mirror of https://github.com/vladmandic/human
5546 lines
1.4 MiB
5546 lines
1.4 MiB
/*
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Human
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,nr(`Initialization of backend ${e} failed`),nr(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return nr(`Initialization of backend ${e} failed`),nr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Tc.nextTensorId++}nextVariableId(){return Tc.nextVariableId++}clone(e){let t=L.runKernel(ro,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return L.runKernel(Ua,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(oh(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=Fg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Fg(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=oh(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let A=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,y);let x=y.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:k,dtype:S}=b;return this.makeTensorFromDataId(v,k,S)});if(s){let b=this.getTensorsForGradient(h,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,d=Fg(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,u,t,d,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=kg(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,u)=>a[u]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&Jr(e[0])&&(r=e.map(i=>wc(i)));let a=s.write(r,t,n),o=new Ge(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=p5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Ge(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new Cc(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*gg(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 Cc||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*gg(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=kg(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((u,c)=>{if(u==null){let d=n[c],p=wp(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return u}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}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=_g(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(M(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(r instanceof Ge,()=>"The result y returned by f() must be a tensor.");let a=BC(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let o={};o[r.id]=n==null?YC(r.shape):n,WC(o,a,l=>this.tidy(l),JC);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:r,grads:i}})}customGrad(e){return M(Qr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(o=>o instanceof Ge),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((o,i)=>{s[i]=o});let r=(o,i)=>(n=e(...t,i),M(n.value instanceof Ge,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),M(Qr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let l=n.gradFunc(o,i),u=Array.isArray(l)?l:[l];M(u.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(...)."),M(u.every(d=>d instanceof Ge),()=>"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 c={};return u.forEach((d,p)=>{c[p]=()=>d}),c};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}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=vc(),n=await this.backend.time(e);return n.wallMs=vc()-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 R5;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}};Tc.nextTensorId=0;Tc.nextVariableId=0;function YC(e){let t=Ag(zt(e),"float32");return L.makeTensor(t,e,"float32")}function D5(){let e=y5();if(e._tfengine==null){let t=new A5(e);e._tfengine=new Tc(t)}return kC(e._tfengine.ENV),GC(()=>e._tfengine),e._tfengine}var L=D5();function JC(e,t){let n={a:e,b:t};return L.runKernel(ea,n)}var Nc={};Le(Nc,{isBrowser:()=>_5,isMobile:()=>eT});function QC(){return typeof navigator!="undefined"&&navigator!=null}function eT(e){if(e||QC()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let n=e;return n.userAgentData&&n.userAgentData.mobile}return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function _5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Ls=Y();Ls.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. 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Actual: ${r}.
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Expected: ${a}.`);for(let o=0;o<a.length;++o){let i=r[o],l=a[o];if(!n(i,l))throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${l}.
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Actual: ${r}.
