return b.values=A,g}let d=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new cc(t,l.shape,c.shape,n):p=new bl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function up(e,t=!1){if(e==="linear")return t?vP:xP;if(e==="relu")return t?IP:_P;if(e==="elu")return t?kP:wP;if(e==="relu6")return t?NP:bP;if(e==="prelu")return t?s_:a_;if(e==="leakyrelu")return t?r_:n_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var o_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:m=`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",b="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(b=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
|
|
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 = ${b};
|
|
vec4 a = getMatrixA(batchA, ${h});
|
|
vec4 b = getMatrixB(batchB, ${d});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${p[0]} * ${f[0]});
|
|
result += (${p[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${A}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},l_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},u_=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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},c_="return a * b;";function h_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new u_(l_.REAL,r.shape,a.shape),u=new u_(l_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ma({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=Jz(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new cc(c_,r.shape,a.shape):i=new bl(c_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var HP={kernelName:bs,backendName:"webgl",kernelFunc:h_};function jP(e,t,n){let r=[oi(e.shape),...li(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[oi(t),...li(t)],i=new Zw(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ge(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=k.sizeFromShape(a.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!sc(a.shape,l)&&!(u.texture!==null&&sc(u.shape,l))?jP(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var GP={kernelName:Io,backendName:"webgl",kernelFunc:ge},d_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,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 c="";a%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},qP=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 c=Math.floor(n/4)*4,u=n%4,h=`
|
|
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);
|
|
}
|
|
`,d="vec4";t==="all"?(i="1.0",h=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,d="bvec4"):t==="any"&&(i="0.0",h=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,d="bvec4");let p="";a%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
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) {
|
|
${p}
|
|
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 < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${h}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${u===1}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
} else if (${u===2}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
} else if (${u===3}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function XP(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function di(e,t,n,r){let a=XP(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new d_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new d_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new qP({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var ZP=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 r=ht(this.rank),a=KP(t);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function KP(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"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var JP=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ht(this.rank),a=Kw("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${o}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${a[this.rank-1]};
|
|
if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${o}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function cp(e,t,n){let r=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new JP(e.shape,t):new ZP(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function YP(e,t,n,r){let a=t,s=e.shape.length,i=k.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=cp(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let f=k.sizeFromShape(d),m=k.sizeFromShape(e.shape)/f,A=ge({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=Kh(e.dtype),g=di(A,y,"sum",r),b=ge({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),b}function $A(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return YP(a,s,i,n)}var QP={kernelName:Os,backendName:"webgl",kernelFunc:$A};function An(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=FA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=cp(a,s,i);return c}var eL={kernelName:Bs,backendName:"webgl",kernelFunc:An},p_=1e3;function hp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=k.sizeFromShape(m),g=k.sizeFromShape(A),b=y===g||y===1||g===1;k.assert(c>=2&&u>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let x=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);k.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],_=r?[g,f,d]:[g,d,f],N=ge({inputs:{x:e},backend:a,attrs:{shape:w}}),T=ge({inputs:{x:t},backend:a,attrs:{shape:_}}),E=[N,T],M=Math.max(y,g),z=n?N.shape[1]:N.shape[2],P=s!=null,B=i!=null,G=l==="leakyrelu",V=l!=null?up(l,!0):null,K=P||B||G||V!=null,X;if((p===1||f===1)&&z>p_&&K===!1){let J=N,ae=T;n&&(J=An({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(J)),r&&(ae=An({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let Y=f!==1,ue=f===1,ne=J;Y&&(ne=ge({inputs:{x:J},backend:a,attrs:{shape:[M,z,1]}}),E.push(ne));let de=f===1?2:1,he=ae;ue&&(he=ge({inputs:{x:ae},backend:a,attrs:{shape:[M,1,z]}}),E.push(he));let me=h_({inputs:{a:ne,b:he},backend:a});X=$A({inputs:{x:me},backend:a,attrs:{axis:de,keepDims:!0}}),E.push(me)}else{let J=Yn(e.dtype,t.dtype),ae=new o_(w,_,[M,p,f],n,r,P,V,B,G),Y=[N,T];if(s!=null&&Y.push(s),B&&Y.push(i),G){let ue=a.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Y.push(ue),E.push(ue)}X=a.runWebGLProgram(ae,Y,J)}let ee=ge({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let J of E)a.disposeIntermediateTensorInfo(J);return ee}function tL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return hp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var nL={kernelName:Vs,backendName:"webgl",kernelFunc:tL},f_="return abs(x);";function rL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=Xw(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new _l(r.shape,f_):a=new Fa(r.shape,f_),n.runWebGLProgram(a,[r],r.dtype)}var aL={kernelName:Wi,backendName:"webgl",kernelFunc:rL},sL=Ar+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,iL=Je({opSnippet:sL}),oL={kernelName:Bi,backendName:"webgl",kernelFunc:iL},lL=Ar+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,uL=Je({opSnippet:lL}),cL={kernelName:Vi,backendName:"webgl",kernelFunc:uL},m_="return a + b;",hL=Jt({opSnippet:m_,packedOpSnippet:m_,supportsComplex:!0,cpuKernelImpl:Oz}),dL={kernelName:ma,backendName:"webgl",kernelFunc:hL},pL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${r};
|
|
setOutput(result);
|
|
}
|
|
`}},fL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${r};
|
|
setOutput(result);
|
|
}
|
|
`}};function dp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Cn({inputs:{x:r[0]},backend:n});if(r.length>Q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=dp({inputs:r.slice(0,o),backend:n}),c=dp({inputs:r.slice(o),backend:n});return dp({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>Yn(o,l)),s=r.map(o=>o.shape),i=Q().getBool("WEBGL_PACK")?new fL(r[0].shape,s):new pL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var mL={kernelName:Za,backendName:"webgl",kernelFunc:dp};function AL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=di(m,m.dtype,"all",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var yL={kernelName:yh,backendName:"webgl",kernelFunc:AL};function gL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=di(m,m.dtype,"any",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var xL={kernelName:gh,backendName:"webgl",kernelFunc:gL},wL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,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 * ${r};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${r}; i++) {
|
|
int inIdx = ${o};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},_L=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ht(o),c=un("coords",o),u,h;if(s===1){h=o+1;let N=ht(h);u=`
|
|
${N} sourceLocR = ${N}(${c.join()}, 0);
|
|
++${c[o-1]};
|
|
${N} sourceLocG = ${N}(${c.join()}, 0);
|
|
++${c[o-2]};
|
|
${N} sourceLocA = ${N}(${c.join()}, 0);
|
|
--${c[o-1]};
|
|
${N} sourceLocB = ${N}(${c.join()}, 0);
|
|
--${c[o-2]};`}else h=o,u=`
|
|
${l} sourceLocR = coords;
|
|
++${c[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${c[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${c[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=un("sourceLocR",h-1).concat("inIdx.r"),A=un("sourceLocG",h-1).concat("inIdx.g"),y=un("sourceLocB",h-1).concat("inIdx.b"),g=un("sourceLocA",h-1).concat("inIdx.a"),b=n==="max"?"greaterThan":"lessThan",x=r?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${g.join()})));`,w=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,_=r?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${d.join()}),
|
|
vec2(${d.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${d.join()}),
|
|
vec2(${d.slice(-2).join()}));
|
|
}
|
|
${_}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
|
|
sourceLocB${p}, sourceLocA${p}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${w};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${x}
|
|
vec4 candidate = ${w};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${b}(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 A_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new wL(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=A_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function y_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new _L(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=y_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function g_(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!Q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=k.sizeFromShape(o),c=ge({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=A_(e,c,r);s.push(u);let h=ge({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return y_(e,t,r)}function bL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=An({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=g_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var vL={kernelName:Ja,backendName:"webgl",kernelFunc:bL};function kL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=k.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=An({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=g_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var IL={kernelName:ru,backendName:"webgl",kernelFunc:kL},NL=Ar+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,SL=Je({opSnippet:NL}),TL={kernelName:Ui,backendName:"webgl",kernelFunc:SL},EL=Ar+"return log(x + sqrt(x * x + 1.0));",CL=Je({opSnippet:EL}),RL={kernelName:Hi,backendName:"webgl",kernelFunc:CL},FL=Ar+`
|
|
return atan(x);
|
|
`,ML=Je({opSnippet:FL}),$L={kernelName:ji,backendName:"webgl",kernelFunc:ML},DL=VP+`
|
|
return atan(a, b);
|
|
`,OL=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+UP+`
|
|
return result;
|
|
`,zL=Jt({opSnippet:DL,packedOpSnippet:OL}),PL={kernelName:qi,backendName:"webgl",kernelFunc:zL},LL=Ar+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,WL=Je({opSnippet:LL}),BL={kernelName:Gi,backendName:"webgl",kernelFunc:WL},hc=class{constructor(e,t,n,r=!1,a=!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,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${d}, ${p});
|
|
|
|
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 < ${h};
|
|
wC += ${c}) {
|
|
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 = ${r?a?m:A:`wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let g="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let x=Math.floor(s/4)*4,w=s%4,_=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${g}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${d}, ${p});
|
|
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 < ${x}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${_}
|
|
}
|
|
|
|
int xC = xCCorner + ${x};
|
|
if (${w===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${_}
|
|
} else if (${w===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${_}
|
|
} else if (${w===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${_}
|
|
}
|
|
}
|
|
setOutput(${b});
|
|
}
|
|
`}},DA=class{constructor(e,t,n,r=!1,a=!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,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",b="0.0";if(g||(b="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${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 < ${d};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${h}) {
|
|
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 ${E} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${p} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let x="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let _=Math.floor(s/4)*4,N=s%4,T=`
|
|
if (${g}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${x}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${A}, ${y});
|
|
const float initializationValue = ${b};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${b});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${d};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${_}; wC += 4) {
|
|
int xC = xCCorner + wC * ${h};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
|
|
);
|
|
|
|
${T}
|
|
}
|
|
|
|
int xC = xCCorner + ${_};
|
|
if (${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 + ${h}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${N===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
}
|
|
`}};function VL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ml(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Cn({inputs:{x:a},backend:n});let h=new hc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var UL={kernelName:Ya,backendName:"webgl",kernelFunc:VL};function HL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new DA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var jL={kernelName:au,backendName:"webgl",kernelFunc:HL},GL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${u});
|
|
const float avgMultiplier = float(${h});
|
|
|
|
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) / ${r}.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) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},qL=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${f}, ${m});
|
|
const float avgMultiplier = float(${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) / ${a}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
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 < ${d};
|
|
wC += ${c}) {
|
|
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 XL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new qL(d);return n.runWebGLProgram(p,[a],i.dtype)}var KL={kernelName:wh,backendName:"webgl",kernelFunc:XL};function ZL(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ml([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new GL(u);return n.runWebGLProgram(h,[a],i.dtype)}var JL={kernelName:xh,backendName:"webgl",kernelFunc:ZL};function YL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return hp({a,b:s,transposeA:i,transposeB:o,backend:n})}var QL={kernelName:Qa,backendName:"webgl",kernelFunc:YL},eW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),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)));
|
|
}
|
|
`}},tW=class{constructor(e,t,n,r,a,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)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),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);
|
|
}
|
|
`}},nW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;k.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||a.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 c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=Q().getBool("WEBGL_PACK_NORMALIZATION")?new tW(r.shape,a.shape,s.shape,u,h,l):new eW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},rW={kernelName:hs,backendName:"webgl",kernelFunc:nW},sW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ht(this.rank),n=`uniform int start[${this.rank}];`,r=aW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${OA[o]} = start[${o}] + coords.${OA[o]};`);a=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${s.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${n}
|
|
void main() {
|
|
${a}
|
|
setOutput(getSource(${r}));
|
|
}
|
|
`}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)}}},OA=["x","y","z","w","u","v"];function aW(e){if(e===1)return"sourceLoc";if(e<=6)return OA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var iW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ht(this.rank),n=un("coords",this.rank),r=un("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
|
|
result.x = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${r[this.rank-1]};
|
|
result.y = ${s};
|
|
--${r[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${r[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${r[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[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 oW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=an.computeFlatOffset(t,k.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function dc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=an.parseSliceParams(a,s,i);if(an.assertParamsValid(a,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=nP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=an.isSliceContinous(a.shape,o,l);if(c||!u){let h=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iW(l):new sW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),oW(a,o,l,n)}var lW={kernelName:Eo,backendName:"webgl",kernelFunc:dc},uW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;k.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,b)=>g*b),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=[],f=ge({inputs:{x:a},backend:n,attrs:{shape:l}}),m=An({inputs:{x:f},backend:n,attrs:{perm:c}}),A=ge({inputs:{x:m},backend:n,attrs:{shape:u}}),y=dc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},cW={kernelName:su,backendName:"webgl",kernelFunc:uW};function hW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=qw(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var dW={kernelName:_h,backendName:"webgl",kernelFunc:hW},pW="return float(a != b);",x_=Jt({opSnippet:pW,dtype:"bool"}),fW={kernelName:yo,backendName:"webgl",kernelFunc:x_};function pc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Cn({inputs:{x:a.complexTensorInfos.real},backend:n})}var mW={kernelName:Vh,backendName:"webgl",kernelFunc:pc},AW="return float(int(x));";function yW(e,t){let n=new Fa(e.shape,AW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function zA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Cn({inputs:{x:a},backend:n});let i=Ct(a.shape),o=zA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ma({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=pc({inputs:{input:a},backend:n}),o=zA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(a.dtype,s)){let i=Cn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return yW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=x_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var gW={kernelName:es,backendName:"webgl",kernelFunc:zA},w_="return ceil(x);",xW=Je({opSnippet:w_,packedOpSnippet:w_,cpuKernelImpl:Pz}),wW={kernelName:ts,backendName:"webgl",kernelFunc:xW},_W=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,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},bW=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,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function vW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;Q().getBool("WEBGL_PACK_CLIP")?o=new bW(a.shape):o=new _W(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var kW={kernelName:Aa,backendName:"webgl",kernelFunc:vW},IW=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 __(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function NW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new IW(r.shape),i=[__(r,a.complexTensorInfos.real),__(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var SW={kernelName:iu,backendName:"webgl",kernelFunc:NW},TW=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-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},EW=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ht(r),s=un("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${c.join()}));
|
|
}`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
|
|
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${pp(i,l,m)}),
|
|
vec2(${pp(c,l,m)}));
|
|
}`}let d=o.length,p=o[o.length-1];h+=`
|
|
return getChannel(
|
|
getT${d}(${pp(i,l,p)}),
|
|
vec2(${pp(c,l,p)}));`,this.userCode=`
|
|
float getValue(${i.map(f=>"int "+f)}) {
|
|
${h}
|
|
}
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[r-1]} = ${s[r-1]} + 1;
|
|
if (${s[r-1]} < ${n[r-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[r-2]} = ${s[r-2]} + 1;
|
|
if (${s[r-2]} < ${n[r-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[r-1]} = ${s[r-1]} - 1;
|
|
if (${s[r-2]} < ${n[r-2]} &&
|
|
${s[r-1]} < ${n[r-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function pp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function fp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Cn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var CW={kernelName:Dh,backendName:"webgl",kernelFunc:fp};function vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>pc({inputs:{input:f},backend:n})),u=e.map(f=>fp({inputs:{input:f},backend:n})),h=vl(c,t,n),d=vl(u,t,n),p=Ma({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=b_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=Lz(h,u,r,d),f=C.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>Q().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=vl(e.slice(0,c),t,n),h=vl(e.slice(c),t,n),d=vl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new EW(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=b_(e,t,n),i=new TW(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=ge({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function b_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ge({inputs:{x:a},attrs:{shape:[-1,k.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function v_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=k.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Cn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),vl(o,s,n)}var RW={kernelName:Xi,backendName:"webgl",kernelFunc:v_},k_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,b="",x="";n&&(r?b=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?b=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:b=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,x="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${b}
|
|
|
|
const ivec2 strides = ivec2(${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 < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${c};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${p}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${p}) *
|
|
getW(wR, wC, ${p}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${p}, xR, xC) *
|
|
getW(wR, wC, ${p}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${p}, d2),
|
|
getW(wR, wC, ${p} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${p}),
|
|
getX(batch, xR, xC, ${p} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${p}, xR, xC),
|
|
getX(batch, ${p} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${p}, d2),
|
|
getW(wR, wC, ${p} + 1, d2),
|
|
getW(wR, wC, ${p} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${p}),
|
|
getX(batch, xR, xC, ${p} + 1),
|
|
getX(batch, xR, xC, ${p} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${p}, xR, xC),
|
|
getX(batch, ${p} + 1, xR, xC),
|
|
getX(batch, ${p} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${w}
|
|
${x}
|
|
setOutput(result);
|
|
}
|
|
`}},FW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${a}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${r});
|
|
|
|
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 < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${p}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${p}) *
|
|
getW(wF, wR, wC, ${p}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${p}),
|
|
getX(batch, xF, xR, xC, ${p} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${p}, d2),
|
|
getW(wF, wR, wC, ${p} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${p}),
|
|
getX(batch, xF, xR, xC, ${p} + 1),
|
|
getX(batch, xF, xR, xC, ${p} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${p}, d2),
|
|
getW(wF, wR, wC, ${p} + 1, d2),
|
|
getW(wF, wR, wC, ${p} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},MW=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=ln(),A=h==="channelsLast",y=A?0:1,g=A?1:2,b="";for(let x=0;x<=1;x++)for(let w=0;w<=1;w++)b+=`
|
|
blockIndex = rc.y + ${w};
|
|
pos = rc.x + ${x};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
|
|
d0 = offsetY + ${u} * (pos / ${f});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
|
|
d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
|
|
|
|
if(d1 < ${t[g]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${a}.));
|
|
|
|
if (${A}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${x*2+w}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${x*2+w}] = 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;
|
|
|
|
${b}
|
|
|
|
${m.output} = result;
|
|
}
|
|
`}};function I_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>p_,b=l[2]%2!=0&&!!c.isPacked;if(g||!Q().getBool("WEBGL_LAZILY_UNPACK")||!Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!b){let x=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ge({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),_=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=hp({a:w,b:_,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ge({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(_),y.push(N)}else{let x=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},_=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(sc(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let N=ge({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=hp({a:w,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=_,E.shape=n.outShape,A=Cn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let x of y)r.disposeIntermediateTensorInfo(x);return A}function N_({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,b=!1,x=[],w=ge({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),_=ge({inputs:{x:t},backend:r,attrs:{shape:[1,m,k.sizeFromShape(t.shape)/m]}});x.push(w),x.push(_);let N=new MW(y,w.shape,n),T=r.runWebGLProgram(N,[w],"float32"),E=ge({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});x.push(T),x.push(E);let M=a!=null,z=s!=null,P=o==="leakyrelu",B=o?up(o,!0):null,G=new o_(E.shape,_.shape,[1,A,n.outChannels],g,b,M,B,z,P),V=[E,_];if(a&&V.push(a),z&&V.push(s),P){let J=r.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));V.push(J),x.push(J)}let K=r.runWebGLProgram(G,V,"float32"),X=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=ge({inputs:{x:K},backend:r,attrs:{shape:X}});x.push(K);for(let J of x)r.disposeIntermediateTensorInfo(J);return ee}function $W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=I_({x:a,filter:s,convInfo:d,backend:n});else if(Q().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=N_({x:a,filter:s,convInfo:d,backend:n});else{let m=new k_(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=ge({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var DW={kernelName:ns,backendName:"webgl",kernelFunc:$W},OW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=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} - ${r};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${a};
|
|
|
|
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);
|
|
}
|
|
`}},zW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=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[${l}], coords[${c}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${r}.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) / ${a}.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);
|
|
}
|
|
`}},PW=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=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} - ${a};
|
|
|
|
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 * ${r} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},LW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${a}.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 < ${r}; 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 = ${r} - 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 WW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new OW(d);return n.runWebGLProgram(p,[a,s],"float32")}var BW={kernelName:vh,backendName:"webgl",kernelFunc:WW};function VW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(c),d=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new zW(d);return n.runWebGLProgram(p,[a,s],"float32")}var UW={kernelName:rs,backendName:"webgl",kernelFunc:VW};function HW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new FW(c);return n.runWebGLProgram(u,[a,s],"float32")}var jW={kernelName:ou,backendName:"webgl",kernelFunc:HW};function GW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new PW(c);return n.runWebGLProgram(u,[a,s],"float32")}var qW={kernelName:kh,backendName:"webgl",kernelFunc:GW};function XW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new LW(c);return n.runWebGLProgram(u,[a,s],"float32")}var KW={kernelName:Ih,backendName:"webgl",kernelFunc:XW},ZW=i_+`
|
|
return cos(x);
|
|
`,JW=Je({opSnippet:ZW}),YW={kernelName:as,backendName:"webgl",kernelFunc:JW},QW=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,eB=Je({opSnippet:QW}),tB={kernelName:Ki,backendName:"webgl",kernelFunc:eB},nB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,b,x]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${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 = ${b};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${p} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
float in_x = ${x};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${d} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},rB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new nB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},aB={kernelName:Zi,backendName:"webgl",kernelFunc:rB},E_=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${S_(r,"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() {
|
|
${ht(r)} coords = getOutputCoords();
|
|
int end = ${T_(r,"coords")};
|
|
float val = ${a};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${T_(r,"coords")} = idx;
|
|
val += getX(${S_(r,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function S_(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 ${e} is not yet supported`)}function T_(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function sB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=An({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Cn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new E_(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new E_(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=An({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var iB={kernelName:ss,backendName:"webgl",kernelFunc:sB};function oB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=qw(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=zz(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var lB={kernelName:Nh,backendName:"webgl",kernelFunc:oB},uB=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 cB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new uB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var hB={kernelName:Ji,backendName:"webgl",kernelFunc:cB},C_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?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"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${u});
|
|
const ivec2 pads = ivec2(${o}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${m};
|
|
int q = d2 - d1 * ${m};
|
|
|
|
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 < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${h};
|
|
|
|
if (xR < 0 || xR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
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}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},R_=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let x=0;x<p;x++)for(let w=0;w<f;w++)A+=`
|
|
vec4 xTexelR${x}C${w*2} = vec4(0.);
|
|
vec4 wR${x}C${w} = vec4(0.);
|
|
vec4 xR${x}C${w} = vec4(0.);`;for(let x=0;x<p;x++)for(let w=0;w<m;w++){let _=w*2;if(A+=`
|
|
xR = xRCorner + ${x*h};
|
|
xC = xCCorner + ${_*d};
|
|
`,u===1){if(_<f&&(l%2==1?A+=`
|
|
xCOffset = xC + 1;
|
|
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${x}C${_} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
xTexelR${x}C${_}.zw = vec2(0.);
|
|
}
|
|
} else {
|
|
xTexelR${x}C${_} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + 1 - 2;
|
|
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
vec4 previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
previous.zw = vec2(0.);
|
|
}
|
|
|
|
xR${x}C${_} = vec4(previous.zw, xTexelR${x}C${_}.xy);
|
|
} else {
|
|
xR${x}C${_} = vec4(0, 0, xTexelR${x}C${_}.xy);
|
|
}
|
|
`:A+=`
|
|
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
|
|
xTexelR${x}C${_} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${x}C${_} = vec4(0.);
|
|
}
|
|
|
|
xR${x}C${_} = xTexelR${x}C${_};
|
|
`,_+1<f)){let N=l%2==0?k.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
|
|
xCOffset = xC + ${l%2} + ${N};
|
|
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${x}C${_+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`,d>1&&(A+=`
|
|
xCOffset -= 2;
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${x}C${_} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${x}C${_} = vec4(0.);
|
|
}
|
|
`),A+=`
|
|
xR${x}C${_+1} = vec4(
|
|
xTexelR${x}C${_}.zw, xTexelR${x}C${_+2}.xy);
|
|
`):A+=`
|
|
xCOffset = xC + ${N};
|
|
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${x}C${_+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
|
|
xR${x}C${_+1} = xTexelR${x}C${_+2};
|
|
`}}else _<f&&(A+=`
|
|
if(xR >= 0 && xR < ${s}) {
|
|
`,l%2==1?(A+=`
|
|
xCOffset = xC + 1 - ${u};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${x}C${_} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${x}C${_} = vec4(0.);
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${i}) {
|
|
xTexelR${x}C${_+2} = getX(batch, xR, xC + 1, d1);
|
|
} else {
|
|
xTexelR${x}C${_+2} = vec4(0.);
|
|
}
|
|
|
|
xR${x}C${_} = vec4(
|
|
xTexelR${x}C${_}.zw, xTexelR${x}C${_+2}.zw);
|
|
`,_+1<f&&(A+=`
|
|
vec4 final = vec4(0.);
|
|
xCOffset = xC + 1 + ${u};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xR${x}C${_+1} = vec4(xTexelR${x}C${_+2}.xy, final.xy);
|
|
`)):(A+=`
|
|
if(xC >= 0 && xC < ${i}) {
|
|
xTexelR${x}C${_} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${x}C${_} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + ${u};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${x}C${_+2} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${x}C${_+2} = vec4(0.);
|
|
}
|
|
|
|
xR${x}C${_} = vec4(
|
|
xTexelR${x}C${_}.xy, xTexelR${x}C${_+2}.xy);
|
|
`,_+1<f&&(A+=`
|
|
xR${x}C${_+1} = vec4(
|
|
xTexelR${x}C${_}.zw, xTexelR${x}C${_+2}.zw);
|
|
`)),A+="}");_<f&&(A+=`
|
|
vec4 wTexelR${x}C${_} = getW(${x}, ${_}, d1, q);
|
|
wR${x}C${_} = vec4(wTexelR${x}C${_}.xz, wTexelR${x}C${_}.xz);
|
|
`,_+1<f&&(A+=`
|
|
vec4 wTexelR${x}C${_+1} = getW(${x}, ${_+1}, d1, q);
|
|
wR${x}C${_+1} =
|
|
vec4(wTexelR${x}C${_+1}.xz, wTexelR${x}C${_+1}.xz);`))}for(let x=0;x<p;x++)for(let w=0;w<f;w++)A+=`dotProd += xR${x}C${w} * wR${x}C${w};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?y=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:y=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,g="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${y}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${u});
|
|
const ivec2 pads = ivec2(${o}, ${l});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2;
|
|
int q = 0;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
vec4 dotProd = vec4(0.);
|
|
|
|
${A}
|
|
|
|
vec4 result = dotProd;
|
|
${b}
|
|
${g}
|
|
setOutput(result);
|
|
}
|
|
`}};function dB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,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 h=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new R_(h):d=new C_(h),n.runWebGLProgram(d,[a,s],"float32")}var pB={kernelName:is,backendName:"webgl",kernelFunc:dB},fB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=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} - ${r};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${a};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},mB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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) / ${r}.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) / ${a}.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 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function AB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new fB(h);return n.runWebGLProgram(d,[a,s],"float32")}var yB={kernelName:Sh,backendName:"webgl",kernelFunc:AB};function gB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new mB(h);return n.runWebGLProgram(d,[a,s],"float32")}var xB={kernelName:Th,backendName:"webgl",kernelFunc:gB},wB=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function _B(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=k.sizeFromShape(r.shape),i=ge({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new wB(s),l=n.runWebGLProgram(o,[i],i.dtype),c=ge({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var bB={kernelName:Eh,backendName:"webgl",kernelFunc:_B},vB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
|
|
const ivec2 strides = ivec2(${a}, ${s});
|
|
const ivec2 pads = ivec2(${u}, ${h});
|
|
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 * ${c};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function kB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new vB(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=ge({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var IB={kernelName:lu,backendName:"webgl",kernelFunc:kB},NB="return (x >= 0.0) ? x : (exp(x) - 1.0);",SB=`
|
|
vec4 result;
|
|
|
|
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
|
|
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
|
|
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
|
|
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
|
|
|
|
return result;
|
|
`,TB=Je({opSnippet:NB,packedOpSnippet:SB}),EB={kernelName:Yi,backendName:"webgl",kernelFunc:TB},CB="return (b >= 1.0) ? a : a * (b + 1.0);",RB=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,FB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(RB,r.shape,a.shape):new bl(CB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},MB={kernelName:Fh,backendName:"webgl",kernelFunc:FB},$B=`
|
|
return vec4(equal(a, b));
|
|
`,DB="return float(a == b);",OB=Jt({opSnippet:DB,packedOpSnippet:$B,dtype:"bool"}),zB={kernelName:eo,backendName:"webgl",kernelFunc:OB},PB=`
|
|
// 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 = ${C.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,LB=Je({opSnippet:PB}),WB={kernelName:Qi,backendName:"webgl",kernelFunc:LB},F_="return exp(x);",M_=Je({opSnippet:F_,packedOpSnippet:F_,cpuKernelImpl:Wz}),BB={kernelName:ls,backendName:"webgl",kernelFunc:M_};function PA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(k.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ge({inputs:{x:s},backend:r,attrs:{shape:o}})}var VB={kernelName:to,backendName:"webgl",kernelFunc:PA},$_="return exp(x) - 1.0;",UB=Je({opSnippet:$_,packedOpSnippet:$_,cpuKernelImpl:Bz}),HB={kernelName:no,backendName:"webgl",kernelFunc:UB},D_=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.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 = ${a};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${r});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${r}; 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 O_(e,t,n){let r=n.texData.get(e.dataId),a=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ge({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new D_("real",l,t),u=new D_("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ma({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=ge({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function jB(e){let{inputs:t,backend:n}=e,{input:r}=t;return O_(r,!1,n)}var GB={kernelName:Mh,backendName:"webgl",kernelFunc:jB},qB=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 LA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||k.inferDtype(a),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new qB(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var XB={kernelName:uu,backendName:"webgl",kernelFunc:LA},KB=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},ZB={kernelName:ro,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new KB(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},z_="return floor(x);",JB=Je({opSnippet:z_,packedOpSnippet:z_,cpuKernelImpl:Vz}),YB={kernelName:us,backendName:"webgl",kernelFunc:JB},QB=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,eV=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,tV=Jt({opSnippet:QB,packedOpSnippet:eV,dtype:"int32"}),nV={kernelName:cs,backendName:"webgl",kernelFunc:tV},rV=class{constructor(e){this.variableNames=["A"];let t=ln(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},aV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=ln(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${r}.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;
|
|
}
|
|
`}},iV={kernelName:qh,backendName:"webgl",kernelFunc:sV},kl;function sV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[c,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],h=[u,c],d=[u,c,s];(o||i||l)&&(kl==null&&(kl=document.createElement("canvas").getContext("2d")),kl.canvas.width=c,kl.canvas.height=u,kl.drawImage(a,0,0,c,u),a=kl.canvas);let p=n.makeTensorInfo(h,"int32");n.texData.get(p.dataId).usage=Un.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);let f=Q().getBool("WEBGL_PACK")?new aV(d):new rV(d),m=n.runWebGLProgram(f,[p],"int32");return n.disposeData(p.dataId),m}function oV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(u),A=C.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),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=I_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(Q().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=N_({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,w=o!=null,_=p==="leakyrelu",N=p?up(p,!1):null,T=new k_(A,x,N,w,_),E=[a,s];if(i&&E.push(i),o&&E.push(o),_){let M=n.makeTensorInfo([],"float32",k.createScalarValue(f,"float32"));E.push(M),g.push(M)}y=n.runWebGLProgram(T,E,"float32")}let b=ge({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),b}var lV={kernelName:Us,backendName:"webgl",kernelFunc:oV};function uV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),k.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?up(d,y):null,b=[a,s],x=i!=null,w=o!=null,_=d==="leakyrelu";if(x&&b.push(i),w&&b.push(o),_){let E=n.makeTensorInfo([],"float32",k.createScalarValue(p,"float32"));b.push(E),f.push(E)}let N;y?N=new R_(A,x,g,w,_):N=new C_(A,x,g,w,_);let T=n.runWebGLProgram(N,b,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var cV={kernelName:Hs,backendName:"webgl",kernelFunc:uV},hV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ht(t.length),a=ht(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${r} strides = ${r}(${this.strides});
|
|
void main() {
|
|
${a} 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 dV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=ge({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=ge({inputs:{x:r},backend:n,attrs:{shape:[k.sizeFromShape(r.shape)/c,c]}}),p=new hV(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=ge({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var pV={kernelName:so,backendName:"webgl",kernelFunc:dV},mV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ht(this.rank),r=fV(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function fV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function AV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=k.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=k.sizeFromShape(s.shape),h=[],d=ge({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=ge({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),b=n.bufferSync(d),x=Uz(b,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let m=new mV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=ge({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var yV={kernelName:ao,backendName:"webgl",kernelFunc:AV},gV="return float(a > b);",xV=`
|
|
return vec4(greaterThan(a, b));
|
|
`,wV=Jt({opSnippet:gV,packedOpSnippet:xV,cpuKernelImpl:Hz,dtype:"bool"}),_V={kernelName:io,backendName:"webgl",kernelFunc:wV},bV="return float(a >= b);",vV=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,kV=Jt({opSnippet:bV,packedOpSnippet:vV,dtype:"bool"}),IV={kernelName:ds,backendName:"webgl",kernelFunc:kV};function NV(e){let{inputs:t,backend:n}=e,{input:r}=t;return O_(r,!0,n)}var SV={kernelName:$h,backendName:"webgl",kernelFunc:NV},TV="return float(!isnan(x) && !isinf(x));",EV=Je({opSnippet:TV,dtype:"bool"}),CV={kernelName:oo,backendName:"webgl",kernelFunc:EV},RV="return float(isinf(x));",FV=Je({opSnippet:RV,dtype:"bool"}),MV={kernelName:lo,backendName:"webgl",kernelFunc:FV},$V="return float(isnan(x));",DV=Je({opSnippet:$V,dtype:"bool"}),OV={kernelName:uo,backendName:"webgl",kernelFunc:DV},zV="return float(a < b);",PV=`
|
|
return vec4(lessThan(a, b));
|
|
`,LV=Jt({opSnippet:zV,packedOpSnippet:PV,cpuKernelImpl:jz,dtype:"bool"}),WV={kernelName:co,backendName:"webgl",kernelFunc:LV},BV="return float(a <= b);",VV=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,UV=Jt({opSnippet:BV,packedOpSnippet:VV,dtype:"bool"}),HV={kernelName:ho,backendName:"webgl",kernelFunc:UV};function jV(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Gz(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var GV={kernelName:Oh,backendName:"webgl",kernelFunc:jV},qV=`if (x < 0.0) return NAN;
|
|
return log(x);`,XV=`
|
|
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;
|
|
`,KV=Je({opSnippet:qV,packedOpSnippet:XV,cpuKernelImpl:qz}),ZV={kernelName:ms,backendName:"webgl",kernelFunc:KV},JV="return log(1.0 + x);",YV=Je({opSnippet:JV}),QV={kernelName:po,backendName:"webgl",kernelFunc:YV},eU="return float(a >= 1.0 && b >= 1.0);",tU=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,nU=Jt({opSnippet:eU,packedOpSnippet:tU,dtype:"bool"}),rU={kernelName:fo,backendName:"webgl",kernelFunc:nU},aU="return float(!(x >= 1.0));",sU=Je({opSnippet:aU}),iU={kernelName:cu,backendName:"webgl",kernelFunc:sU},oU="return float(a >= 1.0 || b >= 1.0);",lU=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,uU=Jt({opSnippet:oU,packedOpSnippet:lU,dtype:"bool"}),cU={kernelName:hu,backendName:"webgl",kernelFunc:uU},hU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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 * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},dU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,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);
|
|
}
|
|
`}},pU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=Q().getBool("WEBGL_PACK_NORMALIZATION")?new dU(a.shape,s,i,o,l):new hU(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},fU={kernelName:du,backendName:"webgl",kernelFunc:pU},mU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,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 = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${r}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${r})
|
|
* float(${a})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${a});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},AU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new mU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},yU={kernelName:zh,backendName:"webgl",kernelFunc:AU};function gU(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=ge({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=di(i,e.dtype,"max",r),l=ge({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function P_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,b=new Array(o);for(let _=0;_<b.length;_++)b[_]=a.shape[u[_]];let x=FA(g,a.shape,a.dtype,u,b);p=n.makeTensorInfo(b,a.dtype);let w=n.texData.get(p.dataId);w.values=x}else p=cp(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=C.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,b=Xz(g,k.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let x=n.texData.get(y.dataId);x.values=b}else y=gU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var xU={kernelName:As,backendName:"webgl",kernelFunc:P_},wU=t_+`
|
|
return max(a, b);
|
|
`,_U=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+lp+`
|
|
return result;
|
|
`,bU=Jt({opSnippet:wU,packedOpSnippet:_U,cpuKernelImpl:Kz}),vU={kernelName:ys,backendName:"webgl",kernelFunc:bU};function kU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ml(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;k.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Cn({inputs:{x:a},backend:n});let h=new hc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var IU={kernelName:gs,backendName:"webgl",kernelFunc:kU};function NU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new DA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var SU={kernelName:pu,backendName:"webgl",kernelFunc:NU},TU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*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 < ${a};
|
|
wR += ${r}) {
|
|
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 * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},EU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${h}, ${d});
|
|
|
|
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 += ${a}) {
|
|
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 < ${c};
|
|
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(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${p} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function CU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new DA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new EU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var RU={kernelName:Lh,backendName:"webgl",kernelFunc:CU};function FU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;ml([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new hc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new TU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var MU={kernelName:Ph,backendName:"webgl",kernelFunc:FU};function $U(e,t,n,r){let a=new hc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new hc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var DU={kernelName:Wh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];k.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,d]=$U(r,o,u,l);return[h,d]}};function OU(e,t,n,r){let a=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/a,i=ge({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=di(i,"float32","mean",r),l=ge({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var zU={kernelName:xs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let b=i.texData.get(f.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[u[N]];let w=FA(b,r.shape,r.dtype,u,x);f=i.makeTensorInfo(x,r.dtype);let _=i.texData.get(f.dataId);_.values=w}else f=cp(r,u,i);p.push(f),c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=C.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=C.expandShapeToKeepDim(m,l));let g=OU(f,A,y,i);for(let b of p)i.disposeIntermediateTensorInfo(b);return g}};function PU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=k.sizeFromShape(p),m=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=di(m,m.dtype,"min",n),y;if(i){let g=C.expandShapeToKeepDim(d,l);y=ge({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ge({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var LU={kernelName:ws,backendName:"webgl",kernelFunc:PU},WU=t_+`
|
|
return min(a, b);
|
|
`,BU=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+lp+`
|
|
return result;
|
|
`,VU=Jt({opSnippet:WU,packedOpSnippet:BU,cpuKernelImpl:Zz}),UU={kernelName:_s,backendName:"webgl",kernelFunc:VU},HU=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ht(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===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=`
|
|
${a} start = ${a}(${s});
|
|
${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outC = getOutputCoords();
|
|
for (int i = 0; i < ${r}; 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};
|
|
}
|
|
}
|
|
${a} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},jU=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ht(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=un("rc",r),l=un("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
|
|
${a} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${h};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${h};
|
|
}
|
|
source -= start;
|
|
`;d=`
|
|
${a} rc = outputLoc;
|
|
${p}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${o[r-1]} += 1;
|
|
if(${c}) {
|
|
${p}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}else{let p=`
|
|
${a} source = rc;
|
|
${a} lt = ${a}(lessThan(source, start));
|
|
${a} gte = ${a}(greaterThanEqual(source, end));
|
|
${a} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${h}) +
|
|
gte * ((end - 1) * 2 - source + ${h});
|
|
source -= start;
|
|
`;d=`
|
|
${a} rc = outputLoc;
|
|
${p}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${o[r-1]} += 1;
|
|
if(${c}) {
|
|
${p}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${o[r-2]} += 1;
|
|
if(${o[r-2]} < ${this.outputShape[r-2]}) {
|
|
${p}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${o[r-1]} += 1;
|
|
if(${c}) {
|
|
${p}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${a} start = ${a}(${s});
|
|
const ${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}},GU=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jU(r.shape,a,s):new HU(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},qU={kernelName:fu,backendName:"webgl",kernelFunc:GU},XU=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,KU=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+lp+`
|
|
return result;
|
|
`,ZU=Jt({opSnippet:XU,packedOpSnippet:KU}),JU={kernelName:mo,backendName:"webgl",kernelFunc:ZU},YU=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)}}},QU=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,eH=`
|
|
// 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;
|
|