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Expected: ${a}.`)}}function f9(e,t){e().then(()=>t.fail(),()=>t())}function m9(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Jr(e)||Jr(e[0])||Jr(t)||Jr(t[0])?Yg(e,n,(s,r)=>s==r):Yg(e,t,(s,r)=>Jg(s,r,0))}function g9(e,t,n){if(n==null&&(n=Zg()),!Jg(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Jg(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function A9(e,t,n){for(let s=0;s<e.length;s++)if(e[s]<t||e[s]>n)throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`)}function y9(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function Ab(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?Ab(n):e[t]=wc(n)}return e}var gh="3.9.0";function yb(){Y().set("PROD",!0)}function x9(){Y().set("DEBUG",!0)}function b9(){Y().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function 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with dtype ${a.dtype}. `)}),n.length===1)return Bs(n[0]);let s=n,r={axis:t};return L.runKernel(Zi,s,r)}var gt=W({concat_:Q9});function eN(e){let n={x:F(e,"x","sigmoid")};return L.runKernel(To,n)}var Un=W({sigmoid_:eN});function tN(e,t,n){let s=F(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return L.runKernel(Nl,r,a)}var _e=W({slice_:tN});function nN(e){let n={x:F(e,"x","tanh")};return L.runKernel($o,n)}var jo=W({tanh_:nN});function sN(e,t,n,s,r,a){let o=F(e,"forgetBias","basicLSTMCell"),i=F(t,"lstmKernel","basicLSTMCell"),l=F(n,"lstmBias","basicLSTMCell"),u=F(s,"data","basicLSTMCell"),c=F(r,"c","basicLSTMCell"),d=F(a,"h","basicLSTMCell"),p=gt([u,d],1),h=Ue(p,i),f=le(h,l),m=f.shape[0],g=f.shape[1]/4,A=[m,g],y=_e(f,[0,0],A),x=_e(f,[0,g],A),b=_e(f,[0,g*2],A),v=_e(f,[0,g*3],A),k=le(z(Un(y),jo(x)),z(c,Un(le(o,b)))),S=z(jo(k),Un(v));return[k,S]}var rN=W({basicLSTMCell_:sN});function aN(e,t,n){let s=F(e,"x","batchToSpaceND"),r=t.reduce((i,l)=>i*l);M(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),M(s.shape[0]%r==0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return L.runKernel(Ki,a,o)}var $c=W({batchToSpaceND_:aN});function oN(e){let t;return e.rank===0||e.rank===1?t=V(e,[1,1,1,e.size]):e.rank===2?t=V(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=V(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function iN(e,t,n,s,r,a){a==null&&(a=.001);let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;s!=null&&(c=F(s,"offset","batchNorm")),M(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${u.rank}.`),c!=null&&M(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),qo(o,i,l,c,u,a)}var kb=W({batchNorm2d_:lN});function uN(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(s,"offset","batchNorm")),M(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),M(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),qo(o,i,l,c,u,a)}var Ib=W({batchNorm3d_:uN});function cN(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),u;r!=null&&(u=F(r,"scale","batchNorm"));let c;return s!=null&&(c=F(s,"offset","batchNorm")),M(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),M(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),qo(o,i,l,c,u,a)}var Sb=W({batchNorm4d_:cN});function dN(e,t,n){let s=F(e,"x","bincount"),r=F(t,"weights","bincount");M(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),M(n>=0,()=>`size must be non-negative, but got 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Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). 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Got strides ${n} and dilations '${a}'`);let p={x:u,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=L.runKernel(Ga,p,h);return c?V(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Rr=W({conv2d_:bN});function vN(e,t,n,s,r="NWC",a=1,o){let i=F(e,"x","conv1d"),l=F(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=V(i,[1,i.shape[0],i.shape[1]])),M(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&M(on(s),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(ir(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),M(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=V(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=V(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Rr(p,d,[1,n],s,"NHWC",[1,a],o);return c?V(g,[g.shape[2],g.shape[3]]):V(g,[g.shape[0],g.shape[2],g.shape[3]])}var kh=W({conv1d_:vN});function wN(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=V(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),M(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];M(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),M(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&M(on(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let p={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=L.runKernel(ja,p,h);return u?V(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var fA=W({conv2DBackpropInput_:wN});function kN(e,t,n,s,r,a){let o=F(e,"x","conv2dTranspose"),i=F(t,"filter","conv2dTranspose");return fA(n,o,i,s,r,"NHWC",a)}var Ih=W({conv2dTranspose_:kN});function IN(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=F(e,"x","conv3d"),i=F(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=V(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),M(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),M(ir(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let c={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=L.runKernel(lc,c,d);return u?V(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var mA=W({conv3d_:IN});function SN(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=V(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],u=o.shape[4];M(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),M(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=L.runKernel(Ep,c,d);return i?V(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Rb=W({conv3DBackpropInput_:SN});function CN(e,t,n,s,r){let a=F(e,"x","conv3dTranspose"),o=F(t,"filter","conv3dTranspose");return Rb(n,a,o,s,r)}var Db=W({conv3dTranspose_:CN});function TN(e){let n={x:F(e,"x","cos")};return L.runKernel(qa,n)}var Oc=W({cos_:TN});function NN(e){let n={x:F(e,"x","cosh")};return L.runKernel(Xa,n)}var Sh=W({cosh_:NN});function EN(e,t=0,n=!1,s=!1){let a={x:F(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return L.runKernel(Ka,a,o)}var Ch=W({cumsum_:EN});function RN(e,t,n,s=!1){let r=F(e,"x","denseBincount"),a=F(t,"weights","denseBincount");M(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),M(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return L.runKernel(Rp,o,i)}var _b=W({denseBincount_:RN});function DN(e,t,n="NHWC"){let s=F(e,"x","depthToSpace"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];M(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${r} and ${t} for depthToSpace with input shape
|
|
${s.shape}`),M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${a} and ${t} for depthToSpace with input shape
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rank ${a.rank}.`),M(on(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=V(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},u={depthRadius:t,bias:n,alpha:s,beta:r},c=L.runKernel(hc,l,u);return i?V(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var kA=W({localResponseNormalization_:iE});function lE(e){let n={x:F(e,"x","log")};return L.runKernel(oo,n)}var ss=W({log_:lE});function uE(e){let n={x:F(e,"x","log1p")};return L.runKernel(pl,n)}var Mc=W({log1p_:uE});function cE(e){return M(Qr(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=F(t,"x","tf.grad","string_or_numeric"),r=n!=null?F(n,"dy","tf.grad"):null;return L.tidy(()=>{let{value:a,grads:o}=L.gradients(()=>e(s),[s],r);return r!=null&&Tn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Eh(o),o[0]})}}function dE(e){return M(Qr(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{M(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let s=Ec(t,"args","tf.grads","string_or_numeric"),r=n!=null?F(n,"dy","tf.grads"):null;return L.tidy(()=>{let{value:a,grads:o}=L.gradients(()=>e(...s),s,r);return r!=null&&Tn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Eh(o),o})}}function pE(e){return M(Qr(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Ge,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Ge,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=L.gradients(()=>e(t),[t],n);return Eh(s),{grad:s[0],value:r}}}function hE(e){return M(Qr(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof Ge),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Ge,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=L.gradients(()=>e(...t),t,n);return n!=null&&Tn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Eh(s.grads),s}}function zb(e,t){M(Qr(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(u=>u instanceof Cc),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in L.registeredVariables)t.push(L.registeredVariables[u])}let s=n?t.filter(u=>!u.trainable):null,r=t.length;t=t.filter(u=>u.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=L.gradients(e,t,null,a);M(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),s!=null&&s.forEach(u=>l[u.name]=null),{value:o,grads:l}}function lr(e){return L.customGrad(e)}function Eh(e){if(e.filter(n=>n==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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s=F(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=F(t,"weights","computeWeightedLoss"));let a=r==null?s:z(s,r);if(n===Dn.NONE)return a;if(n===Dn.SUM)return ke(a);if(n===Dn.MEAN){if(r==null)return _t(a);{let o=s.size/r.size,i=he(ke(a),ke(r));return o>1?he(i,Te(o)):i}}if(n===Dn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return he(ke(a),Te(s.size));{let o=z(r,as(s.shape)),i=pe(ke(Yo(o,Te(0))),"float32");return he(ke(a),i)}}throw Error(`Unknown reduction: ${n}`)}var Fr=W({computeWeightedLoss_:k_});function I_(e,t,n,s=Dn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","absoluteDifference"),a=F(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=F(n,"weights","absoluteDifference")),Tn(r.shape,a.shape,"Error in absoluteDifference: ");let i=Ut(ye(r,a));return Fr(i,o,s)}var S_=W({absoluteDifference_:I_});function C_(e,t,n,s,r=Dn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","cosineDistance"),o=F(t,"predictions","cosineDistance"),i=null;s!