`,L_=Jt({opSnippet:QU,packedOpSnippet:eH,checkOutOfBounds:!0}),tH={kernelName:os,backendName:"webgl",kernelFunc:L_},W_="return a - b;",B_=Jt({opSnippet:W_,packedOpSnippet:W_,supportsComplex:!0,cpuKernelImpl:aP}),nH={kernelName:Ls,backendName:"webgl",kernelFunc:B_};function V_(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=k.parseAxisParam([s],a.shape),o=P_({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=ge({inputs:{x:o},backend:n,attrs:{shape:l}}),u=B_({inputs:{a,b:c},backend:n}),h=M_({inputs:{x:u},backend:n}),d=$A({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=ge({inputs:{x:d},backend:n,attrs:{shape:l}}),f=L_({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var rH={kernelName:zs,backendName:"webgl",kernelFunc:V_};function aH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:V_({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new YU(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var sH={kernelName:Bh,backendName:"webgl",kernelFunc:aH},U_="return -x;";function iH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=Yz(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new _l(r.shape,U_):a=new Fa(r.shape,U_),n.runWebGLProgram(a,[r],r.dtype)}var oH={kernelName:Ao,backendName:"webgl",kernelFunc:iH},lH=Mr.nonMaxSuppressionV3Impl;function uH(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=lH(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var cH={kernelName:go,backendName:"webgl",kernelFunc:uH},hH=Mr.nonMaxSuppressionV4Impl;function dH(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=hH(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var pH={kernelName:xo,backendName:"webgl",kernelFunc:dH},fH=Mr.nonMaxSuppressionV5Impl;function mH(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=fH(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var AH={kernelName:wo,backendName:"webgl",kernelFunc:mH},yH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${r}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},gH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=k.sizeFromShape(a.shape),c=new yH(l,s,i,o),u=ge({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=ge({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},xH={kernelName:vs,backendName:"webgl",kernelFunc:gH};function mp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=pc({inputs:{input:r},backend:n}),s=mp({inputs:{x:a},backend:n}),i=fp({inputs:{input:r},backend:n}),o=mp({inputs:{x:i},backend:n}),l=Ma({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return LA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var wH={kernelName:Po,backendName:"webgl",kernelFunc:mp};function H_(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=pc({inputs:{input:r},backend:n}),s=H_({inputs:{x:a},backend:n}),i=fp({inputs:{input:r},backend:n}),o=mp({inputs:{x:i},backend:n}),l=Ma({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return LA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var _H={kernelName:_o,backendName:"webgl",kernelFunc:H_};function bH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return PA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});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 h=PA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=v_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var vH={kernelName:bo,backendName:"webgl",kernelFunc:bH},kH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ht(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(float(${n}));
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${a} start = ${a}(${s});
|
|
${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(float(${n}));
|
|
} else {
|
|
${a} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}},IH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ht(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",r),l=un("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
|
|
if(${c}) {
|
|
`,r===1?"":`}
|
|
rc = outputLoc;
|
|
${o[r-2]} += 1;
|
|
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
|
|
if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
|
|
${h[f]}
|
|
if (${d}) {
|
|
result[${f}] = float(${n});
|
|
} else {
|
|
${a} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`;p+=r===1?"} ":"}}",this.userCode=`
|
|
const ${a} start = ${a}(${s});
|
|
const ${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},j_=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IH(a.shape,s,i):new kH(a.shape,s,i);return n.runWebGLProgram(o,[a],a.dtype)},NH={kernelName:ks,backendName:"webgl",kernelFunc:j_},SH=`
|
|
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);
|
|
`,TH=`
|
|
// 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));
|
|
`+lp+`
|
|
return result;
|
|
`,EH=Jt({opSnippet:SH,packedOpSnippet:TH}),CH={kernelName:Is,backendName:"webgl",kernelFunc:EH};function RH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=k.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),d=a;h!=null&&(d=An({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=Qz(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,u),A=k.sizeFromShape(m),y=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Kh(a.dtype),b=di(y,g,"prod",n);p=ge({inputs:{x:b},backend:n,attrs:{shape:f}}),l.push(y),l.push(b)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,c);p=ge({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var FH={kernelName:vo,backendName:"webgl",kernelFunc:RH},G_=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=eP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},MH={kernelName:mu,backendName:"webgl",kernelFunc:G_},$H="return 1.0 / x;",DH=Je({opSnippet:$H}),OH={kernelName:ko,backendName:"webgl",kernelFunc:DH},zH=Ar+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,PH=`
|
|
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;
|
|
`,LH=Je({opSnippet:zH,packedOpSnippet:PH}),WH={kernelName:Ss,backendName:"webgl",kernelFunc:LH},BH=Ar+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,VH=`
|
|
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;
|
|
`,UH=Je({opSnippet:BH,packedOpSnippet:VH}),HH={kernelName:Es,backendName:"webgl",kernelFunc:UH},jH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${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 = ${h};
|
|
|
|
// 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);
|
|
}
|
|
`}},GH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]},
|
|
${c[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${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 = ${h};
|
|
|
|
// 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 qH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new GH(a.shape,l,c,s,i):new jH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var XH={kernelName:Ts,backendName:"webgl",kernelFunc:qH},KH=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${h});
|
|
const float invWidthScale = float(${d});
|
|
|
|
const int winHeight = int(${p});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${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), ${r-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), ${a-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 ZH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new KH(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var JH={kernelName:Hh,backendName:"webgl",kernelFunc:ZH},YH=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${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 = ${d};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new YH(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var ej={kernelName:Au,backendName:"webgl",kernelFunc:QH},tj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${h});
|
|
const float invWidthScale = float(${d});
|
|
|
|
const int winHeight = int(${p});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${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(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function nj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new tj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var rj={kernelName:Uh,backendName:"webgl",kernelFunc:nj},aj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ht(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${a}));
|
|
}
|
|
`}},sj=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-${n} tensor is not yet supported`);this.outputShape=e;let r=un("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ht(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${a}){
|
|
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 = ${o(r.slice())};
|
|
if(${a}){
|
|
result.g = ${l(r.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${c(r.slice())};
|
|
if(${a}) {
|
|
result.a = ${u(r.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function ij(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=k.parseAxisParam(s,a.shape);if(i===0)return Cn({inputs:{x:a},backend:n});let l=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sj(a.shape,o):new aj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var oj={kernelName:Cs,backendName:"webgl",kernelFunc:ij},lj=class{constructor(e,t,n,r){this.variableNames=["Image"],this.outputShape=[];let a=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=C.getImageCenter(r,a,s),u=l.toFixed(3),h=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
|
|
vec3 fill = vec3(${n.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - ${u}) * ${o} - (float(y) - ${h}) * ${i};
|
|
float coordYFloat = (float(x) - ${u}) * ${i} + (float(y) - ${h}) * ${o};
|
|
int coordX = int(round(coordXFloat + ${u}));
|
|
int coordY = int(round(coordYFloat + ${h}));
|
|
${d}
|
|
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${a}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},uj={kernelName:Lo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new lj(r.shape,a,s,i);return o.runWebGLProgram(l,[r],r.dtype)}},cj=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,hj=Je({opSnippet:cj}),dj={kernelName:Rs,backendName:"webgl",kernelFunc:hj},pj="return inversesqrt(x);",fj=Je({opSnippet:pj,cpuKernelImpl:tP}),mj={kernelName:Fs,backendName:"webgl",kernelFunc:fj},q_=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ht(a.length),l=ht(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${a});
|
|
|
|
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 * ${p};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${d};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function Aj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=ge({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=ge({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new q_(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=ge({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var yj={kernelName:No,backendName:"webgl",kernelFunc:Aj},gj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ht(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${r});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${a}));
|
|
} else {
|
|
setOutput(getB(${a}));
|
|
}
|
|
}
|
|
`}};function xj(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new gj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],Yn(a.dtype,s.dtype))}var wj={kernelName:So,backendName:"webgl",kernelFunc:xj},_j=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${C.SELU_SCALEALPHA};
|
|
float scale = ${C.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,bj=Je({opSnippet:_j}),vj={kernelName:To,backendName:"webgl",kernelFunc:bj},kj="return 1.0 / (1.0 + exp(-1.0 * x));",Ij=Je({opSnippet:kj}),Nj={kernelName:$s,backendName:"webgl",kernelFunc:Ij},Sj=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Tj=Je({opSnippet:Sj}),Ej={kernelName:Ro,backendName:"webgl",kernelFunc:Tj},Cj=i_+`
|
|
return sin(x);
|
|
`,Rj=Je({opSnippet:Cj}),Fj={kernelName:Ms,backendName:"webgl",kernelFunc:Rj},Mj=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,$j=Je({opSnippet:Mj}),Dj={kernelName:Co,backendName:"webgl",kernelFunc:$j},Oj=`
|
|
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;
|
|
`,zj=Je({opSnippet:Oj}),Pj={kernelName:Fo,backendName:"webgl",kernelFunc:zj},Lj=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;k.assert(a.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<a.shape.length;++y)l.push([0,0]);let c=[],u=j_({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(u.shape,s,o,!1),d=C.getPermuted(h.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),f=ge({inputs:{x:u},backend:n,attrs:{shape:h}}),m=An({inputs:{x:f},backend:n,attrs:{perm:d}}),A=ge({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},Wj={kernelName:yu,backendName:"webgl",kernelFunc:Lj};function Bj(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new q_(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=ge({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var Vj={kernelName:jh,backendName:"webgl",kernelFunc:Bj};function Uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=k.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=dc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var Hj={kernelName:Mo,backendName:"webgl",kernelFunc:Uj},jj="return sqrt(x);",Gj=Je({opSnippet:jj}),qj={kernelName:Ds,backendName:"webgl",kernelFunc:Gj},Xj="return x * x;",Kj=Je({opSnippet:Xj}),Zj={kernelName:gu,backendName:"webgl",kernelFunc:Kj},X_="return (a - b) * (a - b);",Jj=Jt({opSnippet:X_,packedOpSnippet:X_}),Yj={kernelName:Ps,backendName:"webgl",kernelFunc:Jj};function Qj({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Ar+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Fa(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var eG={kernelName:ga,backendName:"webgl",kernelFunc:Qj},tG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ht(n.length),s=ht(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${a} begin = ${a}(${e});
|
|
${a} strides = ${a}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function nG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=an.sliceInfo(a.shape,s,i,o,l,c,u,h,d),b=ge({inputs:{x:a},backend:n,attrs:{shape:y}}),x;if(p){let _=dc({inputs:{x:b},backend:n,attrs:{begin:f,size:A}});x=ge({inputs:{x:_},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(_)}else if(g.some(_=>_===0))x=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([b])){let _=n.texData.get(b.dataId).values,N=We(b.shape,b.dtype,_),T=rP(g,N,m,f);x=n.makeTensorInfo(g,b.dtype,T.values)}else{let _=new tG(f,m,g);x=n.runWebGLProgram(_,[b],b.dtype)}let w=ge({inputs:{x},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(x),w}var rG={kernelName:$o,backendName:"webgl",kernelFunc:nG},aG="return tan(x);",sG=Je({opSnippet:aG}),iG={kernelName:Do,backendName:"webgl",kernelFunc:sG},oG=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,lG=Je({opSnippet:oG}),uG={kernelName:Ws,backendName:"webgl",kernelFunc:lG},hG=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 r=ht(this.rank),a=cG(e);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function cG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function K_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>k.decodeString(u)),l=We(a.shape,a.dtype,o),c=sP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new hG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var dG={kernelName:ya,backendName:"webgl",kernelFunc:K_};function pG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=iP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var fG={kernelName:Oo,backendName:"webgl",kernelFunc:pG};function mG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;ml(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=oP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var AG={kernelName:Gh,backendName:"webgl",kernelFunc:mG};function yG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=dc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=ge({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var gG={kernelName:zo,backendName:"webgl",kernelFunc:yG},xG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
|
|
sumValue += dot(values, segFilter);
|
|
`,d="";a%n>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return initializationValue;
|
|
}
|
|
`);let p="";a%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${p}
|
|
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 < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${h}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${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
|
|
);
|
|
|
|
${h}
|
|
} else if (${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
|
|
);
|
|
|
|
${h}
|
|
} else if (${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
|
|
);
|
|
|
|
${h}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function wG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=C.getAxesPermutation([c],o),h=a;u!=null&&(h=An({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(h.shape,c,i),p=k.sizeFromShape([h.shape[c]]),f=ge({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=Kh(a.dtype),A=(x,w,_,N,T)=>{let E=x.shape[0],M=x.shape[1],z=C.segment_util.segOpComputeOptimalWindowSize(M,T),P={windowSize:z,inSize:M,batchSize:E,numSegments:T},B=new xG(P,w),G=n.compileAndRun(B,[x,_],N);if(l.push(G),G.shape[1]===T)return G;let V=G_({backend:n,attrs:{start:0,stop:T,step:1,dtype:"float32"}}),K=K_({inputs:{x:V},backend:n,attrs:{reps:[M/z]}});return l.push(V),l.push(K),A(G,w,K,N,T)},y=A(f,"unsortedSegmentSum",s,m,i),g=ge({inputs:{x:y},backend:n,attrs:{shape:d}}),b=g;if(u!=null){l.push(g);let x=C.getUndoAxesPermutation(u);b=An({inputs:{x:b},backend:n,attrs:{perm:x}})}return l.forEach(x=>n.disposeIntermediateTensorInfo(x)),b}var _G={kernelName:xu,backendName:"webgl",kernelFunc:wG},bG=[fU,yU,nL,aL,oL,cL,dL,mL,yL,xL,vL,IL,TL,RL,PL,$L,BL,jL,UL,KL,JL,QL,rW,cW,dW,gW,wW,kW,SW,zP,RW,BW,UW,DW,qW,KW,jW,YW,tB,aB,iB,lB,hB,yB,xB,pB,bB,IB,EB,MB,zB,WB,BB,VB,HB,GB,XB,ZB,YB,nV,iV,lV,cV,pV,yV,_V,IV,OP,SV,CW,CV,MV,OV,LP,WV,HV,GV,QV,ZV,rU,iU,cU,xU,SU,IU,RU,MU,DU,vU,zU,LU,UU,qU,JU,sH,HP,oH,cH,pH,AH,fW,xH,_H,vH,NH,CH,BP,FH,MH,mW,tH,OH,HH,WH,GP,XH,JH,ej,rj,oj,uj,dj,mj,yj,wj,vj,Nj,Ej,Fj,Dj,lW,rH,Pj,Wj,Vj,Hj,qj,Zj,Yj,eG,rG,nH,QP,iG,uG,dG,fG,eL,AG,gG,_G,wH];for(let e of bG)Wo(e);var Rn;(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"})(Rn||(Rn={}));var fc;(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"})(fc||(fc={}));var Z_;function vG(e){Z_=e.wasm.cwrap(Vs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function kG(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,f=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 ${T.shape.length}.`);f=T.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=fc[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],b=a.shape[0],x=n.makeOutput([b,y,g],a.dtype),w=n.dataIdMap.get(x.dataId).id,_=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return Z_(d,_,a.shape.length,p,N,s.shape.length,l,c,A,f,m,h||0,w),x}var IG={kernelName:Vs,backendName:"wasm",setupFunc:vG,kernelFunc:kG};function yn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var NG=yn(Wi);function cn(e,t,n){let r;function a(i){r=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:c,b:u}=l,h=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,p=n!=null?n:c.dtype,f=C.assertAndGetBroadcastShape(c.shape,u.shape),m=o.makeOutput(f,p);if(k.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(m.dataId).id,b=()=>r(h,A,c.shape.length,d,y,u.shape.length,Rn[c.dtype],g);if(t&&c.dtype==="float32")return b(),m;let x=C.getBroadcastDims(c.shape,f),w=C.getBroadcastDims(u.shape,f),_=x.every((T,E)=>T===E),N=w.every((T,E)=>T===E);if(_&&N)return b(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var SG=!0,TG=cn(ma,SG),J_;function EG(e){J_=e.wasm.cwrap(Za,null,["array","number","number","number"])}function CG(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return J_(s,a.length,Rn[r.dtype],i),r}var RG={kernelName:Za,backendName:"wasm",setupFunc:EG,kernelFunc:CG};function Ap(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var FG={kernelName:ps,backendName:"wasm",kernelFunc:Ap},Y_;function MG(e){Y_=e.wasm.cwrap(Bs,null,["number","array","number","number","number","array","number"])}function yp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=DG(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=$G(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=Ap({inputs:t,backend:n});return f.shape=o,f}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return Y_(u,p,l.shape.length,Rn[l.dtype],h,d,s.length),c}function $G(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function DG(e,t){let n=[],r=[];for(let a=0;a<e.length;++a)e[a]!==1&&n.push(e[a]),e[t[a]]!==1&&r.push(t[a]);for(let a=0;a<r.length;++a){let s=-1;for(let i=0;i<r.length;++i)r[i]>=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var OG={kernelName:Bs,backendName:"wasm",kernelFunc:yp,setupFunc:MG};function Il(e,t,n){let r=e.shape,a=e.shape.length,s=k.parseAxisParam(t,r),i=s,o=C.getAxesPermutation(i,a),l=null,c=!1;if(o!=null){let u=new Array(a);for(let d=0;d<u.length;d++)u[d]=r[o[d]];i=C.getInnerMostAxes(i.length,a),l=yp({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var Q_;function zG(e){Q_=e.wasm.cwrap(Ja,null,["number","number","number","number","number"])}function PG(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:h}=Il(s,a,t);if(h){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),f=t.dataIdMap.get(p.dataId).id,m=k.sizeFromShape(p.shape),A=l.shape[u[0]];return Q_(o,Rn[l.dtype],m,A,f),h&&t.disposeData(c.dataId),p}var LG={kernelName:Ja,backendName:"wasm",kernelFunc:PG,setupFunc:zG},eb;function WG(e){eb=e.wasm.cwrap(Ya,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function BG(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=C.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=u.strideWidth,b=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let x=r.makeOutput(u.outShape,"float32"),w=r.dataIdMap.get(x.dataId).id;return eb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,b,w),x}var VG={kernelName:Ya,backendName:"wasm",setupFunc:WG,kernelFunc:BG};function yr(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=k.sizeFromShape(r.shape),i=k.inferFromImplicitShape(a,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. 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Please use 'channelsLast'.`);let z=r.makeOutput(p.outShape,"float32"),P=r.dataIdMap.get(z.dataId).id;return ub(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,b,M,x,w,_,N,T,E,P),z}var yq={kernelName:is,backendName:"wasm",setupFunc:mq,kernelFunc:Aq},gq=!1,xq=cn(eo,gq,"bool"),wq=yn(ls);function BA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),yr({inputs:{x:a},backend:r,attrs:{shape:o}})}var _q={kernelName:to,backendName:"wasm",kernelFunc:BA};function bq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var vq={kernelName:uu,backendName:"wasm",kernelFunc:bq},cb;function kq(e){cb=e.wasm.cwrap(ro,null,["number","number","number","number","number","number"])}function Iq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return cb(s,o,l,c,u,i),a}var Nq={kernelName:ro,backendName:"wasm",kernelFunc:Iq,setupFunc:kq},Sq=yn(us),Tq=!1,Eq=cn(cs,Tq),hb;function Cq(e){hb=e.wasm.cwrap(hs,null,["number","number","number","number","number","number","number"])}function Rq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return hb(u,h,d,p,f,a,A),m}var Fq={kernelName:hs,backendName:"wasm",setupFunc:Cq,kernelFunc:Rq},db;function Mq(e){db=e.wasm.cwrap(Us,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 $q(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d),A=fc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,b=m.outChannels,x=0;if(i!=null){let ne=r.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==b)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${b})`);x=ne.id}let w=m.filterHeight,_=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,z=m.dilationHeight,P=m.dilationWidth,B=m.strideHeight,G=m.strideWidth,V=m.inChannels,K=m.padInfo.type==="SAME"?1:0,X=m.batchSize,ee=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),Y=r.dataIdMap.get(ae.dataId).id,ue=o==null?0:r.dataIdMap.get(o.dataId).id;return db(y,X,ee,J,g,w,_,x,N,T,E,M,K,z,P,B,G,V,b,A,ue,f||0,Y),ae}var Dq={kernelName:Us,backendName:"wasm",setupFunc:Mq,kernelFunc:$q},pb;function Oq(e){pb=e.wasm.cwrap(Hs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),A=fc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,b=m.outChannels,x=0;if(i!=null){let ne=r.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==b)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${b})`);x=ne.id}let w=m.filterHeight,_=m.filterWidth,N=m.padInfo.top,T=m.padInfo.right,E=m.padInfo.bottom,M=m.padInfo.left,z=m.dilationHeight,P=m.dilationWidth,B=m.strideHeight,G=m.strideWidth,V=m.inChannels,K=m.padInfo.type==="SAME"?1:0,X=m.batchSize,ee=m.inHeight,J=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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TX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=bb(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=VA(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var EX={kernelName:xo,backendName:"wasm",setupFunc:SX,kernelFunc:TX},vb;function CX(e){vb=e.wasm.cwrap(wo,"number",["number","number","number","number","number","number"])}function RX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=vb(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=VA(t,d);t.wasm._free(A);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[y,g]}var FX={kernelName:wo,backendName:"wasm",setupFunc:CX,kernelFunc:RX},MX=!1,$X=cn(yo,MX,"bool"),kb;function DX(e){kb=e.wasm.cwrap(vs,null,["number","number","number","number","number"])}function OX(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(a.dataId).id;return kb(u,s,i,o,c),l}var zX={kernelName:vs,backendName:"wasm",setupFunc:DX,kernelFunc:OX};function PX(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var LX={kernelName:_o,backendName:"wasm",kernelFunc:PX};function WX(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return BA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=t.map(l=>BA({inputs:{input:l},backend:n,attrs:{dim:a}}));return rb({inputs:o,backend:n,attrs:{axis:a}})}var BX={kernelName:bo,backendName:"wasm",kernelFunc:WX},Ib;function VX(e){Ib=e.wasm.cwrap(ks,null,["number","array","number","number","array","array","number","number"])}function UX(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return Ib(i,c,t.shape.length,Rn[t.dtype],d,p,a,l),o}var HX={kernelName:ks,backendName:"wasm",kernelFunc:UX,setupFunc:VX},jX=!1,GX=cn(Is,jX),Nb;function qX(e){Nb=e.wasm.cwrap(Ns,null,["number","number","number"])}function XX(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return Nb(s,i,l),o}var KX={kernelName:Ns,backendName:"wasm",setupFunc:qX,kernelFunc:XX},Sb;function ZX(e){Sb=e.wasm.cwrap(vo,null,["number","number","number","number"])}function JX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=Il(i,a,t),f=h;if(p){let b=t.dataIdMap.get(u.dataId).id;b!==o&&(c=u,l=b,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=k.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(k.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(g.dataId).id;Sb(l,y,Rn[g.dtype],b)}if(p&&t.disposeData(u.dataId),s){let b=C.expandShapeToKeepDim(g.shape,d);g.shape=b}return g}var YX={kernelName:vo,backendName:"wasm",setupFunc:ZX,kernelFunc:JX},QX=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=cA(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},eK={kernelName:mu,backendName:"wasm",kernelFunc:QX},tK=!0,nK=cn(os,tK),rK=yn(Ss),aK=yn(Es),Tb;function sK(e){Tb=e.wasm.cwrap(Ts,null,["number","number","number","number","number","number","number","number","number","number"])}function iK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,[u,h,d,p]=a.shape,f=[u,l,c,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=gp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(k.sizeFromShape(a.shape)===0)return g;let b=t.dataIdMap.get(g.dataId).id;return Tb(y,u,h,d,p,l,c,s?1:0,i?1:0,b),A!=null&&t.disposeData(A.dataId),g}var oK={kernelName:Ts,backendName:"wasm",setupFunc:sK,kernelFunc:iK},Eb;function 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2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new W(`${r}: Improper config format: ${JSON.stringify(s)}.