=null&&(i=F(s,"weights","cosineDistance")),Tn(a.shape,o.shape,"Error in cosineDistance: ");let l=Te(1),u=ye(l,ke(z(a,o),n,!0));return Fr(u,i,r)}var T_=W({cosineDistance_:C_});function N_(e,t,n,s=Dn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","hingeLoss"),a=F(t,"predictions","hingeLoss"),o=null;n!=null&&(o=F(n,"weights","hingeLoss")),Tn(r.shape,a.shape,"Error in hingeLoss: ");let i=Te(1);r=ye(z(Te(2),r),i);let l=Vs(ye(i,z(r,a)));return Fr(l,o,s)}var E_=W({hingeLoss_:N_});function R_(e,t,n,s=1,r=Dn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","huberLoss"),o=F(t,"predictions","huberLoss"),i=null;n!=null&&(i=F(n,"weights","huberLoss")),Tn(a.shape,o.shape,"Error in huberLoss: ");let l=Te(s),u=Ut(ye(o,a)),c=tu(u,l),d=ye(u,c),p=le(z(Te(.5),ft(c)),z(l,d));return Fr(p,i,r)}var D_=W({huberLoss_:R_});function __(e,t,n,s=1e-7,r=Dn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","logLoss"),o=F(t,"predictions","logLoss"),i=null;n!=null&&(i=F(n,"weights","logLoss")),Tn(a.shape,o.shape,"Error in logLoss: ");let l=Te(1),u=Te(s),c=Ct(z(a,ss(le(o,u)))),d=z(ye(l,a),ss(le(ye(l,o),u))),p=ye(c,d);return Fr(p,i,r)}var F_=W({logLoss_:__});function $_(e,t,n,s=Dn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),a=F(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=F(n,"weights","meanSquaredError")),Tn(r.shape,a.shape,"Error in meanSquaredError: ");let i=Uh(r,a);return Fr(i,o,s)}var O_=W({meanSquaredError_:$_});function P_(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),s=F(t,"logits","sigmoidCrossEntropyWithLogits");Tn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Vs(s),a=z(s,n),o=Mc(ns(Ct(Ut(s))));return le(ye(r,a),o)}function M_(e,t,n,s=0,r=Dn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"multiClassLabels","sigmoidCrossEntropy"),o=F(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","sigmoidCrossEntropy")),Tn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let u=Te(s),c=Te(1),d=Te(.5);a=le(z(a,ye(c,u)),z(d,u))}let l=P_(a,o);return Fr(l,i,r)}var z_=W({sigmoidCrossEntropy_:M_});function L_(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return lr((r,a,o)=>{let l=CA(a,[n],!0),u=ye(pe(a,"float32"),l);o([r,u]);let c=Ct(z(u,r));return{value:ke(c,[n]),gradFunc:(h,f)=>{let[m,g]=f,A=Zo(h.shape,[n]);return[z(V(h,A),ye(pe(m,"float32"),ns(g))),z(V(h,A),ye(ns(g),pe(m,"float32")))]}}})(e,t)}function B_(e,t,n,s=0,r=Dn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"onehotLabels","softmaxCrossEntropy"),o=F(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","softmaxCrossEntropy")),Tn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let u=Te(s),c=Te(1),d=Te(a.shape[1]);a=le(z(a,ye(c,u)),he(u,d))}let l=L_(a,o);return Fr(l,i,r)}var W_=W({softmaxCrossEntropy_:B_});function V_(e,t,n,s){let r=F(e,"indices","sparseFillEmptyRows"),a=F(t,"values","sparseFillEmptyRows"),o=F(n,"denseShape","sparseFillEmptyRows"),i=F(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
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${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},u=L.runKernel(Zp,l);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var U_=W({sparseFillEmptyRows_:V_});function H_(e,t,n){let s=F(e,"inputIndices","sparseReshape"),r=F(t,"inputShape","sparseReshape"),a=F(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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|
${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=L.runKernel(Yp,o);return{outputIndices:i[0],outputShape:i[1]}}var G_=W({sparseReshape_:H_});function j_(e,t,n){let s=F(e,"data","sparseSegmentMean"),r=F(t,"indices","sparseSegmentMean"),a=F(n,"segmentIds","sparseSegmentMean");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
|
|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return L.runKernel(Jp,o)}var q_=W({sparseSegmentMean_:j_});function X_(e,t,n){let s=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum"),a=F(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${a.shape}`);let o={data:s,indices:r,segmentIds:a};return L.runKernel(Qp,o)}var K_=W({sparseSegmentSum_:X_});function Z_(e,t,n,s,r,a,o,i){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:u},p=L.runKernel(th,d,c);return{nGrams:p[0],nGramsSplits:p[1]}}var Y_=W({stringNGrams_:Z_});function J_(e,t,n=!0){let s=F(e,"input","stringSplit","string"),r=F(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=L.runKernel(nh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var Q_=W({stringSplit_:J_});function eF(e,t){let n=F(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return L.runKernel(sh,r,s)}var tF=W({stringToHashBucketFast_:eF}),nF={fft:Gc,ifft:ru,rfft:jc,irfft:Vh},sF={hammingWindow:FD,hannWindow:r3,frame:a3,stft:MD},De={flipLeftRight:WD,grayscaleToRGB:UD,resizeNearestNeighbor:d3,resizeBilinear:c3,rotateWithOffset:GD,cropAndResize:LD,nonMaxSuppression:qD,nonMaxSuppressionAsync:t_,nonMaxSuppressionWithScore:s_,nonMaxSuppressionWithScoreAsync:a_,nonMaxSuppressionPadded:i_,nonMaxSuppressionPaddedAsync:u_,threshold:f_,transform:g_},h3={bandPart:y_,gramSchmidt:b_,qr:w_},rF={absoluteDifference:S_,computeWeightedLoss:Fr,cosineDistance:T_,hingeLoss:E_,huberLoss:D_,logLoss:F_,meanSquaredError:O_,sigmoidCrossEntropy:z_,softmaxCrossEntropy:W_},qc={sparseFillEmptyRows:U_,sparseReshape:G_,sparseSegmentMean:q_,sparseSegmentSum:K_},Yh={stringNGrams:Y_,stringSplit:Q_,stringToHashBucketFast:tF},$r=class extends fb{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else 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$r{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=L.