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1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
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2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let c={};for(let p of Object.keys(ir))c[p]=ir[p];for(let p of Object.keys(n))c[p]=n[p];let u=s.config;u.customObjects=c;let h=Object.assign({},ir);for(let p of Object.keys(n))ir[p]=n[p];qA(s.config);let d=l(o,s.config,n,a);return ir=Object.assign({},h),d}else{let c=Object.assign({},ir);for(let h of Object.keys(n))ir[h]=n[h];let u=new o(s.config);return ir=Object.assign({},c),u}}}function wY(e,t){return e<t?-1:e>t?1:0}function _p(e,t){return-1*wY(e,t)}function $a(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function _Y(e){if(e==null)throw new W(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function mi(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new W(`${n} is not a valid ${t}. 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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,b=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(b),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let g=y.sourceLayer,b=y.nodeIndex,x=y.tensorIndex;Br(b===0,"input layer has >1 nodes"),Br(x===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(b),this.inputLayersTensorIndices.push(x)}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 Tl))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={},r={},a={},s={},i=[],o=(y,g,b,x,w,_)=>{(x==null||w==null||_==null)&&(x=y.sourceLayer,w=y.nodeIndex,_=y.tensorIndex);let N=x.inboundNodes[w];if(b.indexOf(N)!==-1)throw new xr(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(Hr.nodeKey(x,w)),x.id in s||(s[x.id]=Object.keys(s).length),b.indexOf(N)===-1&&b.push(N);let T=N.inboundLayers.length;for(let E=0;E<T;E++){let M=N.inputTensors[E],z=N.inboundLayers[E],P=N.nodeIndices[E],B=N.tensorIndices[E];o(M,g,b,z,P,B)}for(g.push(N);b.indexOf(N)>=0;)b.splice(b.indexOf(N),1);i.push(N)},l=[],c=[];for(let y of this.outputs)o(y,l,c);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],b=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,b),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let x=0;x<y.inboundLayers.length;x++){let w=y.inboundLayers[x],_=y.nodeIndices[x],N=w.inboundNodes[_],T=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,T),n[N.id]=N}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let d={};for(let y in r){let g=r[y];g in d||(d[g]=[]),d[g].push(a[y])}let p=Object.keys(d).map(y=>parseInt(y,10)).sort(_p);this.layers=[];for(let y of p){let g=d[y];g.sort((b,x)=>{let w=s[b.id],_=s[x.id];return w<_?-1:w>_?1:0});for(let b of g)b instanceof Hr&&this.internalContainerRefs.push(b),this.layers.push(b)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(_p);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let b=g.outboundLayer;if(b!=null){for(let x of g.inputTensors)if(f.indexOf(x)===-1)throw new xr(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${b.name}". 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Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new W("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new W(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];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)a.push([n[i],e[s]]);else if(t)throw new W(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new W(`${s.length} of ${r} weights are not set: ${s}`)}Ay(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${gm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=vy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return U(()=>{e=mt(e);let n=new xi;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Tc(this.outputs,n,t)})}computeMask(e,t){return U(()=>{e=mt(e);let n;return t==null?n=pi(null,e.length):n=mt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Mp(e);if(t.length!==this.inputLayers.length)throw new W(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(_p);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,b=n[g];u.push(b)}let h=c.computeOutputShape(gn(u)),d=Mp(h),p=c.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${c.name}_${p}_${f}`;n[m]=d[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Br(o in n),a.push(n[o])}return gn(a)}runInternalGraph(e,t){t==null&&(t=pi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(_p);for(let o of r){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,h=c.inputTensors,d=c.outputTensors,p=new Array;for(let f of h)f.id in n&&p.push(n[f.id]);if(p.length===h.length){let f={},m,A,y,g;if(c.callArgs!=null&&(f=c.callArgs),p.length===1){let[b,x]=p[0];f.mask==null&&(f.mask=x),y=mt(u.call(b,f)),g=mt(u.computeMask(b,x)),m=[b],A=[x]}else m=p.map(b=>b[0]),A=p.map(b=>b[1]),f.mask==null&&(f.mask=A),y=mt(u.call(m,f)),g=mt(u.computeMask(m,A));if(u.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let b=0;b<d.length;++b){let x=d[b],w=y[b],_=g[b];n[x.id]=[w,_]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Br(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),a.push(l),s.push(c)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof Hr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=Hr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new W(`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 W("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new W(`No such layer: ${e}`)}calculateLosses(){return U(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Hr.nodeKey(t,n);this.containerNodes.has(r)&&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 h=s.inboundNodes[u],d=Hr.nodeKey(s,u),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let A=h.inboundLayers[m],y=h.nodeIndices[m],g=h.tensorIndices[m],b=Hr.nodeKey(A,y),x=t[b];x==null&&(x=0),f.push([A.name,x,g,p])}l.push(f)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Hr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];r.push([i.name,c,u])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Hr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];a.push([i.name,c,u])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(m,A){m.name in s?s[m.name].push(A):s[m.name]=[A]}function o(m,A){let y=[],g;for(let b of A){let x=b[0],w=b[1],_=b[2];if(g=b[3]==null?{}:b[3],!(x in a)){i(m,A);return}let N=a[x];if(N.inboundNodes.length<=w){i(m,A);return}let T=N.inboundNodes[w];y.push(T.outputTensors[_])}y.length>0&&m.apply(gn(y),g)}function l(m){let A=m.name,y=_r(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new W(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!_Y(s);)for(let m of u){let A=a[m.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],d=[],p=t.inputLayers;for(let m of p){let A=m[0],y=m[1],g=m[2];Br(A in a);let b=a[A].inboundNodes[y].outputTensors;h.push(b[g])}let f=t.outputLayers;for(let m of f){let A=m[0],y=m[1],g=m[2];Br(A in a);let b=a[A].inboundNodes[y].outputTensors;d.push(b[g])}return new e({inputs:h,outputs:d,name:c})}get stateful(){if(this._stateful)throw new W("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){U(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function Zee(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function D3(e,t){return Zee(e,t,"classWeight")}async function O3(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=U(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 a.data());Fe(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),en(i,"float32")}else return null}function Jee(e,t){return L(e,t)}var Yee=32;function P3(e,t){let n,r,a=t;n=a.xs,r=a.ys,k.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=z3("input",e.inputNames,n),i=z3("output",e.outputNames,r),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (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 z3(e,t,n){if(n instanceof et)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 r=[];for(let a of t){if(n[a]==null)throw new W(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Qee(e){if(e.length===3)throw new De("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function tte(e,t,n){let r=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(!r||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 a=n.validationData!=null,s,i;if(a)if(L3(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=Qee(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=v3(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=k3(u,h,n.epochs,null,null,ete(t,n),null,a,c);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await d.onEpochBegin(f);let y=0,g=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let b=await m.next();if(r&&b.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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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new xi(c),h=Tc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Jee(f,a[p]));let m=bt(f);t.push(m),p===0?d=f:d=oe(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=bt(m(r[A],h[A]))}Vt(f),s.push(f)}return d=bt(d),this.calculateLosses().forEach(p=>{d=oe(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>U(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new xi(s),o=Tc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=bt(c(a[l],o[l]));l===0?n=u:n=oe(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=bt(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return ste(this,e,t,n)}async fitDataset(e,t){return tte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Fe(s),gn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Jh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Jh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ia(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ia(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ia(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ia(Vp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ia(Vp(e)));{let e={};for(let t in this.metrics)e[t]=ia(Vp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Sc(e.optimizer_config),n=_r(t),r;if(typeof e.loss=="string")r=fi(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>fi(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=fi(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>fi(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=fi(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=pn.getSaveHandlers(e);if(i.length===0)throw new W(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new W(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new W("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await pn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:cte,generatedBy:`TensorFlow.js tfjs-layers v${gm}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await pn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=pn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;R3(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){R3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ea.className="Model";re.registerClass(ea);var H3=class extends ea{};H3.className="Functional";re.registerClass(H3);async function hte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Sc(n),a=_r(r,t);if(e.weightsManifest!=null){let s=await pn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),Fe(s)}return a}async function pte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=pn.getLoadHandlers(e,t);if(n.length===0)n.push(pn.browserHTTPRequest(e,t));else if(n.length>1)throw new W(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return dte(e,void 0,t)}async function dte(e,t,n){if(n==null&&(n={}),e.load==null)throw new W("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=_r(Sc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new W("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=fte(r.weightData,r.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Fe(c),Fe(u.map(h=>h.tensor))}return o}function fte(e,t){let n=pn.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Qo=class extends ea{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Fp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new W(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Qo||e instanceof ea,n;if(t){if(n=e,n.outputs.length!==1)throw new W("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new W("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new W("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=w3({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new W(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new W("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=x3(this.outputs[0])}this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[r],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(dt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ea({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new xr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new W("Legacy serialization format not supported yet.");a=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Qo))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=_r(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new W("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new W("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Qo.className="Sequential";re.registerClass(Qo);function W8(e){return new ea(e)}function B8(e){return new Qo(e)}function V8(e,t){return t==null&&(t={}),pte(e,t)}function O0(e){return w3(e)}function U8(e,t){lr.registerCallbackConstructor(e,t)}var Fn=class extends re.Serializable{getConfig(){return{}}},j3=class extends Fn{apply(e,t=1){return ZY(e,t)}};j3.className="elu";re.registerClass(j3);var G3=class extends Fn{apply(e){return Ad(e)}};G3.className="selu";re.registerClass(G3);var q3=class extends Fn{apply(e){return Fr(e)}};q3.className="relu";re.registerClass(q3);var X3=class extends Fn{apply(e){return U(()=>Ko(6,Fr(e)))}};X3.className="relu6";re.registerClass(X3);var K3=class extends Fn{apply(e){return e}};K3.className="linear";re.registerClass(K3);var Z3=class extends Fn{apply(e){return 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xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in a7?a7[e]:e,config:{}};return s7(t)}else return e instanceof r7?e:s7(e)}var Fy=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Pe(e);let n=Fr(e);return this.maxValue!=null&&(n=fn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="ReLU";re.registerClass(Fy);var My=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return Ru(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};My.className="LeakyReLU";re.registerClass(My);var $y=class extends 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="ThresholdedReLU";re.registerClass(Oy);var zy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ey().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(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}};zy.className="Softmax";re.registerClass(zy);function Rl(e,t,n){if(typeof e=="number")return pi(e,t);if(e.length!==t)throw new W(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!GY(a))throw new W(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function br(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function Hp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Oa([n-t,0]);else if(r==="same")e=e*t;else throw new W(`Unsupport padding mode: ${r}.`);return e}function Py(e,t){return U(()=>(Tt(t),t==="channelsFirst"?at(e,[0,2,3,1]):e))}function i7(e,t){return U(()=>(Tt(t),t==="channelsFirst"?at(e,[0,2,3,4,1]):e))}function yte(e,t,n,r=1,a="valid",s,i=1){return U(()=>{if(s==null&&(s=gr()),Tt(s),e.shape.length!==3)throw new W(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new W(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new W(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=at(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=nd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function o7(e,t,n,r=[1,1],a="valid",s,i,o=null){return U(()=>{if(s==null&&(s=gr()),Tt(s),e.rank!==3&&e.rank!==4)throw new W(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new W(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Py(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ka.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=at(l,[0,3,1,2])),l})}function gte(e,t,n,r=[1,1,1],a="valid",s,i){return U(()=>{if(s==null&&(s=gr()),Tt(s),e.rank!==4&&e.rank!==5)throw new W(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new W(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=i7(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=jf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=at(o,[0,4,1,2,3])),o})}var Ly=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Ly.verifyArgs(t),this.rank=e,Gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Rl(t.kernelSize,e,"kernelSize"),this.strides=Rl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Hn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Tt(this.dataFormat),this.activation=La(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Wt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Rl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new W(`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 W(`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 W(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,3))throw new W(`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:Pa(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Rc=class extends Ly{constructor(e,t){super(e,t);this.kernel=null,Rc.verifyArgs(t),this.filters=t.filters,Gt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Wt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,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 U(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=Qb(this.activation.getClassName());if(a!=null&&this.rank===2)n=o7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=yte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=o7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=gte(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=dt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=br(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:pt(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 W(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Fc=class extends Rc{constructor(e){super(2,e);Fc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!XA(e.kernelSize,"number",1,2))throw new W(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Fc.className="Conv2D";re.registerClass(Fc);var jp=class extends Rc{constructor(e){super(3,e);jp.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 W(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};jp.className="Conv3D";re.registerClass(jp);var Wy=class extends Fc{constructor(e){super(e);if(this.inputSpec=[new Ut({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new W(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new W("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new W("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ut({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return U(()=>{let n=Pe(e);if(n.shape.length!==4)throw new W(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Hp(o,h,c,this.padding),f=Hp(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=at(n,[0,2,3,1]));let A=rd(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=at(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=dt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Hp(t[r],o,s,this.padding),t[a]=Hp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Wy.className="Conv2DTranspose";re.registerClass(Wy);var l7=class extends Rc{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 W("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new W("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 W(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Wt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Wt(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length<this.rank+2)throw new W(`Inputs to SeparableConv${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 W(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"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 Ut({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return U(()=>{e=Pe(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=at(e,[0,2,3,1])),n=sm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=at(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=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.pointwiseRegularizer=pt(this.pointwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseConstraint),e.pointwiseConstraint=Lt(this.pointwiseConstraint),e}};l7.className="SeparableConv";var By=class extends l7{constructor(e){super(2,e)}};By.className="SeparableConv2D";re.registerClass(By);var Gp=class extends Rc{constructor(e){super(1,e);Gp.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"&&!XA(e.kernelSize,"number",1,1))throw new W(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Gp.className="Conv1D";re.registerClass(Gp);var Vy=class extends Xe{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 U(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=bp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return bp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=bp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return bp(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}};Vy.className="Cropping2D";re.registerClass(Vy);var Uy=class extends Xe{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,Tt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,UY(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 U(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=at(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return at(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="UpSampling2D";re.registerClass(Uy);function xte(e,t,n=[1,1],r="valid",a,s){return U(()=>{a==null&&(a=gr()),Tt(a);let i=Py(e,a);if(e.rank!==4)throw new W(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new W(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ho(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=at(i,[0,3,1,2])),i})}var Hy=class extends Ly{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Wt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=dt(e),e.length<4)throw new W(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new W(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,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 U(()=>{e=Pe(e);let n=xte(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=br(t,this.kernelSize[0],this.padding,this.strides[0]),s=br(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.depthwiseConstraint=Lt(this.depthwiseRegularizer),e}};Hy.className="DepthwiseConv2D";re.registerClass(Hy);function u7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new W("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function c7(e,t,n,r=!1,a,s,i=!1,o=!1){return U(()=>{let l=t.shape.length;if(l<3)throw new W(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(wr(2,l));if(t=at(t,c),s!=null)throw new De("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."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=kn(a,-1)),a=at(a,c)),r&&(t=Sn(t,0),a!=null&&(a=Sn(a,0)));let u=[],h,d=n,p=t.shape[0],f=nr(t),m;a!=null&&(m=nr(a));for(let y=0;y<p;++y){let g=f[y],b=U(()=>e(g,d));if(a==null)h=b[0],d=b[1];else{let x=U(()=>{let w=m[y],_=Nn(w).sub(w),N=b[0].mul(w).add(d[0].mul(_)),T=d.map((E,M)=>b[1][M].mul(w).add(E.mul(_)));return{output:N,newStates:T}});h=x.output,d=x.newStates}o&&u.push(h)}let A;return o&&(A=Tn(u,1)),[h,A,d]})}var $r=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new W("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new qp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new W("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 Ut({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 wr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){fy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return U(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}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 De("Constants support is not implemented in RNN yet.");fy(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ut({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);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 W(`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 Ut({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){U(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new W("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(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new W(`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()):Fe(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(a.shape,i))throw new W(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Vt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=u7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.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 Ut({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof mr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return U(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new W(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=c7((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return U(()=>{let t=Ct(e.shape);return t=Te(t,[1,2]),t=bc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?ey(t,[1,n]):t):this.cell.stateSize>1?[ey(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$r.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=_r(r,n);return new e(Object.assign(t,{cell:a}))}};$r.className="RNN";re.registerClass($r);var Ic=class extends Xe{},Xp=class extends Ic{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=La(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Sl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Sl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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 U(()=>{if(e=e,e.length!==2)throw new W(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>Nn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>Nn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(L(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=L(n,i));let o=oe(a,Vr(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:Pa(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(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)}};Xp.className="SimpleRNNCell";re.registerClass(Xp);var jy=class extends $r{constructor(e){e.cell=new Xp(e),super(e)}call(e,t){return U(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};jy.className="SimpleRNN";re.registerClass(jy);var Kp=class extends Ic{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 W("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Gt(this.units,"units"),this.activation=La(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=La(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Sl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Sl([1,Oa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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 U(()=>{if(e=e,e.length!==2)throw new W(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>Nn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>Nn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let c=Vr(e,this.kernel.read());this.useBias&&(c=Ur(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=L(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=sn(u,[2*this.units,this.units],u.rank-1),p=Vr(r,h),[f,m,A]=sn(c,3,c.rank-1),[y,g]=sn(p,2,p.rank-1);i=this.recurrentActivation.apply(oe(f,y)),o=this.recurrentActivation.apply(oe(m,g));let b=Vr(L(o,r),d);l=this.activation.apply(oe(A,b));let x=oe(L(i,r),L(oe(1,_t(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Pa(this.activation),recurrentActivation:Pa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(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)}};Kp.className="GRUCell";re.registerClass(Kp);var Gy=class extends $r{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 Kp(e),super(e)}call(e,t){return U(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Gy.className="GRU";re.registerClass(Gy);var Mc=class extends Ic{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=La(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=La(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Wt(e.kernelConstraint),this.recurrentConstraint=Wt(e.recurrentConstraint),this.biasConstraint=Wt(e.biasConstraint),this.dropout=Sl([1,Oa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Sl([1,Oa([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=dt(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 r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends or{apply(i,o){let l=a.apply([s]),c=new kp().apply([s]),u=a.apply([s*2]);return l3(l3(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return U(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new W(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>Nn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>Nn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=L(r,i[0])),h=oe(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=sn(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=oe(L(l,a),L(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=L(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Pa(this.activation),recurrentActivation:Pa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(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)}};Mc.className="LSTMCell";re.registerClass(Mc);var qy=class extends $r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Mc(e),super(e)}call(e,t){return U(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};qy.className="LSTM";re.registerClass(qy);var qp=class extends Ic{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return U(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){fy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ai(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(_r(a,n));return new e({cells:r})}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 my(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}Ay(t)}};qp.className="StackedRNNCells";re.registerClass(qp);function Wa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>c3(t(),n),i=()=>kc(s,t,r);return!a||a<=1?Vt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Vt(o.clone()))}var wte=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},h7=class extends $r{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ut({ndim:5})]}call(e,t){return U(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new W("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}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 U(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){U(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new W("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(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new W(`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()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!k.arraysEqual(i.shape,o))throw new W(`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=>Vt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=br(l,r[0],a,s[0],i[0]),h=br(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};h7.className="ConvRNN2D";var Zp=class extends Mc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Gt(this.filters,"filters"),this.kernelSize=Rl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Gt(o,"kernelSize")),this.strides=Rl(r||1,2,"strides"),this.strides.forEach(o=>Gt(o,"strides")),this.padding=a||"valid",Hn(this.padding),this.dataFormat=s||"channelsLast",Tt(this.dataFormat),this.dilationRate=Rl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Gt(o,"dilationRate"))}build(e){var t;e=dt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new W(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);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,c=this.filters;o=new(t=class extends or{apply(u,h){let d=l.apply([c]),p=Rr([c]),f=l.apply([c*2]);return ny([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return U(()=>{if(e.length!==3)throw new W(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>Nn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(J,ae,Y)=>!ae||!ae[Y]?J:L(ae[Y],J),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>Nn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[b,x,w,_]=sn(this.kernel.read(),i,g),[N,T,E,M]=this.useBias?sn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,b,N,this.padding),u=this.inputConv(u,x,T,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,_,M,this.padding);let[z,P,B,G]=sn(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,z),m=this.recurrentConv(m,P),A=this.recurrentConv(A,B),y=this.recurrentConv(y,G);let V=this.recurrentActivation.apply(oe(c,f)),K=this.recurrentActivation.apply(oe(u,m)),X=oe(L(K,s),L(V,this.activation.apply(oe(h,A)))),ee=L(this.recurrentActivation.apply(oe(d,y)),this.activation.apply(X));return[ee,ee,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=wte(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=Zr(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return Zr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Zp.className="ConvLSTM2DCell";re.registerClass(Zp);var Xy=class extends h7{constructor(e){let t=new Zp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Xy.className="ConvLSTM2D";re.registerClass(Xy);var Jp=class extends Xe{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 r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return U(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return kc(()=>c3(n,this.rate,a,this.seed),()=>n,r)}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()}};Jp.className="Dropout";re.registerClass(Jp);var Ky=class extends Jp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ky.className="SpatialDropout1D";re.registerClass(Ky);var Zy=class extends Xe{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=La(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Wt(e.kernelConstraint),this.biasConstraint=Wt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=dt(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=dt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return U(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=Qb(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Pa(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Lt(this.kernelConstraint),biasConstraint:Lt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Zy.className="Dense";re.registerClass(Zy);var Jy=class extends Xe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=dt(e);for(let t of e.slice(1))if(t==null)throw new W(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Da(e,1)]}call(e,t){return U(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return KY(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Jy.className="Flatten";re.registerClass(Jy);var Yy=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=La(e.activation)}call(e,t){return U(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Pa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Yy.className="Activation";re.registerClass(Yy);var Qy=class extends Xe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return U(()=>(e=Pe(e),qY(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Qy.className="RepeatVector";re.registerClass(Qy);var eg=class extends Xe{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.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new W("Can only specifiy one unknown dimension.");else a*=l}let i=Da(e);if(s!==null){if(a===0||i%a!=0)throw new W(n);r[s]=i/a}else if(i!==a)throw new W(n);return r}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 U(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};eg.className="Reshape";re.registerClass(eg);var tg=class extends Xe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=wr(1,e.dims.length+1);if(!k.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ut({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return at(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};tg.className="Permute";re.registerClass(tg);var ng=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return ku(Xs(n,this.maskValue),r)}call(e,t){return U(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=ku(Xs(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};ng.className="Masking";re.registerClass(ng);var rg=class extends Xe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(mt(e.inputLength))}this.inputDim=e.inputDim,Gt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Gt(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Wt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return U(()=>this.maskZero?(e=Pe(e),Xs(e,He(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=mt(this.inputLength);if(t.length!==e.length-1)throw new W(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new W(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return U(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=_c(n,"int32")),u3(this.embeddings.read(),n.as1D()).reshape(dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:pt(this.embeddingsRegularizer),activityRegularizer:pt(this.activityRegularizer),embeddingsConstraint:Lt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};rg.className="Embedding";re.registerClass(rg);var _i=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new W("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[dt(e)]),e=e,e.length<2)throw new W(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=$a(t),t.length>1)throw new W(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&$a(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return U(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Oa(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=bc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Da(c.slice(1))));d=at(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=wr(1,l).concat([0]);n.push(at(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=at(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(wr(0,i-1));s=at(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=$a(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return U(()=>{if(t==null)return null;if(!Array.isArray(t))throw new W("`mask` should be an Array");if(!Array.isArray(e))throw new W("`inputs` should be an Array");if(t.length!==e.length)throw new W(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:kn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=tr(n,t[r]);return n})}},ag=class extends _i{constructor(e){super(e)}mergeFunction(e){return U(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return t})}};ag.className="Add";re.registerClass(ag);var sg=class extends _i{constructor(e){super(e)}mergeFunction(e){return U(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};sg.className="Multiply";re.registerClass(sg);var ig=class extends _i{constructor(e){super(e)}mergeFunction(e){return U(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return L(1/e.length,t)})}};ig.className="Average";re.registerClass(ig);var og=class extends _i{constructor(e){super(e)}mergeFunction(e){return U(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Cr(t,e[n]);return t})}};og.className="Maximum";re.registerClass(og);var lg=class extends _i{constructor(e){super(e)}mergeFunction(e){return U(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ko(t,e[n]);return t})}};lg.className="Minimum";re.registerClass(lg);var ug=class extends _i{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new W("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new W("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return U(()=>ny(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new W("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new W("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new W("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new W(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return U(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(Nn(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(kn(t[s],-1)):r.push(t[s]);let a=ct(r,this.axis);return ed(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ug.className="Concatenate";re.registerClass(ug);function $c(e,t){for(;e<0;)e+=t;return e}function _te(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("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 De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return U(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)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,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var cg=class extends _i{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 De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new W(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new W(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>$c(a,e[s].shape.length)):r=[$c(this.axes,t.shape.length),$c(this.axes,n.shape.length)],this.normalize&&(t=Op(t,r[0]),n=Op(n,r[1])),_te(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[$c(this.axes,e.length),$c(this.axes,t.length)],n}computeOutputShape(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].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};cg.className="Dot";re.registerClass(cg);var hg=class extends Xe{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 U(()=>{this.invokeCallHook(e,t);let n=Pe(e);return kc(()=>vp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};hg.className="GaussianNoise";re.registerClass(hg);var dg=class extends Xe{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 U(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?kc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(vp(n.shape,1,r))},()=>n,t.training||!1):n})}};dg.className="GaussianDropout";re.registerClass(dg);var pg=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return U(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return kc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=ba(Zo(n),this.rate);o=_c(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Pe(e),t.training||!1)}return e})}};pg.className="AlphaDropout";re.registerClass(pg);function Dc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=K2(e,t,n,r,a,s);else if(e.rank===3)i=Z2(e,t,n,r,a,s);else if(e.rank===4)i=J2(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function bte(e,t,n,r,a=.001){return U(()=>{let s=hd(e,r),i=s.mean,o=s.variance;return[Dc(e,i,o,n,t,a),i,o]})}function vte(e,t,n,r,a=.001){return U(()=>{let s=hd(e,r),i=s.mean,o=s.variance,l=[];for(let p of wr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Dc(e,c,u,d,h,a),i,o]})}function kte(e,t,n,r,a=.001){return k.arraysEqual(r.slice().sort(),wr(0,e.rank-1))?bte(e,t,n,r,a):vte(e,t,n,r,a)}var fg=class extends Xe{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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Wt(e.betaConstraint),this.gammaConstraint=Wt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=dt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new W(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ut({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return U(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=wr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=pi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,wr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,b=this.scale?this.gamma.read().reshape(l):null;return Dc(r,A,y,g,b,this.epsilon)}else return Dc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=kte(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{U(()=>{let b=1-g,x=A.read(),w=x.sub(y).mul(b);A.write(x.sub(w))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer),betaConstraint:Lt(this.betaConstraint),gammaConstraint:Lt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};fg.className="BatchNormalization";re.registerClass(fg);var mg=class extends Xe{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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==$a(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return U(()=>{let s=!0,{mean:i,variance:o}=hd(n,this.axis,s),l=pi(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Dc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};mg.className="LayerNormalization";re.registerClass(mg);function Ite(e,t,n){return U(()=>{if(e.rank!==4)throw new W(`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 W("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=gr()),n!=="channelsLast"&&n!=="channelsFirst")throw new W(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],Jr(e,r)})}var Ag=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?gr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new W(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new W(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new W(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ut({ndim:4})]}computeOutputShape(e){e=dt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return U(()=>Ite(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ag.className="ZeroPadding2D";re.registerClass(Ag);function Yp(e,t,n,r,a,s){return U(()=>{Tt(a),n3(s),Hn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=gr()),s==null&&(s="max"),e=Py(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Mu(e,t,n,o):i=Nu(e,t,n,o),a==="channelsFirst"&&(i=at(i,[0,3,1,2])),i})}function d7(e,t,n,r,a,s){return U(()=>{Tt(a),n3(s),Hn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=gr()),s==null&&(s="max"),e=i7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=em(e,t,n,o):i=Uf(e,t,n,o),a==="channelsFirst"&&(i=at(i,[0,4,1,2,3])),i})}var p7=class extends Xe{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 W(`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 W(`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,Hn(this.padding),this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){e=dt(e);let t=br(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return U(()=>{this.invokeCallHook(e,t),e=bc(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return va(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},yg=class extends p7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Hn(r),Yp(e,t,n,r,a,"max")}};yg.className="MaxPooling1D";re.registerClass(yg);var gg=class extends p7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Hn(r),Yp(e,t,n,r,a,"avg")}};gg.className="AveragePooling1D";re.registerClass(gg);var f7=class extends Xe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new W(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Gt(this.poolSize,"poolSize"),Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Hn(this.padding),this.inputSpec=[new Ut({ndim:4})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=br(t,this.poolSize[0],this.padding,this.strides[0]),n=br(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 U(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},xg=class extends f7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Hn(r),Yp(e,t,n,r,a,"max")}};xg.className="MaxPooling2D";re.registerClass(xg);var wg=class extends f7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Hn(r),Yp(e,t,n,r,a,"avg")}};wg.className="AveragePooling2D";re.registerClass(wg);var m7=class extends Xe{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 W(`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,Tt(this.dataFormat),Hn(this.padding),this.inputSpec=[new Ut({ndim:5})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=br(t,this.poolSize[0],this.padding,this.strides[0]),n=br(n,this.poolSize[1],this.padding,this.strides[1]),r=br(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return U(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},_g=class extends m7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Hn(r),d7(e,t,n,r,a,"max")}};_g.className="MaxPooling3D";re.registerClass(_g);var bg=class extends m7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Hn(r),d7(e,t,n,r,a,"avg")}};bg.className="AveragePooling3D";re.registerClass(bg);var A7=class extends Xe{constructor(e){super(e);this.inputSpec=[new Ut({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},vg=class extends A7{constructor(e){super(e||{})}call(e,t){return U(()=>{let n=Pe(e);return bt(n,1)})}};vg.className="GlobalAveragePooling1D";re.registerClass(vg);var kg=class extends A7{constructor(e){super(e||{})}call(e,t){return U(()=>{let n=Pe(e);return Wn(n,1)})}};kg.className="GlobalMaxPooling1D";re.registerClass(kg);var y7=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.inputSpec=[new Ut({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ig=class extends y7{call(e,t){return U(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?bt(n,[1,2]):bt(n,[2,3])})}};Ig.className="GlobalAveragePooling2D";re.registerClass(Ig);var Ng=class extends y7{call(e,t){return U(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Wn(n,[1,2]):Wn(n,[2,3])})}};Ng.className="GlobalMaxPooling2D";re.registerClass(Ng);var g7=class extends Xe{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 r=t.layer,a=_r(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Sg=class extends g7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=dt(e),e.length<3)throw new W(`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=dt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return U(()=>(e=Pe(e),c7((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Sg.className="TimeDistributed";re.registerClass(Sg);function Nte(e){mi(VY,"BidirectionalMergeMode",e)}var Ste="concat",Tg=class extends g7{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=_r(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=_r(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Ste:e.mergeMode,Nte(this.mergeMode),e.weights)throw new De("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,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):gn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=u7(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new W("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 c=n.map(u=>new Ut({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof mr;for(let l of s)if(l instanceof mr!==o)throw new W("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return U(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Sn(a,1));let 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t}},pne=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[oe(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[Qh(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[nm(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[L(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[Ne(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[Xf(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[Yh(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[we(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[Ko(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[Cr(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[Yr(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[bd(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
|
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),ur(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,Vt(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,r)=>this.write(n,t[r]))}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 r=0;r<this.size();r++)e.push(r)}if(e.length===0)return pr([],[0].concat(this.elementShape));let n=this.readMany(e);return ur(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Tn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return pr([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return ur(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),ct(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,nr(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,r=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 a=n===0?0:t.size/n,s=[];U(()=>{t=q(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],c=[0,l,0],u=[1,e[o],a];s[o]=q(Me(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},zc=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(a=>{if(n!==a.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${a.dtype}`);ur(t,a.shape,"TensorList shape mismatch: "),Vt(a)}),this.idTensor=Se(0),this.maxNumElements=r,Vt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new zc([...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.`);ur(e,this.elementShape,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,e);return U(()=>{let a=this.tensors.map(s=>q(s,r));return Tn(a,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=Oc(this.elementShape,this.tensors,e),r=this.tensors.pop();return ur(r.shape,e,"TensorList shape mismatch: "),q(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ur(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Vt(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.`);ur(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,t);return q(this.tensors[e],r)}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.`);ur(this.elementShape,t.shape,"TensorList shape mismatch: "),Vt(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}`);ur(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=Oc(this.elementShape,this.tensors,n);return e.length===0?pr([],[0].concat(r)):U(()=>{let a=e.map(s=>q(this.tensors[s],r));return Tn(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);ur(this.elementShape,t,"TensorList shape mismatch: ");let n=Oc(this.elementShape,this.tensors,t);return this.size()===0?pr([],[0].concat(n)):U(()=>{let r=this.tensors.map(a=>q(a,n));return ct(r,0)})}};function Ane(e,t,n){let r=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 a=e.shape.slice(1);ur(a,t,"TensorList shape mismatch: ");let s=nr(e);return new zc(s,t,r)}function yne(e,t,n){return new zc([],e,t,n)}function gne(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(r!=null&&r!==-1&&a>=r)throw new Error(`Max index must be < array size (${a} vs. ${r})`);let s=new zc([],n,e.dtype,r),i=nr(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function xne(e,t,n){let r=0,a=t.map(u=>(r+=u,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${r}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=Bg(s,n),o=r===0?0:e.size/r,l=U(()=>{let u=[];e=q(e,[1,r,o]);for(let h=0;h<t.length;++h){let d=h===0?0:a[h-1],p=[0,d,0],f=[1,t[h],o];u[h]=q(Me(e,p,f),i)}return e.dispose(),u}),c=new zc([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var wne=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=I("thenBranch",e,t,n),a=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=I("body",e,t,n),a=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[a].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 c=s;for(;l[0];){let u=c;c=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let h=c.map(p=>p.id);u.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()})}return c}case"LoopCond":{let r=I("pred",e,t,n);return[la(r)]}case"Switch":{let r=I("pred",e,t,n),a=I("data",e,t,n);return a.kept||(a=la(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>wn(a,t,n)!==void 0);if(r){let a=wn(r,t,n);return[la(a)]}return}case"Enter":{let r=I("frameName",e,t,n),a=I("tensor",e,t,n);return n.enterFrame(r),[la(a)]}case"Exit":{let r=I("tensor",e,t,n);return n.exitFrame(),[la(r)]}case"NextIteration":{let r=I("tensor",e,t,n);return n.nextIteration(),[la(r)]}case"TensorArrayV3":{let r=I("size",e,t,n),a=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),c=I("name",e,t,n),u=new mne(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,Se(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=I("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),a=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[Se(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),a=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=gne(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=yne(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=Ane(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=xne(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function H7(e,t,n){let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="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 c=I("strides",e,t,n),u=t1(e,t,n),h=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[p,f]=I("args",e,t,n),m=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var _ne=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[nd(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=I("strides",e,t,n),a=t1(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Zr(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=H7(e,t,n);return[ka.