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:H(()=>Ye(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:H(()=>Ye(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;H(()=>{let u=le(z(i,this.rho),z(ft(o),1-this.rho)),c=z(he(yn(le(l,this.epsilon)),yn(le(i,this.epsilon))),o),d=le(z(l,this.rho),z(ft(c),1-this.rho));i.assign(u),l.assign(d);let 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$r{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=L.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:H(()=>Ql(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;H(()=>{let i=le(o,ft(a));o.assign(i);let l=le(z(he(a,yn(le(i,L.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Z(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},$P=0,Qe=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=$P++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Pr(n)+"_"+yf(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let 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Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Gs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return bf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return f1(e?this.trainableWeights:this.weights)}setWeights(e){H(()=>{let t=this.weights;if(t.length!==e.length)throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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ii(t);for(let f=0;f<d.length;++f){if(s!=null){let D=Ah().numTensors;D>s.maxNumTensors&&(s.maxNumTensors=D),D<s.minNumTensors&&(s.minNumTensors=D)}let m=d[f],g=m.sourceLayer;if(g instanceof uu)continue;let A=[],y=[],x=[],b=!1;for(let D of m.inputs){let O=h.getValue(D),E=h.getMask(D);A.push(O),y.push(E),E!=null&&(b=!0),r||(p[D.name]--,p[D.name]===0&&!t.hasKey(D)&&i.indexOf(D.name)===-1&&!O.isDisposed&&D.sourceLayer.stateful!==!0&&x.push(O))}b&&(n=n||{},n.mask=y[0]);let v=vt(g.apply(A,n)),k=null;g.supportsMasking&&(k=g.computeMask(A,y));let S=hM(m),C=Array.isArray(S)?S:[S];for(let D=0;D<C.length;++D){h.hasKey(C[D])||h.add(C[D],v[D],Array.isArray(k)?k[0]:k);let O=i.indexOf(C[D].name);O!==-1&&(l[O]=v[D])}r||Z(x)}return h.disposeMasks(),a?l:l[0]}function dM(e,t){w.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){let r=hv(e[0],t);n=r.sorted,s=r.recipientMap}else{let r=new Set;for(let a of e){let{sorted:o,recipientMap:i}=hv(a,t);for(let l of o)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in i)s[l]==null&&(s[l]=new Set),i[l].forEach(u=>s[l].add(u))}}return{sorted:n,recipientCounts:pM(s)}}function pM(e){let t={};for(let n in e)t[n]=e[n].size;return t}function hv(e,t){let n=new Set,s=[],r={};for(let i of t.names())n.add(i);let a=[],o=[];for(a.push(e);a.length>0;){let i=a[a.length-1];if(n.has(i.name)){a.pop();continue}let l=o[o.length-1]===a.length-1;if(i.inputs.length===0||l)a.pop(),s.push(i),n.add(i.name),l&&o.pop();else{o.push(a.length-1);for(let u of i.inputs)r[u.name]==null&&(r[u.name]=new Set),r[u.name].add(i.name),!n.has(u.name)&&a.push(u)}}return{sorted:s,recipientMap:r}}function hM(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var hr=class extends Qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=yf(A)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],fa(this.inputs).length!==this.inputs.length)throw new G(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let y=A.sourceLayer,x=A.nodeIndex,b=A.tensorIndex;dr(x===0,"input layer has >1 nodes"),dr(b===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let y=this.inputLayers[A];if(!(y instanceof uu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},s={},r={},a={},o=[],i=(A,y,x,b,v,k)=>{(b==null||v==null||k==null)&&(b=A.sourceLayer,v=A.nodeIndex,k=A.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new Gs(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(y.indexOf(S)!==-1)return;this.containerNodes.add(hr.nodeKey(b,v)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let C=S.inboundLayers.length;for(let D=0;D<C;D++){let O=S.inputTensors[D],E=S.inboundLayers[D],R=S.nodeIndices[D],T=S.tensorIndices[D];i(O,y,x,E,R,T)}for(y.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],u=[];for(let A of this.outputs)i(A,l,u);let c=o.slice().reverse();for(let A of c){n[A.id]=A,A.id in t||(t[A.id]=0);let y=t[A.id],x=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];y=Math.max(y,x),s[A.outboundLayer.id]=y,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=y;for(let b=0;b<A.inboundLayers.length;b++){let v=A.inboundLayers[b],k=A.nodeIndices[b],S=v.inboundNodes[k],C=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(y+1,C),n[S.id]=S}}let d={};for(let A in t){let y=t[A];y in d||(d[y]=[]),d[y].push(n[A])}let p={};for(let A in s){let y=s[A];y in p||(p[y]=[]),p[y].