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=H7(e,t,n);return[ka.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=I("outputShape",e,t,n),a=I("strides",e,t,n),s=t1(e,t,n);return[rd(I("x",e,t,n),I("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=I("strides",e,t,n),a=t1(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[Ho(I("input",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[jf(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Nu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Mu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=h0(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Uf(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[em(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[qf(I("x",e,t,n),I("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},bne=(e,t,n)=>{switch(e.op){case"Fill":{let r=I("shape",e,t,n),a=I("dtype",e,t,n),s=I("value",e,t,n);return[Cu(r,s,a)]}case"LinSpace":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("num",e,t,n);return[o0(r,a,s)]}case"Multinomial":{let r=I("logits",e,t,n),a=I("numSamples",e,t,n),s=I("seed",e,t,n);return[d0(r,a,s)]}case"OneHot":{let r=I("indices",e,t,n),a=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[Bo(r,a,s,i)]}case"Ones":return[Rr(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Nn(I("x",e,t,n))];case"RandomUniform":return[Zo(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("step",e,t,n);return[pd(r,a,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let r=I("shape",e,t,n),a=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[vd(r,a,s,I("dtype",e,t,n),i)]}case"Zeros":return[Ct(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[He(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Vg(e,t,n){let r=I("boxes",e,t,n),a=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:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var vne=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=Vg(e,t,n),c=await St.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=Vg(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await St.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=Vg(e,t,n);return[await St.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=ye(I("condition",e,t,n),"bool"),a=[await dm(r)];return r.dispose(),a}case"ListDiff":return m0(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},kne=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=I("x",e,t,n),a=I("k",e,t,n),s=I("sorted",e,t,n),i=cm(r,a,s);return[i.values,i.indices]}case"Unique":{let r=I("x",e,t,n),a=kd(r);return[a.values,a.indices]}case"UniqueV2":{let r=I("x",e,t,n),a=I("axis",e,t,n),s=kd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ine=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=I("default",e,t,n);return[wn(e.name,t,n)||r];case"Placeholder":return[wn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[la(c)]}case"IdentityN":return I("x",e,t,n).map(c=>la(c));case"Snapshot":let a=I("x",e,t,n);return[la(a)];case"Shape":return[en(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>en(c.shape));case"Size":return[Se(I("x",e,t,n).size,"int32")];case"Rank":return[Se(I("x",e,t,n).rank,"int32")];case"NoOp":return[Se(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nne=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Se(0),this.tensorMap=new Map,Vt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),U(()=>{let r=nr(t),a=n.length,s=r.length;k.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];Vt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return U(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return Tn(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Sne=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new Nne(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Tne=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[St.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[St.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=I("image",e,t,n),a=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[St.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ene=(e,t,n)=>{switch(e.op){case"Equal":return[wa(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[Xs(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[er(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[ba(I("a",e,t,n),I("b",e,t,n))];case"Less":return[od(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[qs(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[tr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Fu(I("a",e,t,n))];case"LogicalOr":return[cd(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[mn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Cne=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[qe(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Transpose":return[at(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="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[c,u]=I("args",e,t,n);return[ka.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:c,activation:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Rne=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[js(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[js(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[Yf(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[Pu(I("x",e,t,n))];case"LogSoftmax":return[ud(I("x",e,t,n))];case"SparseToDense":return[pm(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Fne=(e,t,n)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Wn(I("x",e,t,n),i,o)]}case"Mean":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[bt(I("x",e,t,n),i,o)]}case"Min":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Xo(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Te(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ed(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ku(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[Iu(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[zf(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[dd(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[sd(I("x",e,t,n),i,o,l)]}case"Bincount":let r=I("x",e,t,n),a=I("weights",e,t,n),s=I("size",e,t,n);return[Y2(r,a,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[n0(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mne=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=I("n",e,t,n),a=I("axis",e,t,n),s=I("tensors",e,t,n);return s=s.slice(0,r),[ct(s,a)]}case"Gather":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[Gs(r,ye(a,"int32"),0)]}case"GatherV2":{let r=I("axis",e,t,n),a=I("batchDims",e,t,n),s=I("x",e,t,n),i=I("indices",e,t,n);return[Gs(s,ye(i,"int32"),r,a)]}case"Reverse":{let r=I("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=I("x",e,t,n);return[Sn(s,a)]}case"ReverseV2":{let r=I("axis",e,t,n),a=I("x",e,t,n);return[Sn(a,r)]}case"Slice":{let r=I("begin",e,t,n),a=I("size",e,t,n);return[Me(I("x",e,t,n),r,a)]}case"StridedSlice":{let r=I("begin",e,t,n),a=I("end",e,t,n),s=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),c=I("newAxisMask",e,t,n),u=I("shrinkAxisMask",e,t,n),h=I("x",e,t,n);return[lm(h,r,a,s,i,o,l,c,u)]}case"Pack":return U(()=>{let r=I("axis",e,t,n),a=I("tensors",e,t,n),s=a[0].shape,i=va(a[0]).shape,o=a.map(l=>{let c=k.arraysEqual(l.shape,s);if(!c&&!k.arraysEqual(va(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:q(l,s)});return[Tn(o,r)]});case"Unpack":{let r=I("axis",e,t,n),a=I("tensor",e,t,n);return nr(a,r)}case"Tile":{let r=I("reps",e,t,n);return[_a(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),s=I("x",e,t,n);return sn(s,a,r)}case"ScatterNd":{let r=I("indices",e,t,n),a=I("values",e,t,n),s=I("shape",e,t,n);return[y0(r,a,s)]}case"GatherNd":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[g0(r,a)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),s=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[pm(r,s,a,s.dtype===i.dtype?i:ye(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$ne=(e,t,n)=>{switch(e.op){case"FFT":return[Lu(I("x",e,t,n))];case"IFFT":return[Jo(I("x",e,t,n))];case"RFFT":return[Wu(I("x",e,t,n))];case"IRFFT":return[_d(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Dne=(e,t,n)=>{switch(e.op){case"Cast":return[ye(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[kn(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[va(I("x",e,t,n),r)]}case"Reshape":return[q(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[tm(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Jr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),a=I("paddings",e,t,n);return[$u(I("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),a=I("crops",e,t,n);return[Su(I("x",e,t,n),r,a)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[Gf(I("x",e,t,n),r,a)]}case"BroadcastTo":return[Tu(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function j7(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return U(()=>pne(s,i,o));case"basic_math":return U(()=>fne(s,i,o));case"control":return wne(s,i,o);case"convolution":return U(()=>_ne(s,i,o));case"creation":return U(()=>bne(s,i,o));case"dynamic":return vne(s,i,o);case"evaluation":return U(()=>kne(s,i,o));case"image":return U(()=>Tne(s,i,o));case"graph":return U(()=>Ine(s,i,o));case"logical":return U(()=>Ene(s,i,o));case"matrices":return U(()=>Cne(s,i,o));case"normalization":return U(()=>Rne(s,i,o));case"reduction":return U(()=>Fne(s,i,o));case"slice_join":return U(()=>Mne(s,i,o));case"spectral":return U(()=>$ne(s,i,o));case"transformation":return U(()=>Dne(s,i,o));case"hash_table":return Sne(s,i,o,r);case"custom":let l=_7(s.op);if(l&&l.customExecutor)return l.customExecutor(new dne(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return k.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var G7=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function X7(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Mn(d)[0]),u=[];r!=null&&(u=r.map(d=>Mn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((q7(d)||One(d)||zne(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Pne(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Mn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var Lne=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Wne=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Bne=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function q7(e){return Lne.indexOf(e.op)>=0}function One(e){return Wne.indexOf(e.op)>=0}function zne(e){return Bne.indexOf(e.op)>=0}var Ug=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 Ug(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(r=>r.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(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=X7(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Pne(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 r=n.map(u=>this.graph.nodes[Mn(u)[0]]),a=t.map(u=>Mn(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return U(()=>{let u=new G7(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Mn(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let A=j7(m,h,u,this._resourceManager);if(k.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=A,this.checkTensorForDisposal(m.name,m,h,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(f=>wn(f,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,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=qte(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new G7(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>wn(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Mn(g)[0]]),i=n.map(g=>Mn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:h}=X7(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[b,x]=Mn(g),w=[];w[x]=e[g],p[b]=w});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}u==null&&!r&&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=>!q7(g)&&!wn(g.name,p,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 [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=oa(u.node.name,n)),r[u.node.name]==null){let d=j7(u.node,r,n,this._resourceManager);h||([h]=oa(u.node.name,n));let p=n.currentContext;k.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=oa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!wn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!wn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Mn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&k.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Mn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Mn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Vne=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Une="?tfjs-format=file",Hne="model.json",V0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Vne}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=pn.browserHTTPRequest(e,this.loadOptions);else{let t=pn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(pn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=pn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ug(W7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=W7.Instance.transformGraph(e.modelInitializer);this.initializer=new Ug(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=pn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 et)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Jn(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Hne}${Une}`);let n=new V0(e,t);return await n.load(),n}var q8="3.1.0",U0={};ze(U0,{CSVDataset:()=>Z7,Dataset:()=>Fl,FileDataSource:()=>J7,TextLineDataset:()=>K7,URLDataSource:()=>Y7,array:()=>jne,csv:()=>qne,func:()=>Xne,generator:()=>Kne,microphone:()=>Jne,version_data:()=>Yne,webcam:()=>Zne,zip:()=>Gne});var Qne=el(H0()),ere=el(H0());function tre(e,t){return n1(e,t)}function n1(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Ml(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=n1(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function nre(e,t=e6){return Q7(e,t)}function Q7(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Ml(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=Q7(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function e6(e){return e===null?null:Ml(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function t6(e,t){let n=new Map;n1(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(k.isPromise(a)){let s=await a;n.set(r,s)}}return n1(e,t,n)}function Ml(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof et))}function are(e){return e==null||rre(e)||Array.isArray(e)||typeof e=="object"&&e instanceof et||k.isTypedArray(e)}function rre(e){return e===null||typeof e!="object"&&typeof e!="function"}function ire(e){return tre(e,sre)}function sre(e){return e instanceof et?{value:e.clone(),recurse:!1}:Ml(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var n6=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}},Hg=class extends n6{constructor(){super(Hg.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 r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};Hg.INITIAL_CAPACITY=32;function r6(e){return new ore(e)}function jg(e){return new lre(e)}function ure(e,t){return new a6(e,t)}function hre(e,t=Ba.FAIL){return new cre(e,t)}var qt=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 gre(this,e)}filter(e){return new Are(this,e)}map(e){return new yre(this,e)}mapAsync(e){return new s6(this,e)}serialMapAsync(e){return new s6(this,e).serial()}flatmap(e){return new xre(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 mre(this,e,t)}columnMajorBatch(e,t=!0,n=e6){return this.rowMajorBatch(e,t).map(r=>nre(r,n))}concatenate(e,t){return new a6(r6([this,e]),t)}take(e){return e<0||e==null?this:new fre(this,e)}skip(e){return e<0||e==null?this:new pre(this,e)}prefetch(e){return new i6(this,e)}shuffle(e,t){return new wre(this,e,t)}serial(){return new dre(this)}},ore=class extends qt{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:ire(e),done:!1}}},lre=class extends qt{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: ${e.message}`,e}}},dre=class extends qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},pre=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},fre=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},mre=class extends qt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Are=class extends qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Fe(e.value)}}},yre=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),r=dr.getTensorsInContainer(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},gre=class extends qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},s6=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=dr.getTensorsInContainer(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Gg=class extends qt{constructor(){super();this.outputQueue=new Hg,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},xre=class extends Gg{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),r=dr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)dr.isTensorInList(a,r)||a.dispose();return!0}},a6=class extends qt{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}},Ba;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Ba||(Ba={}));var cre=class extends qt{constructor(e,t=Ba.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 r(s){return s instanceof qt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await t6(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Ba.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Ba.SHORTEST:return{value:null,done:!0};case Ba.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},i6=class extends qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new n6(t)}summary(){return`${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()}},wre=class extends i6{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=ere.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}}},Fl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),$n(async()=>(await n.iterator()).columnMajorBatch(e,t,_re),r)}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,$n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,$n(async()=>(await t.iterator()).filter(r=>U(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return $n(async()=>(await t.iterator()).map(n=>U(()=>e(n))),this.size)}mapAsync(e){let t=this;return $n(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 $n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,$n(async()=>{let r=jg(async()=>({value:await t.iterator(),done:!1}));return ure(r.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,$n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Qne.alea(t||k.now().toString());return $n(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,$n(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()}};Fl.MAX_BUFFER_SIZE=1e4;function $n(e,t=null){return new class extends Fl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function jne(e){return $n(async()=>r6(e),e.length)}function Gne(e){if(!Ml(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 $n(async()=>{let n=await t6(e,r=>{if(r instanceof Fl)return{value:r.iterator(),recurse:!1};if(Ml(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return hre(n,Ba.SHORTEST)},t)}function _re(e){if(e===null)return null;let t=e[0];return are(t)?{value:bre(e),recurse:!1}:{value:null,recurse:!0}}function bre(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?Tn(e):pr(e)}var K7=class extends Fl{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))}},r1='"',Pc=Symbol("out"),o6=Symbol("field"),a1=Symbol("quote"),qg=Symbol("quoteafterquote"),l6=Symbol("quoteinquote"),Z7=class extends Fl{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 K7(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((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" 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={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],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 c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Pc;for(let i=0;i<a;i++)switch(s){case Pc:switch(e.charAt(i)){case r1:r=i+1,s=a1;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Pc;break;default:s=o6,r=i;break}break;case o6:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Pc,r=i+1;break;default:}break;case a1:switch(e.charAt(i)){case r1:s=qg;break;default:}break;case qg:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Pc,r=i+1;break;case r1:s=a1;break;default:s=l6;break}break;case l6:switch(e.charAt(i)){case r1:s=a1;break;default:}break;default:}if(s===qg?n.push(e.substring(r,a-1)):n.push(e.substring(r)),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}},u6=class extends qt{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(Q().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new u6(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 r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[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(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({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(a),r({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((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),pr(n,t)}},c6=class extends qt{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=en([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=fr([s,a,o,i],[1,4])}else this.cropBox=fr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Q().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 c6(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=bu.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return U(()=>{let t=kn(ye(e,"float32"),0),n;n=St.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return q(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},h6=class{},d6=class extends qt{split(e){return new vre(this,e)}},vre=class extends d6{constructor(e,t){super();this.upstream=e,this.impl=new kre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},kre=class extends Gg{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Nre=class extends qt{decodeUTF8(){return new Ire(this)}},Ire=class extends d6{constructor(e){super();this.upstream=e,this.impl=new Sre(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Sre=class extends Gg{constructor(e){super();if(this.upstream=e,Q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Ck();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},p6=class extends Nre{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(Q().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof 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o1(e,t,n,r=null){for(let a=0;a<w6.length;a++){let{key:s,indices:i}=w6[a],o=pa[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var j4=class{constructor(e,t,n,r){this.storedBoxes=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=e,this.meshDetector=t,this.irisModel=n,this.meshWidth=r.face.mesh.inputSize,this.meshHeight=r.face.mesh.inputSize,this.irisSize=r.face.iris.inputSize,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(e,t,n,r){let a=s1({startPoint:t.startPoint,endPoint:t.endPoint}),s=[a[0]/this.meshWidth,a[1]/this.meshHeight],i=e.map(h=>[s[0]*(h[0]-this.meshWidth/2),s[1]*(h[1]-this.meshHeight/2),h[2]]),o=n!==0?x6(n,[0,0]):Zg,l=n!==0?i.map(h=>[...Wre(h,o),h[2]]):i,c=n!==0?Lre(r):Zg,u=[...i1({startPoint:t.startPoint,endPoint:t.endPoint}),1];return l.map(h=>[h[0]+bi(u,c[0]),h[1]+bi(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(e){let t=e[Qg[0]][2],n=e[t2[0]][2];return t-n}getEyeBox(e,t,n,r,a=!1){let s=Kg(Xg(this.calculateLandmarksBoundingBox([e[n],e[r]]),this.irisEnlarge)),i=s1(s),o=St.cropAndResize(t,[[s.startPoint[1]/this.meshHeight,s.startPoint[0]/this.meshWidth,s.endPoint[1]/this.meshHeight,s.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return a&&(o=St.flipLeftRight(o)),{box:s,boxSize:i,crop:o}}getEyeCoords(e,t,n,r=!1){let a=[];for(let s=0;s<n2;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(r?1-i/this.irisSize:i/this.irisSize)*n[0]+t.startPoint[0],o/this.irisSize*n[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Yre)}}getAdjustedIrisCoords(e,t,n){let r=e[pa[`${n}EyeUpper0`][Zre]][2],a=e[pa[`${n}EyeLower0`][Jre]][2],s=(r+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=r:o===4&&(l=a),[i[0],i[1],l]})}async predict(e,t){let n=!1,r;if((this.skipped===0||this.skipped>t.face.detector.skipFrames||!t.face.mesh.enabled||!t.videoOptimized)&&(r=await this.boundingBoxDetector.getBoundingBoxes(e),this.skipped=0),t.videoOptimized&&this.skipped++,r&&r.boxes&&(!t.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==t.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let s of r.boxes)this.storedBoxes.push({startPoint:s.box.startPoint.dataSync(),endPoint:s.box.endPoint.dataSync(),landmarks:s.landmarks,confidence:s.confidence});this.storedBoxes.length>0&&(n=!0)}if(n){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let s=0;s<this.storedBoxes.length;s++){let i=Dre({startPoint:this.storedBoxes[s].startPoint,endPoint:this.storedBoxes[s].endPoint},r.scaleFactor),o=Xg(i),l=Kg(o),c=this.storedBoxes[s].landmarks.arraySync(),u=this.storedBoxes[s].confidence;this.storedBoxes[s]={...l,confidence:u,landmarks:c}}this.runsWithoutFaceDetector=0}r&&r.boxes&&r.boxes.forEach(s=>{s.box.startPoint.dispose(),s.box.endPoint.dispose(),s.landmarks.dispose()});let a=U(()=>this.storedBoxes.map((s,i)=>{let o,l=0,c;if(t.face.detector.rotation){let[b,x]=s.landmarks.length>=Hre?Gre:Kre;l=zre(s.landmarks[b],s.landmarks[x]);let w=i1({startPoint:s.startPoint,endPoint:s.endPoint}),_=[w[0]/e.shape[2],w[1]/e.shape[1]],N=St.rotateWithOffset(e,l,0,_);c=x6(-l,w),o=A6({startPoint:s.startPoint,endPoint:s.endPoint},N,[this.meshHeight,this.meshWidth]).div(255)}else{c=Zg;let b=e.clone();o=A6({startPoint:s.startPoint,endPoint:s.endPoint},b,[this.meshHeight,this.meshWidth]).div(255)}if(!t.face.mesh.enabled)return{coords:null,box:s,faceConfidence:null,confidence:s.confidence,image:o};let[,u,h]=this.meshDetector.predict(o),d=u.dataSync()[0];if(d<t.face.detector.minConfidence)return null;let p=q(h,[-1,3]).arraySync();if(t.face.iris.enabled){let{box:b,boxSize:x,crop:w}=this.getEyeBox(p,o,Qg[0],Qg[1],!0),{box:_,boxSize:N,crop:T}=this.getEyeBox(p,o,t2[0],t2[1]),E=this.irisModel.predict(ct([w,T])).dataSync(),M=E.slice(0,n2*3),{rawCoords:z,iris:P}=this.getEyeCoords(M,b,x,!0),B=E.slice(n2*3),{rawCoords:G,iris:V}=this.getEyeCoords(B,_,N),K=this.getLeftToRightEyeDepthDifference(p);Math.abs(K)<30?(o1(p,z,"left"),o1(p,G,"right")):K<1?o1(p,z,"left",["EyeUpper0","EyeLower0"]):o1(p,G,"right",["EyeUpper0","EyeLower0"]);let X=this.getAdjustedIrisCoords(p,P,"left"),ee=this.getAdjustedIrisCoords(p,V,"right");p=p.concat(X).concat(ee)}let f=this.transformRawCoords(p,s,l,c),m=Xg(this.calculateLandmarksBoundingBox(f)),A=Kg(m),y=fr(f),g={coords:y,box:m,faceConfidence:d,confidence:s.confidence,image:o,rawCoords:p};return t.face.mesh.returnRawData||delete g.rawCoords,this.storedBoxes[i]={...A,landmarks:y.arraySync(),confidence:s.confidence,faceConfidence:d},g}));return a=a.filter(s=>s!