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(af);this.layers=[];for(let A of h){let y=p[A];y.sort((x,b)=>{let v=a[x.id],k=a[b.id];return v<k?-1:v>k?1:0});for(let x of y)x instanceof hr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(af);let f=this.inputs.slice(),m=[];for(let A of h)for(let y of d[A]){let x=y.outboundLayer;if(x!=null){for(let b of y.inputTensors)if(f.indexOf(b)===-1)throw new Gs(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of y.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let y=g.filter(x=>x===A).length;if(y!==1)throw new Gs(`The name "${A}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new vf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new G("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new G(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}m1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${k1}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=w1(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return H(()=>{e=vt(e);let n=new ii;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return sd(this.outputs,n,t)})}computeMask(e,t){return H(()=>{e=vt(e);let n;return t==null?n=ti(null,e.length):n=vt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=xf(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(af);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],y=`${m.name}_${g}_${A}`,x=n[y];c.push(x)}let d=u.computeOutputShape(jn(c)),p=xf(d),h=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];dr(i in n),r.push(n[i])}return jn(r)}runInternalGraph(e,t){t==null&&(t=ti(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(af);for(let i of s){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,d=u.inputTensors,p=u.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,A,y;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),A=vt(c.call(x,f)),y=vt(c.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),A=vt(c.call(m,f)),y=vt(c.computeMask(m,g));if(c.activityRegularizer)throw new ze("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=A[x],k=y[x];n[b.id]=[v,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){dr(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),r.push(l),a.push(u)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof hr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=hr.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return H(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=hr.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let c=0;c<a.inboundNodes.length;c++){let d=a.inboundNodes[c],p=hr.nodeKey(a,c),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],A=d.nodeIndices[m],y=d.tensorIndices[m],x=hr.nodeKey(g,A),b=t[x];b==null&&(b=0),f.push([g.name,b,y,h])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=hr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,u,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=hr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,u,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let A=[],y;for(let x of g){let b=x[0],v=x[1],k=x[2];if(y=x[3]==null?{}:x[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=v){o(m,g);return}let C=S.inboundNodes[v];A.push(C.outputTensors[k])}A.length>0&&m.apply(jn(A),y)}function l(m){let g=m.name,A=Ks(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${x}`);o(A,x)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!HO(a);)for(let m of c){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let y of A)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],y=m[2];dr(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[y])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],y=m[2];dr(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[y])}return new e({inputs:d,outputs:p,name:u})}get stateful(){if(this._stateful)throw new G("Container instance unexpectedly has _stateful = true. 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Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let s=n.batchSize==null?32:n.batchSize;S1(s);let r=!0,a=this.standardizeUserDataXY(e,t,r,s);try{let o=a[0].concat(a[1]);this.makeTestFunction();let i=this.testFunction,l=this.testLoop(i,o,s,n.verbose,n.steps);return jn(l)}finally{li(a[0],e),li(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),vM(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(n!=null){if(r=null,t!=null)throw new G(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new G(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new G("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new ii;if(e instanceof Ge&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new G(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)a.add(this.inputs[i],e[i])}else for(let i of this.inputs){let l=e[i.name];if(l==null)throw new G(`No value is provided for the model's input ${i.name}`);a.add(i,l)}let o=sd(r,a);return n?o:o[0]}retrieveSymbolicTensors(e){let t=ti(null,e.length),n=e.length;for(let s of this.layers){let r=Array.isArray(s.output)?s.output:[s.output],a=r.map(o=>o.name);for(let o=0;o<e.length;++o){let i=a.indexOf(e[o]);if(i!