==null),this.detectedFaces=a.length,a}calculateLandmarksBoundingBox(e){let t=e.map(s=>s[0]),n=e.map(s=>s[1]),r=[Math.min(...t),Math.min(...n)],a=[Math.max(...t),Math.max(...n)];return{startPoint:r,endPoint:a,landmarks:e}}},_6=mh(G4()),b6={};Nr(b6,{FaceBoxes:()=>v6,load:()=>Qre});var k6={};function Lc(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};k6[e]=i,Ve("Human profiler",e,i)}var v6=class{constructor(e,t){this.enlarge=1.1,this.model=e,this.config=t}async estimateFaces(e,t){t&&(this.config=t);let n=[],r=St.resizeBilinear(e,[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),a=r.toInt(),s,i;if(t.profile){let o=await Vo(()=>this.model.executeAsync(a));s=o.result[0].dataSync(),i=o.result[1].squeeze().arraySync(),o.result.forEach(l=>l.dispose()),Lc("faceboxes",o)}else{let[o,l,c]=await this.model.executeAsync(a);s=o.dataSync();let u=l.squeeze();i=u.arraySync(),o.dispose(),l.dispose(),u.dispose(),c.dispose()}a.dispose(),r.dispose();for(let o in i)if(s[o]&&s[o]>this.config.face.detector.minConfidence){let l=[i[o][0]/this.enlarge,i[o][1]/this.enlarge,i[o][2]*this.enlarge,i[o][3]*this.enlarge],c=[l[1],l[0],l[3]-l[1],l[2]-l[0]],u=[parseInt((c[0]*e.shape[2]).toString()),parseInt((c[1]*e.shape[1]).toString()),parseInt((c[2]*e.shape[2]).toString()),parseInt((c[3]*e.shape[1]).toString())],h=St.cropAndResize(e,[l],[0],[this.config.face.detector.inputSize,this.config.face.detector.inputSize]),d=h.div([255]);h.dispose(),n.push({confidence:s[o],box:u,boxRaw:this.config.face.mesh.returnRawData?c:null,image:d})}return n}};async function Qre(e){let t=await Jn(e.face.detector.modelPath);Ve(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`);let n=new v6(t,e);return e.face.mesh.enabled&&Ve(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&Ve(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),n}var I6={};Nr(I6,{load:()=>r2,predict:()=>a2});var $l,l1={age:0},u1=Number.MAX_SAFE_INTEGER;async function r2(e){return $l||($l=await Jn(e.face.age.modelPath),Ve(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),$l}async function a2(e,t){return $l?u1<t.face.age.skipFrames&&t.videoOptimized&&l1.age&&l1.age>0?(u1++,l1):(t.videoOptimized?u1=0:u1=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=St.resizeBilinear(e,[t.face.age.inputSize,t.face.age.inputSize],!1),a=L(r,[255]);Fe(r);let s,i={age:0};if(!t.profile)t.face.age.enabled&&(s=await $l.predict(a));else{let o=t.face.age.enabled?await Vo(()=>$l.predict(a)):{};s=o.result.clone(),o.result.dispose(),Lc("age",o)}if(a.dispose(),s){let o=s.dataSync();i.age=Math.trunc(10*o[0])/10}s.dispose(),l1=i,n(i)})):null}var N6={};Nr(N6,{load:()=>s2,predict:()=>i2});var vi,o2={gender:""},c1=Number.MAX_SAFE_INTEGER,l2=!1,u2=[.2989,.587,.114];async function s2(e){return vi||(vi=await Jn(e.face.gender.modelPath),l2=vi.inputs[0].shape[3]===1,Ve(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),vi}async function i2(e,t){return vi?c1<t.face.gender.skipFrames&&t.videoOptimized&&o2.gender!==""?(c1++,o2):(t.videoOptimized?c1=0:c1=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=St.resizeBilinear(e,[t.face.gender.inputSize,t.face.gender.inputSize],!1),a;l2?a=U(()=>{let[o,l,c]=sn(r,3,3),u=L(o,u2[0]),h=L(l,u2[1]),d=L(c,u2[2]);return Qh([u,h,d]).sub(.5).mul(2)}):a=L(r,[255]),Fe(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await vi.predict(a));else{let o=t.face.gender.enabled?await Vo(()=>vi.predict(a)):{};s=o.result.clone(),o.result.dispose(),Lc("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(l2){let l=Math.trunc(100*Math.abs(o[0]-o[1]))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=l)}else{let l=Math.trunc(200*Math.abs(o[0]-.5))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]<=.5?"female":"male",i.confidence=Math.min(.99,l))}}s.dispose(),o2=i,n(i)})):null}var S6={};Nr(S6,{load:()=>c2,predict:()=>h2});var eae=["angry","disgust","fear","happy","sad","surprise","neutral"],Dl,d2=[],h1=Number.MAX_SAFE_INTEGER,p2=[.2989,.587,.114],T6=1;async function c2(e){return Dl||(Dl=await Jn(e.face.emotion.modelPath),Ve(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),Dl}async function h2(e,t){return Dl?h1<t.face.emotion.skipFrames&&t.videoOptimized&&d2.length>0?(h1++,d2):(t.videoOptimized?h1=0:h1=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=St.resizeBilinear(e,[t.face.emotion.inputSize,t.face.emotion.inputSize],!1),[a,s,i]=sn(r,3,3);r.dispose();let o=L(a,p2[0]),l=L(s,p2[1]),c=L(i,p2[2]);a.dispose(),s.dispose(),i.dispose();let u=Qh([o,l,c]);o.dispose(),l.dispose(),c.dispose();let h=U(()=>u.sub(.5).mul(2));u.dispose();let d=[];if(t.face.emotion.enabled){let p;if(t.profile){let f=await Vo(()=>Dl.predict(h));p=f.result.dataSync(),f.result.dispose(),Lc("emotion",f)}else{let f=await Dl.predict(h);p=f.dataSync(),Fe(f)}for(let 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R6={};Nr(R6,{PoseNet:()=>F6,load:()=>f2});var nae=[-123.15,-115.9,-103.06];function rae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}function aae(e){let[t,n,r,a]=e;return{offsets:r,heatmap:a,displacementFwd:t,displacementBwd:n}}var sae=class{constructor(e){this.model=e}predict(e,t){return U(()=>{let n=(t.body.modelType==="ResNet"?e.toFloat().add(nae):e.toFloat().div(127.5).sub(1)).expandDims(0),r=this.model.predict(n).map(s=>s.squeeze([0])),a=t.body.modelType==="ResNet"?aae(r):rae(r);return{heatmapScores:a.heatmap.sigmoid(),offsets:a.offsets,displacementFwd:a.displacementFwd,displacementBwd:a.displacementBwd}})}dispose(){this.model.dispose()}};function m2(e){return Math.floor(e/2)}var iae=class{constructor(e,t){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=t}enqueue(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)}dequeue(){let e=this.priorityQueue[0];return 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n=e.shape[1],r=e.shape[2],a=U(()=>e.resizeBilinear([t.hand.inputSize,t.hand.inputSize]).div(127.5).sub(1)),s=await this.getBoxes(a,t);a.dispose();let i=[];if(!s||s.length===0)return i;for(let o of s){let l=o.box.dataSync(),c=l.slice(0,2),u=l.slice(2,4),h=o.palmLandmarks.arraySync();o.box.dispose(),o.palmLandmarks.dispose(),i.push(Eae({startPoint:c,endPoint:u,palmLandmarks:h,confidence:o.confidence},[r/t.hand.inputSize,n/t.hand.inputSize]))}return i}};function Rae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Fae(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Rae(n)}var H6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ki(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Mae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function j6(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(ki(e[a],Mae(t,s)))}return n}function G6(e,t){let 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a=w2(t),s=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=e.map(d=>[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),o=G6(n,[0,0]),l=i.map(d=>[...q6(d,o),d[2]]),c=$ae(r),u=[...d1(t),1],h=[ki(u,c[0]),ki(u,c[1])];return l.map(d=>[d[0]+h[0],d[1]+h[1],d[2]])}async estimateHands(e,t){let n=!1,r;(this.skipped===0||this.skipped>t.hand.skipFrames||!t.hand.landmarks||!t.videoOptimized)&&(r=await this.handDetector.estimateHandBounds(e,t),this.skipped=0),t.videoOptimized&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==t.hand.maxHands||!t.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let a=[];for(let s=0;s<this.storedBoxes.length;s++){let i=this.storedBoxes[s];if(i)if(t.hand.landmarks){let o=t.hand.rotation?Fae(i.palmLandmarks[Oae],i.palmLandmarks[zae]):0,l=d1(i),c=[l[0]/e.shape[2],l[1]/e.shape[1]],u=t.hand.rotation?St.rotateWithOffset(e,o,0,c):e.clone(),h=G6(-o,l),d=n?this.getBoxForPalmLandmarks(i.palmLandmarks,h):i,p=Tae(d,u,[this.inputSize,this.inputSize]),f=p.div(255);p.dispose(),u.dispose();let[m,A]=await this.landmarkDetector.predict(f);f.dispose();let y=m.dataSync()[0];if(m.dispose(),y>=t.hand.minConfidence){let g=q(A,[-1,3]),b=g.arraySync();A.dispose(),g.dispose();let x=this.transformRawCoords(b,d,o,h),w=this.getBoxForHandLandmarks(x);this.storedBoxes[s]=w;let _={landmarks:x,confidence:y,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};a.push(_)}else this.storedBoxes[s]=null;A.dispose()}else{let o=_2(b2(i),X6),l={confidence:i.confidence,box:{topLeft:o.startPoint,bottomRight:o.endPoint}};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(s=>s!==null),this.detectedHands=a.length,a}calculateLandmarksBoundingBox(e){let 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${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var Wae=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=e[n].keypoints.find(l=>l.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.y<s.position.y&&a.position.y<s.position.y?t.push({body:n,gesture:"i give up"}):s&&r&&r.position.y<s.position.y?t.push({body:n,gesture:"raise left hand"}):s&&a&&a.position.y<s.position.y&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},Bae=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let r=e[n].mesh[35][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let s=e[n].mesh[152][2];Math.abs(s)>10&&t.push({face:n,gesture:`head ${s<0?"up":"down"}`})}return t},Vae=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let 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`),h.pixelate=function(w){let _=w/o,N=w/l,T=x(h.pixelate.SHADER);m.uniform2f(T.uniform.size,_,N),b()},h.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
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`)}var Et=null,Yt=null,Ft=null;function Gae(e,t){let n;if(e instanceof et)n=Sr(e);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,s=r,i=a;if(t.filter.width>0?s=t.filter.width:t.filter.height>0&&(s=r*(t.filter.height/a)),t.filter.height>0?i=t.filter.height:t.filter.width>0&&(i=a*(t.filter.width/r)),!s||!i)return Ve("Human: invalid input",e),null;(!Et||Et.width!==s||Et.height!==i)&&(Et=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,i):document.createElement("canvas"),Et.width!==s&&(Et.width=s),Et.height!==i&&(Et.height=i));let o=Et.getContext("2d");if(e instanceof ImageData?o.putImageData(e,0,0):o.drawImage(e,0,0,r,a,0,0,Et.width,Et.height),t.filter.enabled){if((!Ft||!Yt||Et.width!==Yt.width||Et.height!==Yt.height)&&(Yt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Et.width,Et.height):document.createElement("canvas"),Yt.width!==Et.width&&(Yt.width=Et.width),Yt.height!==Et.height&&(Yt.height=Et.height),Ve("created FX filter"),Ft=fa.flags.IS_BROWSER?new jae({canvas:Yt}):null),!Ft)return Et;Ft.reset(),Ft.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Ft.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ft.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ft.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Ft.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Ft.addFilter("hue",t.filter.hue),t.filter.negative&&Ft.addFilter("negative"),t.filter.sepia&&Ft.addFilter("sepia"),t.filter.vintage&&Ft.addFilter("brownie"),t.filter.sepia&&Ft.addFilter("sepia"),t.filter.kodachrome&&Ft.addFilter("kodachrome"),t.filter.technicolor&&Ft.addFilter("technicolor"),t.filter.polaroid&&Ft.addFilter("polaroid"),t.filter.pixelate!==0&&Ft.addFilter("pixelate",t.filter.pixelate),Ft.apply(Et)}else Yt=Et,Ft&&(Ft=null);let l;if(Yt.data){let u=[Yt.height,Yt.width,3];l=Rf(Yt.data,u,"int32")}else if(t.backend==="webgl"||Yt instanceof ImageData)l=bu.fromPixels(Yt);else{let u=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(s,i):document.createElement("canvas");u.width=s,u.height=i;let h=u.getContext("2d");h==null||h.drawImage(Yt,0,0);let d=h==null?void 0:h.getImageData(0,0,s,i);l=bu.fromPixels(d)}let c=l.toFloat();n=c.expandDims(0),l.dispose(),c.dispose()}return{tensor:n,canvas:t.filter.return?Yt:null}}var qae={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,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:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}},k2=`
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2Q==`,Z6={};Nr(Z6,{author:()=>av,browser:()=>nv,bugs:()=>sv,default:()=>Xae,dependencies:()=>cv,description:()=>Y6,devDependencies:()=>dv,engines:()=>lv,homepage:()=>iv,keywords:()=>fv,license:()=>ov,main:()=>ev,module:()=>tv,name:()=>J6,peerDependencies:()=>hv,repository:()=>uv,scripts:()=>pv,sideEffects:()=>Q6,types:()=>rv,version:()=>N2});var J6="@vladmandic/human",N2="0.20.9",Y6="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",Q6=!1,ev="dist/human.node.js",tv="dist/human.esm.js",nv="dist/human.esm.js",rv="types/human.d.ts",av="Vladimir Mandic 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e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Wc(s,i):n[a]=i}),n),{})}var mv=class{constructor(e={}){this.tf=P2,this.package=Z6,this.version=N2,this.config=Wc(qae,e),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=_6,this.age=I6,this.gender=N6,this.emotion=S6,this.body=R6,this.hand=V6}profile(){return this.config.profile?k6:{}}analyze(...e){if(!this.analyzeMemoryLeaks)return;let t=this.tf.engine().state.numTensors,n=this.numTensors;this.numTensors=t;let r=t-n;r!==0&&Ve(...e,r)}sanity(e){if(!this.checkSanity)return null;if(!e)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(e instanceof this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(t){return"backend not loaded"}return null}simmilarity(e,t){return this.config.face.embedding.enabled?tae(e,t):0}async load(e=null){this.state="load";let t=ft();e&&(this.config=Wc(this.config,e)),this.firstRun&&(Ve(`version: ${this.version} TensorFlow/JS version: ${this.tf.version_core}`),await this.checkBackend(!0),this.tf.ENV.flags.IS_BROWSER&&(Ve("configuration:",this.config),Ve("tf flags:",this.tf.ENV.flags)));let n=this.config.face.detector.modelPath.includes("faceboxes")?b6:_6;this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.face||(this.config.face.enabled?n.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?r2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?s2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?c2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?E6(this.config):null),this.models.posenet||(this.config.body.enabled?f2(this.config):null),this.models.handpose||(this.config.hand.enabled?x2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await n.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await r2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await s2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await c2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await E6(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await f2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await x2(this.config))),this.firstRun&&(Ve("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.firstRun=!1);let r=Math.trunc(ft()-t);r>(this.perf.load||0)&&(this.perf.load=r)}async checkBackend(e=!1){if(this.config.backend&&this.config.backend!==""&&e||this.tf.getBackend()!==this.config.backend){let t=ft();this.state="backend",Ve("setting backend:",this.config.backend),this.config.backend==="wasm"&&(Ve("settings wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath),await 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p){if(this.analyze("Get Face"),!f.image||f.image.isDisposedInternal){Ve("Face object is disposed:",f.image);continue}this.analyze("Start Age:"),this.config.async?l=this.config.face.age.enabled?a2(f.image,this.config):{}:(this.state="run:age",o=ft(),l=this.config.face.age.enabled?await a2(f.image,this.config):{},this.perf.age=Math.trunc(ft()-o)),this.analyze("Start Gender:"),this.config.async?c=this.config.face.gender.enabled?i2(f.image,this.config):{}:(this.state="run:gender",o=ft(),c=this.config.face.gender.enabled?await i2(f.image,this.config):{},this.perf.gender=Math.trunc(ft()-o)),this.analyze("Start Emotion:"),this.config.async?u=this.config.face.emotion.enabled?h2(f.image,this.config):{}:(this.state="run:emotion",o=ft(),u=this.config.face.emotion.enabled?await h2(f.image,this.config):{},this.perf.emotion=Math.trunc(ft()-o)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?h=this.config.face.embedding.enabled?C6(f.image,this.config):[]:(this.state="run:embedding",o=ft(),h=this.config.face.embedding.enabled?await C6(f.image,this.config):[],this.perf.embedding=Math.trunc(ft()-o)),this.analyze("End Emotion:"),this.config.async&&([l,c,u,h]=await Promise.all([l,c,u,h])),this.analyze("Finish Face:"),!this.config.face.iris.enabled&&((n=f==null?void 0:f.annotations)==null?void 0:n.leftEyeIris)&&((r=f==null?void 0:f.annotations)==null?void 0:r.rightEyeIris)&&(delete f.annotations.leftEyeIris,delete f.annotations.rightEyeIris);let m=((a=f.annotations)==null?void 0:a.leftEyeIris)&&((s=f.annotations)==null?void 0:s.rightEyeIris)?11.7*Math.max(Math.abs(f.annotations.leftEyeIris[3][0]-f.annotations.leftEyeIris[1][0]),Math.abs(f.annotations.rightEyeIris[4][1]-f.annotations.rightEyeIris[2][1])):0;d.push({confidence:f.confidence,box:f.box,mesh:f.mesh,boxRaw:f.boxRaw,meshRaw:f.meshRaw,annotations:f.annotations,age:l.age,gender:c.gender,genderConfidence:c.confidence,emotion:u,embedding:h,iris:m!==0?Math.trunc(m)/100:0}),(i=f.image)==null||i.dispose(),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),d}async detect(e,t={}){return new Promise(async n=>{var r,a,s,i;this.state="config";let o;this.config=Wc(this.config,t),this.state="check";let l=this.sanity(e);l&&(Ve(l,e),n({error:l}));let c,u,h,d=ft();await this.checkBackend(),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start 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0:s.estimateHands(p.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",o=ft(),u=this.config.hand.enabled?await((i=this.models.handpose)==null?void 0:i.estimateHands(p.tensor,this.config)):[],this.perf.hand=Math.trunc(ft()-o)),this.analyze("End Hand:"),this.config.async&&([h,c,u]=await Promise.all([h,c,u])),p.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(o=ft(),f=[...Bae(h),...Wae(c),...Uae(u),...Vae(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(ft()-o)),this.perf.total=Math.trunc(ft()-d),this.state="idle",n({face:h,body:c,hand:u,gesture:f,performance:this.perf,canvas:p.canvas})})}async warmupBitmap(){let e=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(s=>s.blob()),t,n;switch(this.config.warmup){case"face":t=await e(k2);break;case"full":t=await e(I2);break;default:t=null}if(t){let r=await createImageBitmap(t);n=await this.detect(r,this.config),r.close()}return n}async warmupCanvas(){return new Promise(e=>{let t,n=0;switch(this.config.warmup){case"face":n=256,t="data:image/jpeg;base64,"+k2;break;case"full":n=1200,t="data:image/jpeg;base64,"+I2;break;default:t=null}let r=new Image(n,n);r.onload=()=>{let a=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(n,n):document.createElement("canvas");a.width=n,a.height=n;let s=a.getContext("2d");s==null||s.drawImage(r,0,0);let i=s==null?void 0:s.getImageData(0,0,n,n);this.detect(i,this.config).then(o=>e(o))},t?r.src=t:e(null)})}async warmupNode(){let e=s=>Buffer.from(s,"base64"),t=this.config.warmup==="face"?e(k2):e(I2),n=(void 0).decodeJpeg(t),r=n.expandDims(0);this.tf.dispose(n);let a=await this.detect(r,this.config);return this.tf.dispose(r),a}async warmup(e){let t=ft();e&&(this.config=Wc(this.config,e));let n=this.config.videoOptimized;this.config.videoOptimized=!1;let r;typeof createImageBitmap=="function"?r=await this.warmupBitmap():typeof Image!="undefined"?r=await this.warmupCanvas():r=await this.warmupNode(),this.config.videoOptimized=n;let a=ft();return Ve("Warmup",this.config.warmup,Math.round(a-t),"ms",r),r}};async function Kae(e,t,n){if(!e)return;let r=t.getContext("2d");r.font=n.baseFont,r.fillStyle=n.baseLabel;let a=1;for(let s=0;s<e.length;s++){let[i,o]=Object.entries(e[s]);if(o.length>1&&o[1].length>0){let l=i[1]>0?`#${i[1]}`:"",c=`${i[0]} ${l}: ${o[1]}`;r.fillStyle="black",r.fillText(c,8,2+a*n.baseLineHeight),r.fillStyle=n.baseLabel,r.fillText(c,6,0+a*n.baseLineHeight),a+=1}}}async function Zae(e,t,n,r){if(!e)return;let a=t.getContext("2d");for(let s of e){a.font=n.baseFont,a.strokeStyle=n.baseColor,a.fillStyle=n.baseColor,a.lineWidth=n.baseLineWidth,a.beginPath(),n.drawBoxes&&a.rect(s.box[0],s.box[1],s.box[2],s.box[3]);let i=[];if(s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris 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Path2D,i,o;i=e[a].keypoints.find(l=>l.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightShoulder"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftHip"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftShoulder"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftAnkle"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightHip"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightKnee"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightAnkle"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="leftShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="leftElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="leftWrist"),o&&s.lineTo(o.position.x,o.position.y)),i=e[a].keypoints.find(l=>l.part==="rightShoulder"),i&&(s.moveTo(i.position.x,i.position.y),o=e[a].keypoints.find(l=>l.part==="rightElbow"),o&&s.lineTo(o.position.x,o.position.y),o=e[a].keypoints.find(l=>l.part==="rightWrist"),o&&s.lineTo(o.position.x,o.position.y)),r.stroke(s)}}}async function Yae(e,t,n){if(!e)return;let r=t.getContext("2d");r.lineJoin="round";for(let a of e){if(r.font=n.baseFont,r.lineWidth=n.baseLineWidth,n.drawBoxes&&(r.lineWidth=n.baseLineWidth,r.beginPath(),r.strokeStyle=n.baseColor,r.fillStyle=n.baseColor,r.rect(a.box[0],a.box[1],a.box[2],a.box[3]),r.fillStyle="black",r.fillText("hand",a.box[0]+3,1+a.box[1]+n.baseLineHeight,a.box[2]),r.fillStyle=n.baseLabel,r.fillText("hand",a.box[0]+2,0+a.box[1]+n.baseLineHeight,a.box[2]),r.stroke()),n.drawPoints&&a.landmarks&&a.landmarks.length>0)for(let s of a.landmarks)r.fillStyle=n.useDepth?`rgba(${127.5+2*s[2]}, ${127.5-2*s[2]}, 255, 0.5)`:n.baseColor,r.beginPath(),r.arc(s[0],s[1],2,0,2*Math.PI),r.fill();if(n.drawPolygons){let s=i=>{if(!!i)for(let o=0;o<i.length;o++)r.lineWidth=n.baseLineWidth,r.beginPath(),r.strokeStyle=n.useDepth?`rgba(${127.5+2*i[o][2]}, ${127.5-2*i[o][2]}, 255, 0.5)`:n.baseColor,r.moveTo(i[o>0?o-1:0][0],i[o>0?o-1:0][1]),r.lineTo(i[o][0],i[o][1]),r.stroke()};s(a.annotations.indexFinger),s(a.annotations.middleFinger),s(a.annotations.ringFinger),s(a.annotations.pinky),s(a.annotations.thumb)}}}var Bc={face:Zae,body:Jae,hand:Yae,gesture:Kae};var Vc=0,Av=!1,wt={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function Qae(){if(Av)return;let e=`
|
|
:root { --rounded: 0.2rem; }
|
|
.menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;
|
|
box-shadow: 0 0 8px dimgrey; background: ${wt.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }
|
|
|
|
.menu:hover { box-shadow: 0 0 8px ${wt.hover}; }
|
|
.menu-container { display: block; max-height: 100vh; }
|
|
.menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }
|
|
.menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }
|
|
.menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }
|
|
.menu-title { cursor: pointer; }
|
|
.menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }
|
|
.menu-label { padding: 0; font-weight: 800; }
|
|
|
|
.menu-list { margin-right: 0.8rem; }
|
|
select:focus { outline: none; }
|
|
.menu-list-item { background: ${wt.itemBackground}; color: ${wt.itemColor}; border: none; padding: 0.2rem; font-family: inherit;
|
|
font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }
|
|
|
|
.menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}
|
|
.menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }
|
|
|
|
.menu-button { border: 0; background: ${wt.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;
|
|
border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }
|
|
.menu-button:hover { background: ${wt.buttonHover}; box-shadow: 4px 4px 4px 0 black; }
|
|
.menu-button:focus { outline: none; }
|
|
|
|
.menu-checkbox { width: 2.8rem; height: 1rem; background: ${wt.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }
|
|
.menu-checkbox:after { content: 'OFF'; color: ${wt.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
|
|
.menu-checkbox:before { content: 'ON'; color: ${wt.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
|
|
.menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${wt.checkboxOff};
|
|
border-radius: var(--rounded); transition: left 0.6s ease; }
|
|
|
|
input[type=checkbox] { visibility: hidden; }
|
|
input[type=checkbox]:checked + label { left: 1.4rem; background: ${wt.checkboxOn}; }
|
|
|
|
.menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${wt.rangeBackground}; }
|
|
.menu-range:before { color: ${wt.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }
|
|
|
|
input[type=range] { -webkit-appearance: none; }
|
|
input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${wt.itemBackground}; border-radius: var(--rounded); border: 1px; }
|
|
input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${wt.itemBackground}; border-radius: var(--rounded); border: 1px; }
|
|
input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${wt.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
|
|
input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${wt.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
|
|
|
|
.svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }
|
|
.svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }
|
|
`,t=document.createElement("style");t.innerHTML=e,document.getElementsByTagName("head")[0].appendChild(t),Av=!0}var yv=class{constructor(t,n,r,a){a&&(wt={...wt,...a}),Qae(),this.createMenu(t,n,r),this.id=0,this.instance=Vc,Vc++,this._maxFPS=0,this.hidden=0}createMenu(t,n="",r={top:null,left:null,bottom:null,right:null}){this.menu=document.createElement("div"),this.menu.id=`menu-${Vc}`,this.menu.className="menu",r&&(r.top&&(this.menu.style.top=r.top),r.bottom&&(this.menu.style.bottom=r.bottom),r.left&&(this.menu.style.left=r.left),r.right&&(this.menu.style.right=r.right)),this.container=document.createElement("div"),this.container.id=`menu-container-${Vc}`,this.container.className="menu-container menu-container-fadein";let a=document.createElement("div");a.className="menu-title",a.id=`menu-title-${Vc}`;let s=`<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" style="width: 2rem; height: 2rem; vertical-align: top;">
|
|
<path d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h352a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48zm-51.37 182.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z" class="svg-background"/>
|
|
<path d="M348.63 214.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z" class="svg-foreground"/>
|
|
</svg>`;n&&(a.innerHTML=`${n}${s}`),this.menu.appendChild(a),a.addEventListener("click",()=>{this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.menu.style.borderStyle=this.container.classList.contains("menu-container-fadeout")?"none":"solid"}),this.menu.appendChild(this.container),typeof t=="object"?t.appendChild(this.menu):document.getElementById(t).appendChild(this.menu)}get newID(){return this.id++,`menu-${this.instance}-${this.id}`}get ID(){return`menu-${this.instance}-${this.id}`}get width(){return this.menu.offsetWidth}get height(){return this.menu.offsetHeight}hide(){this.container.classList.contains("menu-container-fadein")&&(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"))}visible(){return this.container.classList.contains("menu-container-fadein")}toggle(t){if(this.container.classList.toggle("menu-container-fadeout"),this.container.classList.toggle("menu-container-fadein"),this.container.classList.contains("menu-container-fadein")&&t){let n=t.x||(t.touches&&t.touches[0]?t.touches[0].pageX:null);n&&(this.menu.style.left=`${n-this.menu.offsetWidth/2}px`),this.menu.offsetLeft<0&&(this.menu.style.left=0),this.menu.offsetLeft+this.menu.offsetWidth>window.innerWidth&&(this.menu.style.left=null,this.menu.style.right=0),this.menu.style.borderStyle="solid"}else this.menu.style.borderStyle="none"}addTitle(t){let n=document.createElement("div");return n.className="menu-title",n.id=this.newID,n.innerHTML=t,this.menu.appendChild(n),n.addEventListener("click",()=>{this.hidden=!this.hidden;let r=document.getElementsByClassName("menu");for(let a of r)a.style.display=this.hidden?"none":"block"}),n}addLabel(t){let n=document.createElement("div");return n.className="menu-item menu-label",n.id=this.newID,n.innerHTML=t,this.container.appendChild(n),n}addBool(t,n,r,a){let s=document.createElement("div");return s.className="menu-item",s.innerHTML=`<div class="menu-checkbox"><input class="menu-checkbox" type="checkbox" id="${this.newID}" ${n[r]?"checked":""}/><label class="menu-checkbox-label" for="${this.ID}"></label></div>${t}`,this.container.appendChild(s),s.addEventListener("change",i=>{n[r]=i.target.checked,a&&a(i.target.checked)}),s}async addList(t,n,r,a){let s=document.createElement("div");s.className="menu-item";let i="";for(let o of n)i+=`<option value="${o}" ${o===r?"selected":""}>${o}</option>`;return s.innerHTML=`<div class="menu-list"><select name="${this.ID}" class="menu-list-item">${i}</select><label for="${this.ID}"></label></div>${t}`,s.style.fontFamily=document.body.style.fontFamily,s.style.fontSize=document.body.style.fontSize,s.style.fontVariant=document.body.style.fontVariant,this.container.appendChild(s),s.addEventListener("change",o=>{a&&a(n[o.target.selectedIndex])}),s}addRange(t,n,r,a,s,i,o){let l=document.createElement("div");return l.className="menu-item",l.innerHTML=`<input class="menu-range" type="range" id="${this.newID}" min="${a}" max="${s}" step="${i}" value="${n[r]}">${t}`,this.container.appendChild(l),l.addEventListener("change",c=>{n[r]=parseInt(c.target.value)===parseFloat(c.target.value)?parseInt(c.target.value):parseFloat(c.target.value),c.target.setAttribute("value",c.target.value),o&&o(c.target.value)}),l.input=l.children[0],l}addHTML(t){let n=document.createElement("div");return n.className="menu-item",n.id=this.newID,t&&(n.innerHTML=t),this.container.appendChild(n),n}addButton(t,n,r){let a=document.createElement("button");return a.className="menu-item menu-button",a.style.fontFamily=document.body.style.fontFamily,a.style.fontSize=document.body.style.fontSize,a.style.fontVariant=document.body.style.fontVariant,a.type="button",a.id=this.newID,a.innerText=t,this.container.appendChild(a),a.addEventListener("click",()=>{a.innerText===t?a.innerText=n:a.innerText=t,r&&r(a.innerText!==t)}),a}addValue(t,n,r=""){let a=document.createElement("div");return a.className="menu-item",a.id=`menu-val-${t}`,a.innerText=`${t}: ${n}${r}`,this.container.appendChild(a),a}updateValue(t,n,r=""){let a=document.getElementById(`menu-val-${t}`);a?a.innerText=`${t}: ${n}${r}`:this.addValue(t,n)}addChart(t,n,r=150,a=40,s){s&&(wt.chartColor=s);let i=document.createElement("div");return i.className="menu-item menu-chart-title",i.id=this.newID,i.innerHTML=`<font color=${wt.chartColor}>${t}</font><canvas id="menu-canvas-${n}" class="menu-chart-canvas" width="${r}px" height="${a}px"></canvas>`,this.container.appendChild(i),i}async updateChart(t,n){if(!n||n.length===0)return;let r=document.getElementById(`menu-canvas-${t}`);if(!r)return;let a=r.getContext("2d");a.fillStyle=wt.background,a.fillRect(0,0,r.width,r.height);let s=r.width/n.length,i=1+Math.max(...n),o=r.height/i;for(let l=0;l<n.length;l++){let c=a.createLinearGradient(0,(i-n[l])*o,0,0);c.addColorStop(.1,wt.chartColor),c.addColorStop(.4,wt.background),a.fillStyle=c,a.fillRect(l*s,0,s-4,r.height),a.fillStyle=wt.background,a.font=`${s/1.5}px "Segoe UI"`,a.fillText(Math.round(n[l]),l*s+1,r.height-1,s-1)}}},Uc=yv;var ese=`
|
|
#gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }
|
|
#gl-bench div { position: relative; display: block; margin: 4px; padding: 0 7px 0 10px; background: darkslategray; border-radius: 0.2rem; cursor: pointer; opacity: 0.9; }
|
|
#gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }
|
|
#gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }
|
|
#gl-bench .gl-mem { font-size: 12px; fill: white; }
|
|
#gl-bench .gl-fps { font-size: 13px; fill: white; }
|
|
#gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }
|
|
#gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }
|
|
#gl-bench rect { fill: black; }
|
|
#gl-bench .opacity { stroke: black; }
|
|
`,tse=`
|
|
<div class="gl-box">
|
|
<svg viewBox="0 0 55 60">
|
|
<text x="27" y="56" class="gl-fps">00 FPS</text>
|
|
<text x="30" y="8" class="gl-mem"></text>
|
|
<rect x="0" y="14" rx="4" ry="4" width="55" height="32"></rect>
|
|
<polyline class="gl-chart"></polyline>
|
|
</svg>
|
|
<svg viewBox="0 0 14 60" class="gl-cpu-svg">
|
|
<line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
|
|
<line x1="7" y1="38" x2="7" y2="11" class="gl-cpu" stroke-dasharray="0 27"/>
|
|
<path d="M5.35 43c-.464 0-.812.377-.812.812v1.16c-.783.1972-1.421.812-1.595 1.624h-1.16c-.435 0-.812.348-.812.812s.348.812.812.812h1.102v1.653H1.812c-.464 0-.812.377-.812.812 0 .464.377.812.812.812h1.131c.1943.783.812 1.392 1.595 1.595v1.131c0 .464.377.812.812.812.464 0 .812-.377.812-.812V53.15h1.653v1.073c0 .464.377.812.812.812.464 0 .812-.377.812-.812v-1.131c.783-.1943 1.392-.812 1.595-1.595h1.131c.464 0 .812-.377.812-.812 0-.464-.377-.812-.812-.812h-1.073V48.22h1.102c.435 0 .812-.348.812-.812s-.348-.812-.812-.812h-1.16c-.1885-.783-.812-1.421-1.595-1.624v-1.131c0-.464-.377-.812-.812-.812-.464 0-.812.377-.812.812v1.073H6.162v-1.073c0-.464-.377-.812-.812-.812zm.58 3.48h2.088c.754 0 1.363.609 1.363 1.363v2.088c0 .754-.609 1.363-1.363 1.363H5.93c-.754 0-1.363-.609-1.363-1.363v-2.088c0-.754.609-1.363 1.363-1.363z" style="fill: grey"></path>
|
|
</svg>
|
|
<svg viewBox="0 0 14 60" class="gl-gpu-svg">
|
|
<line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
|
|
<line x1="7" y1="38" x2="7" y2="11" class="gl-gpu" stroke-dasharray="0 27"/>
|
|
<path d="M1.94775 43.3772a.736.736 0 10-.00416 1.472c.58535.00231.56465.1288.6348.3197.07015.18975.04933.43585.04933.43585l-.00653.05405v8.671a.736.736 0 101.472 0v-1.4145c.253.09522.52785.1495.81765.1495h5.267c1.2535 0 2.254-.9752 2.254-2.185v-3.105c0-1.2075-1.00625-2.185-2.254-2.185h-5.267c-.28865 0-.5635.05405-.8165.1495.01806-.16445.04209-.598-.1357-1.0787-.22425-.6072-.9499-1.2765-2.0125-1.2765zm2.9095 3.6455c.42435 0 .7659.36225.7659.8119v2.9785c0 .44965-.34155.8119-.7659.8119s-.7659-.36225-.7659-.8119v-2.9785c0-.44965.34155-.8119.7659-.8119zm4.117 0a2.3 2.3 0 012.3 2.3 2.3 2.3 0 01-2.3 2.3 2.3 2.3 0 01-2.3-2.3 2.3 2.3 0 012.3-2.3z" style="fill: grey"></path>
|
|
</svg>
|
|
</div>
|
|
`,gv=class{constructor(t,n={}){this.css=ese,this.svg=tse,this.paramLogger=()=>{},this.chartLogger=()=>{},this.chartLen=20,this.chartHz=20,this.names=[],this.cpuAccums=[],this.gpuAccums=[],this.activeAccums=[],this.chart=new Array(this.chartLen),this.now=()=>performance&&performance.now?performance.now():Date.now(),this.updateUI=()=>{[].forEach.call(this.nodes["gl-gpu-svg"],o=>o.style.display=this.trackGPU?"inline":"none")},Object.assign(this,n),this.detected=0,this.finished=[],this.isFramebuffer=0,this.frameId=0;let r,a=0,s,i=o=>{++a<20?r=requestAnimationFrame(i):(this.detected=Math.ceil(1e3*a/(o-s)/70),cancelAnimationFrame(r)),s||(s=o)};if(requestAnimationFrame(i),t){let o=async(u,h)=>Promise.resolve(setTimeout(()=>{t.getError();let d=this.now()-u;h.forEach((p,f)=>{p&&(this.gpuAccums[f]+=d)})},0)),l=(u,h,d)=>{let p=h.now();u.apply(d,arguments),h.trackGPU&&h.finished.push(o(p,h.activeAccums.slice(0)))},c="drawElements";t[c]?t[c]=l(t[c],this,t):console.log("bench: cannot attach to webgl function")}if(!this.withoutUI){this.dom||(this.dom=document.body);let o=document.createElement("div");o.id="gl-bench",this.dom.appendChild(o),this.dom.insertAdjacentHTML("afterbegin",'<style id="gl-bench-style">'+this.css+"</style>"),this.dom=o,this.dom.addEventListener("click",()=>{this.trackGPU=!this.trackGPU,this.updateUI()}),this.paramLogger=((l,c,u)=>{let h=["gl-cpu","gl-gpu","gl-mem","gl-fps","gl-gpu-svg","gl-chart"],d={...h};return h.forEach(p=>d[p]=c.getElementsByClassName(p)),this.nodes=d,(p,f,m,A,y,g,b)=>{d["gl-cpu"][p].style.strokeDasharray=(f*.27).toFixed(0)+" 100",d["gl-gpu"][p].style.strokeDasharray=(m*.27).toFixed(0)+" 100",d["gl-mem"][p].innerHTML=u[p]?u[p]:A?"mem: "+A.toFixed(0)+"mb":"",d["gl-fps"][p].innerHTML="FPS: "+y.toFixed(1),l(u[p],f,m,A,y,g,b)}})(this.paramLogger,this.dom,this.names),this.chartLogger=((l,c)=>{let u={"gl-chart":c.getElementsByClassName("gl-chart")};return(h,d,p)=>{let f="",m=d.length;for(let A=0;A<m;A++){let y=(p+A+1)%m;d[y]!==void 0&&(f=f+" "+(55*A/(m-1)).toFixed(1)+","+(45-d[y]*22/60/this.detected).toFixed(1))}u["gl-chart"][h].setAttribute("points",f),l(this.names[h],d,p)}})(this.chartLogger,this.dom)}}addUI(t){this.names.indexOf(t)===-1&&(this.names.push(t),this.dom&&(this.dom.insertAdjacentHTML("beforeend",this.svg),this.updateUI()),this.cpuAccums.push(0),this.gpuAccums.push(0),this.activeAccums.push(!1))}nextFrame(t){this.frameId++;let n=t||this.now();if(this.frameId<=1)this.paramFrame=this.frameId,this.paramTime=n;else{let r=n-this.paramTime;if(r>=1e3){let a=this.frameId-this.paramFrame,s=a/r*1e3;for(let i=0;i<this.names.length;i++){let o=this.cpuAccums[i]/r*100,l=this.gpuAccums[i]/r*100,c=performance&&performance.memory?performance.memory.usedJSHeapSize/(1<<20):0;this.paramLogger(i,o,l,c,s,r,a),this.cpuAccums[i]=0,Promise.all(this.finished).then(()=>{this.gpuAccums[i]=0,this.finished=[]})}this.paramFrame=this.frameId,this.paramTime=n}}if(!this.detected||!this.chartFrame)this.chartFrame=this.frameId,this.chartTime=n,this.circularId=0;else{let r=n-this.chartTime,a=this.chartHz*r/1e3;for(;--a>0&&this.detected;){let i=(this.frameId-this.chartFrame)/r*1e3;this.chart[this.circularId%this.chartLen]=i;for(let o=0;o<this.names.length;o++)this.chartLogger(o,this.chart,this.circularId);this.circularId++,this.chartFrame=this.frameId,this.chartTime=n}}}begin(t){this.updateAccums(t)}end(t){this.updateAccums(t)}updateAccums(t){let n=this.names.indexOf(t);n===-1&&(n=this.names.length,this.addUI(t));let r=this.now(),a=r-this.t0;for(let s=0;s<n+1;s++)this.activeAccums[s]&&(this.cpuAccums[s]+=a);this.activeAccums[n]=!this.activeAccums[n],this.t0=r}},xv=gv;var jr={},se=new mv(jr),ie={baseColor:"rgba(173, 216, 230, 0.3)",baseBackground:"rgba(50, 50, 50, 1)",baseLabel:"rgba(173, 216, 230, 1)",baseFontProto:'small-caps {size} "Segoe UI"',baseLineWidth:12,crop:!0,columns:2,busy:!1,facing:!0,useWorker:!1,worker:"worker.js",samples:["../assets/sample6.jpg","../assets/sample1.jpg","../assets/sample4.jpg","../assets/sample5.jpg","../assets/sample3.jpg","../assets/sample2.jpg"],compare:"../assets/sample-me.jpg",drawBoxes:!0,drawPoints:!1,drawPolygons:!0,fillPolygons:!1,useDepth:!0,console:!0,maxFPSframes:10,modelsPreload:!0,menuWidth:0,menuHeight:0,camera:{},detectFPS:[],drawFPS:[],buffered:!1,drawThread:null,detectThread:null,framesDraw:0,framesDetect:0,bench:!1},xe={},p1,Ii,f1={};function nse(...e){if(!Array.isArray(e))return e;let t="";for(let n of e)typeof n=="object"?t+=JSON.stringify(n).replace(/{|}|"|\[|\]/g,"").replace(/,/g,", "):t+=n;return t}function Dn(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;ie.console&&console.log(n,...e)}function jn(e){document.getElementById("status").innerText=e}var Ni;async function rse(e){var n,r,a,s;if(document.getElementById("compare-container").style.display=se.config.face.embedding.enabled?"block":"none",!se.config.face.embedding.enabled||((n=e==null?void 0:e.face)==null?void 0:n.length)>0&&((r=e==null?void 0:e.face[0].embedding)==null?void 0:r.length)!==192)return;Ni||(Ni=e,document.getElementById("compare-canvas").getContext("2d").drawImage(Ni.canvas,0,0,200,200));let t=se.simmilarity((a=Ni==null?void 0:Ni.face[0])==null?void 0:a.embedding,(s=e==null?void 0:e.face[0])==null?void 0:s.embedding);document.getElementById("simmilarity").innerText=`simmilarity: ${Math.trunc(1e3*t)/10}%`}var wv=performance.now();async function m1(e){let t=f1,n=document.getElementById("canvas");ie.drawFPS.push(1e3/(performance.now()-wv)),ie.drawFPS.length>ie.maxFPSframes&&ie.drawFPS.shift(),wv=performance.now(),await xe.process.updateChart("FPS",ie.detectFPS),(ie.buffered||!t.canvas)&&(t.canvas=await se.image(e,jr));let r=n.getContext("2d");r.fillStyle=ie.baseBackground,r.fillRect(0,0,n.width,n.height),t.canvas?(t.canvas.width!==n.width&&(n.width=t.canvas.width),t.canvas.height!==n.height&&(n.height=t.canvas.height),r.drawImage(t.canvas,0,0,t.canvas.width,t.canvas.height,0,0,t.canvas.width,t.canvas.height)):r.drawImage(e,0,0,e.width,e.height,0,0,n.width,n.height),await Bc.face(t.face,n,ie,se.facemesh.triangulation),await Bc.body(t.body,n,ie),await Bc.hand(t.hand,n,ie),await Bc.gesture(t.gesture,n,ie),await rse(t);let a=se.tf.engine(),s=a.backendInstance?`gpu: ${(a.backendInstance.numBytesInGPU?a.backendInstance.numBytesInGPU:0).toLocaleString()} bytes`:"",i=`system: ${a.state.numBytes.toLocaleString()} bytes ${s} | tensors: ${a.state.numTensors.toLocaleString()}`,o=t.canvas?`processing: ${t.canvas.width} x ${t.canvas.height}`:"",l=Math.trunc(10*ie.detectFPS.reduce((h,d)=>h+d,0)/ie.detectFPS.length)/10,c=Math.trunc(10*ie.drawFPS.reduce((h,d)=>h+d,0)/ie.drawFPS.length)/10,u=ie.detectFPS.length>5&&l<5?'<font color="lightcoral">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>':"";document.getElementById("log").innerHTML=`
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video: ${ie.camera.name} | facing: ${ie.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ie.camera.width} x ${ie.camera.height} ${o}<br>
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backend: ${se.tf.getBackend()} | ${i}<br>
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performance: ${nse(t.performance)}ms FPS process:${l} refresh:${c}<br>
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${u}<br>
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|
`,ie.framesDraw++,ie.lastFrame=performance.now(),ie.buffered?ie.drawThread=requestAnimationFrame(()=>m1(e,n)):!ie.buffered&&ie.drawThread&&(Dn("stopping buffered refresh"),cancelAnimationFrame(ie.drawThread),ie.drawThread=null)}async function A1(){var c;if(ie.busy)return null;ie.busy=!0;let e=document.getElementById("video"),t=document.getElementById("canvas"),n=document.getElementById("log"),r=e.srcObject?e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused:!1,a="";if(jn("setting up camera"),!navigator.mediaDevices)return a="camera access not supported",n.innerText+=`
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${a}`,Dn(a),jn(a),ie.busy=!1,a;let s,i={audio:!1,video:{facingMode:ie.facing?"user":"environment",resizeMode:ie.crop?"crop-and-scale":"none"}};window.innerWidth>window.innerHeight?i.video.width={ideal:window.innerWidth}:i.video.height={ideal:window.innerHeight-document.getElementById("menubar").offsetHeight};try{s=await navigator.mediaDevices.getUserMedia(i)}catch(u){return u.name==="PermissionDeniedError"||u.name==="NotAllowedError"?a="camera permission denied":u.name==="SourceUnavailableError"?a="camera not available":a=`camera error: ${u.message||u}`,n.innerText+=`
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${a}`,jn(a),Dn("camera error:",u),ie.busy=!1,a}if(s)e.srcObject=s;else return ie.busy=!1,"camera stream empty";let o=s.getVideoTracks()[0],l=o.getSettings();return ie.camera={name:(c=o.label)==null?void 0:c.toLowerCase(),width:l.width,height:l.height,facing:l.facingMode==="user"?"front":"back"},new Promise(u=>{e.onloadeddata=async()=>{e.width=e.videoWidth,e.height=e.videoHeight,t.width=e.width,t.height=e.height,t.style.width=t.width>t.height?"100vw":"",t.style.height=t.width>t.height?"":"100vh",ie.menuWidth.input.setAttribute("value",e.width),ie.menuHeight.input.setAttribute("value",e.height);let h=Math.trunc(window.devicePixelRatio*(8+4*t.width/window.innerWidth));ie.baseFont=ie.baseFontProto.replace(/{size}/,`${h}px`),ie.baseLineHeight=h+2,r&&e.play(),r&&!ie.detectThread&&Hc(e,t),ie.busy=!1,jn(""),u()}})}function _v(){if(!Ii){let e=null;Ii=new xv(e,{trackGPU:!1,chartHz:20,chartLen:20}),Ii.begin()}}function ase(e,t,n,r){p1||(Dn("creating worker thread"),p1=new Worker(ie.worker,{type:"module"}),p1.addEventListener("message",a=>{a.data.result.performance&&a.data.result.performance.total&&ie.detectFPS.push(1e3/a.data.result.performance.total),ie.detectFPS.length>ie.maxFPSframes&&ie.detectFPS.shift(),ie.bench&&(Ii||_v(),Ii.nextFrame(r)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=ie.bench?"block":"none"),f1=a.data.result,ie.framesDetect++,ie.drawThread||m1(e),ie.detectThread=requestAnimationFrame(s=>Hc(e,n,s))})),p1.postMessage({image:t.data.buffer,width:n.width,height:n.height,userConfig:jr},[t.data.buffer])}function Hc(e,t,n){var a;if(!(e.srcObject&&e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState>2&&!e.paused)&&e.srcObject){ie.drawThread&&cancelAnimationFrame(ie.drawThread),ie.detectThread&&cancelAnimationFrame(ie.detectThread),ie.drawThread=null,ie.detectThread=null,e.paused?Dn("camera paused"):e.srcObject.getVideoTracks()[0].readyState==="live"&&e.readyState<=2?setTimeout(()=>Hc(e,t),500):Dn(`camera not ready: track state: ${(a=e.srcObject)==null?void 0:a.getVideoTracks()[0].readyState} stream state: ${e.readyState}`),clearTimeout(ie.drawThread),ie.drawThread=null,Dn("frame statistics: process:",ie.framesDetect,"refresh:",ie.framesDraw),Dn("memory",se.tf.engine().memory());return}if(jn(""),ie.useWorker){let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(t.width,t.height):document.createElement("canvas");s.width=t.width,s.height=t.height;let i=s.getContext("2d");i.drawImage(e,0,0,e.width,e.height,0,0,t.width,t.height);let o=i.getImageData(0,0,t.width,t.height);ase(e,o,t,jr,n)}else se.detect(e,jr).then(s=>{s.performance&&s.performance.total&&ie.detectFPS.push(1e3/s.performance.total),ie.detectFPS.length>ie.maxFPSframes&&ie.detectFPS.shift(),ie.bench&&(Ii||_v(),Ii.nextFrame(n)),document.getElementById("gl-bench")&&(document.getElementById("gl-bench").style.display=ie.bench?"block":"none"),s.error?(Dn(s.error),document.getElementById("log").innerText+=`
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Human error: ${s.error}`):(f1=s,ie.drawThread||m1(e),ie.framesDetect++,ie.detectThread=requestAnimationFrame(i=>Hc(e,t,i)))})}async function sse(e){return new Promise(t=>{let n=new Image;n.onload=async()=>{Dn("Processing image:",n.src);let r=document.getElementById("canvas");n.width=n.naturalWidth,n.height=n.naturalHeight,r.width=se.config.filter.width&&se.config.filter.width>0?se.config.filter.width:n.naturalWidth,r.height=se.config.filter.height&&se.config.filter.height>0?se.config.filter.height:n.naturalHeight,f1=await se.detect(n,jr),await m1(n);let s=document.createElement("canvas");s.className="thumbnail",s.width=window.innerWidth/(ie.columns+.1),s.height=r.height/(window.innerWidth/s.width),s.getContext("2d").drawImage(r,0,0,r.width,r.height,0,0,s.width,s.height),document.getElementById("samples-container").appendChild(s),n.src="",t(!0)},n.src=e})}async function bv(){jr.videoOptimized=!0,document.getElementById("samples-container").style.display="none",document.getElementById("canvas").style.display="block";let e=document.getElementById("video"),t=document.getElementById("canvas");if(e.srcObject!==null&&!e.paused)document.getElementById("play").style.display="block",document.getElementById("btnStart").className="button button-start",document.getElementById("btnStart").innerHTML="start<br>video",jn("paused"),e.pause();else{let n=await A1();if(n)jn(n);else{document.getElementById("play").style.display="none";for(let r of Object.values(xe))r.hide();jn(""),document.getElementById("btnStart").className="button button-stop",document.getElementById("btnStart").innerHTML="pause<br>video",await e.play(),ie.detectThread||Hc(e,t)}}}async function ise(){document.getElementById("play").style.display="none",jr.videoOptimized=!1;let e=Math.trunc(window.devicePixelRatio*(8+4*ie.columns));ie.baseFont=ie.baseFontProto.replace(/{size}/,`${e}px`),ie.baseLineHeight=e+2,document.getElementById("canvas").style.display="none",document.getElementById("samples-container").style.display="block",Dn("Running detection of sample images"),jn("processing images"),document.getElementById("samples-container").innerHTML="";for(let t of ie.samples)await sse(t);jn("")}function ose(){let e=[];window.innerWidth>800?e=[`${document.getElementById("btnDisplay").offsetLeft-50}px`,`${document.getElementById("btnImage").offsetLeft-50}px`,`${document.getElementById("btnProcess").offsetLeft-50}px`,`${document.getElementById("btnModel").offsetLeft-50}px`]:e=["0rem","11rem","21.1rem","33rem"],xe.display=new Uc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[0]}),xe.display.addBool("perf monitor",ie,"bench",t=>ie.bench=t),xe.display.addBool("buffered output",ie,"buffered",t=>ie.buffered=t),xe.display.addBool("crop & scale",ie,"crop",t=>{ie.crop=t,A1()}),xe.display.addBool("camera facing",ie,"facing",t=>{ie.facing=t,A1()}),xe.display.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.display.addBool("use 3D depth",ie,"useDepth"),xe.display.addBool("draw boxes",ie,"drawBoxes"),xe.display.addBool("draw polygons",ie,"drawPolygons"),xe.display.addBool("Fill Polygons",ie,"fillPolygons"),xe.display.addBool("draw points",ie,"drawPoints"),xe.image=new Uc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[1]}),xe.image.addBool("enabled",se.config.filter,"enabled",t=>se.config.filter.enabled=t),ie.menuWidth=xe.image.addRange("image width",se.config.filter,"width",0,3840,10,t=>se.config.filter.width=parseInt(t)),ie.menuHeight=xe.image.addRange("image height",se.config.filter,"height",0,2160,10,t=>se.config.filter.height=parseInt(t)),xe.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.image.addRange("brightness",se.config.filter,"brightness",-1,1,.05,t=>se.config.filter.brightness=parseFloat(t)),xe.image.addRange("contrast",se.config.filter,"contrast",-1,1,.05,t=>se.config.filter.contrast=parseFloat(t)),xe.image.addRange("sharpness",se.config.filter,"sharpness",0,1,.05,t=>se.config.filter.sharpness=parseFloat(t)),xe.image.addRange("blur",se.config.filter,"blur",0,20,1,t=>se.config.filter.blur=parseInt(t)),xe.image.addRange("saturation",se.config.filter,"saturation",-1,1,.05,t=>se.config.filter.saturation=parseFloat(t)),xe.image.addRange("hue",se.config.filter,"hue",0,360,5,t=>se.config.filter.hue=parseInt(t)),xe.image.addRange("pixelate",se.config.filter,"pixelate",0,32,1,t=>se.config.filter.pixelate=parseInt(t)),xe.image.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.image.addBool("negative",se.config.filter,"negative",t=>se.config.filter.negative=t),xe.image.addBool("sepia",se.config.filter,"sepia",t=>se.config.filter.sepia=t),xe.image.addBool("vintage",se.config.filter,"vintage",t=>se.config.filter.vintage=t),xe.image.addBool("kodachrome",se.config.filter,"kodachrome",t=>se.config.filter.kodachrome=t),xe.image.addBool("technicolor",se.config.filter,"technicolor",t=>se.config.filter.technicolor=t),xe.image.addBool("polaroid",se.config.filter,"polaroid",t=>se.config.filter.polaroid=t),xe.process=new Uc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[2]}),xe.process.addList("backend",["cpu","webgl","wasm","humangl"],se.config.backend,t=>se.config.backend=t),xe.process.addBool("async operations",se.config,"async",t=>se.config.async=t),xe.process.addBool("enable profiler",se.config,"profile",t=>se.config.profile=t),xe.process.addBool("memory shield",se.config,"deallocate",t=>se.config.deallocate=t),xe.process.addBool("use web worker",ie,"useWorker"),xe.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.process.addLabel("model parameters"),xe.process.addRange("max objects",se.config.face.detector,"maxFaces",1,50,1,t=>{se.config.face.detector.maxFaces=parseInt(t),se.config.body.maxDetections=parseInt(t),se.config.hand.maxHands=parseInt(t)}),xe.process.addRange("skip frames",se.config.face.detector,"skipFrames",0,50,1,t=>{se.config.face.detector.skipFrames=parseInt(t),se.config.face.emotion.skipFrames=parseInt(t),se.config.face.age.skipFrames=parseInt(t),se.config.hand.skipFrames=parseInt(t)}),xe.process.addRange("min confidence",se.config.face.detector,"minConfidence",0,1,.05,t=>{se.config.face.detector.minConfidence=parseFloat(t),se.config.face.gender.minConfidence=parseFloat(t),se.config.face.emotion.minConfidence=parseFloat(t),se.config.hand.minConfidence=parseFloat(t)}),xe.process.addRange("score threshold",se.config.face.detector,"scoreThreshold",.1,1,.05,t=>{se.config.face.detector.scoreThreshold=parseFloat(t),se.config.hand.scoreThreshold=parseFloat(t),se.config.body.scoreThreshold=parseFloat(t)}),xe.process.addRange("overlap",se.config.face.detector,"iouThreshold",.1,1,.05,t=>{se.config.face.detector.iouThreshold=parseFloat(t),se.config.hand.iouThreshold=parseFloat(t)}),xe.process.addBool("detection rotation",se.config.face.detector,"rotation",t=>{se.config.face.detector.rotation=t,se.config.hand.rotation=t}),xe.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.process.addButton("process sample images","process images",()=>ise()),xe.process.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.process.addChart("FPS","FPS"),xe.models=new Uc(document.body,"",{top:`${document.getElementById("menubar").offsetHeight}px`,left:e[3]}),xe.models.addBool("face detect",se.config.face,"enabled",t=>se.config.face.enabled=t),xe.models.addBool("face mesh",se.config.face.mesh,"enabled",t=>se.config.face.mesh.enabled=t),xe.models.addBool("face iris",se.config.face.iris,"enabled",t=>se.config.face.iris.enabled=t),xe.models.addBool("face age",se.config.face.age,"enabled",t=>se.config.face.age.enabled=t),xe.models.addBool("face gender",se.config.face.gender,"enabled",t=>se.config.face.gender.enabled=t),xe.models.addBool("face emotion",se.config.face.emotion,"enabled",t=>se.config.face.emotion.enabled=t),xe.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.models.addBool("body pose",se.config.body,"enabled",t=>se.config.body.enabled=t),xe.models.addBool("hand pose",se.config.hand,"enabled",t=>se.config.hand.enabled=t),xe.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.models.addBool("gestures",se.config.gesture,"enabled",t=>se.config.gesture.enabled=t),xe.models.addHTML('<hr style="border-style: inset; border-color: dimgray">'),xe.models.addBool("face compare",se.config.face.embedding,"enabled",t=>{se.config.face.embedding.enabled=t,Ni=null}),document.getElementById("btnDisplay").addEventListener("click",t=>xe.display.toggle(t)),document.getElementById("btnImage").addEventListener("click",t=>xe.image.toggle(t)),document.getElementById("btnProcess").addEventListener("click",t=>xe.process.toggle(t)),document.getElementById("btnModel").addEventListener("click",t=>xe.models.toggle(t)),document.getElementById("btnStart").addEventListener("click",()=>bv()),document.getElementById("play").addEventListener("click",()=>bv())}async function lse(){Dn("Demo starting ..."),Dn("Browser:",navigator==null?void 0:navigator.userAgent),ose(),document.getElementById("log").innerText=`Human: version ${se.version}`,ie.modelsPreload&&!ie.useWorker&&(jn("loading"),await se.load(jr)),ie.useWorker||(jn("initializing"),await se.warmup(jr)),jn("human: ready"),document.getElementById("loader").style.display="none",document.getElementById("play").style.display="block",Dn("Demo ready...")}window.onload=lse;window.onresize=A1;
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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.
|
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* You may obtain a copy of the License at
|
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*
|
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
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* Unless required by applicable law or agreed to in writing, software
|
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* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
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* =============================================================================
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*/
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/**
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* @license
|
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* Copyright 2018 Google LLC
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*
|
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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|
* =============================================================================
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|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
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* 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 See the LICENSE file. */
|
|
//# sourceMappingURL=demo-browser-index.js.map
|