==-1&&(t[o]=r[i],n--),n===0)break}if(n===0)break}if(n>0){let s=[];throw t.forEach((r,a)=>{r==null&&s.push(e[a])}),new G(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return H(()=>{let s=this.checkNumSamples(e);if(n)throw new ze("Verbose predictLoop() is not implemented yet.");let r=T1(s,t),a=this.outputs.map(o=>[]);for(let o=0;o<r.length;++o)H(()=>{let l=r[o][0],u=r[o][1],c=rd(e,l,u),d=[];if(Array.isArray(c))for(let h=0;h<c.length;++h)d.push({key:this.inputs[h],value:c[h]});else d.push({key:this.inputs[0],value:c});let p=new ii(d);return sd(this.outputs,p)}).forEach((l,u)=>a[u].push(l));return jn(a.map(o=>gt(o,0)))})}predict(e,t={}){let n=xv(e);wv(n,this.inputNames,this.feedInputShapes,!1);try{let s=t.batchSize==null?32:t.batchSize;return S1(s),this.predictLoop(n,s)}finally{li(n,e)}}predictOnBatch(e){wv(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new Gs("You must compile a model before training/testing. 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For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new G("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 G("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=Z3({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new G(`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 G("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new Mr({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 Gs("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 Gs("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 Gs("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 Gs("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={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("Legacy serialization format not supported yet.");r=t}else w.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."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof pu))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Ks(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("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 G("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}}};pu.className="Sequential";ue.registerClass(pu);function FM(e){return new Mr(e)}function $M(e){return new pu(e)}function OM(e,t){return t==null&&(t={}),RM(e,t)}function Iv(e){return Z3(e)}function PM(e,t){Os.registerCallbackConstructor(e,t)}var Xn=class extends ue.Serializable{getConfig(){return{}}},Sv=class extends Xn{apply(e,t=1){return lP(e,t)}};Sv.className="elu";ue.registerClass(Sv);var Cv=class extends Xn{apply(e){return Mh(e)}};Cv.className="selu";ue.registerClass(Cv);var Tv=class extends Xn{apply(e){return Vs(e)}};Tv.className="relu";ue.registerClass(Tv);var Nv=class extends Xn{apply(e){return H(()=>tu(6,Vs(e)))}};Nv.className="relu6";ue.registerClass(Nv);var Ev=class extends Xn{apply(e){return e}};Ev.className="linear";ue.registerClass(Ev);var Rv=class extends Xn{apply(e){return Un(e)}};Rv.className="sigmoid";ue.registerClass(Rv);var Dv=class extends Xn{apply(e){return cP(e)}};Dv.className="hardSigmoid";ue.registerClass(Dv);var _v=class extends Xn{apply(e){return Ko(e)}};_v.className="softplus";ue.registerClass(_v);var Fv=class extends Xn{apply(e){return uP(e)}};Fv.className="softsign";ue.registerClass(Fv);var $v=class extends Xn{apply(e){return jo(e)}};$v.className="tanh";ue.registerClass($v);var E1=class extends Xn{apply(e,t=-1){return Jo(e,t)}};E1.className="softmax";ue.registerClass(E1);var Ov=class extends Xn{apply(e,t=-1){return Rh(e,t)}};Ov.className="logSoftmax";ue.registerClass(Ov);var Pv=class extends Xn{apply(e,t=1){return H(()=>z(Un(z(e,t)),e))}};Pv.className="swish";ue.registerClass(Pv);var Mv=class extends Xn{apply(e){return H(()=>z(e,jo(Ko(e))))}};Mv.className="mish";ue.registerClass(Mv);function ya(e){return e.getClassName()}function R1(e,t={}){return Kc(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function xa(e){if(e==null){let t={};return t.className="linear",t.config={},R1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},R1(t)}else return e instanceof Xn?e:R1(e)}function D1(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var zv=class extends ue.Serializable{},ad=class extends zv{constructor(e){super();D1(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return H(()=>{let t=Mt([1]);return this.hasL1&&(t=le(t,ke(z(this.l1,Ut(e))))),this.hasL2&&(t=le(t,ke(z(this.l2,Qc(e))))),V(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};ad.className="L1L2";ue.registerClass(ad);function MM(e){return D1(e),new ad({l1:e!=null?e.l1:null,l2:0})}function zM(e){return D1(e),new ad({l2:e!=null?e.l2:null,l1:0})}var Lv={l1l2:"L1L2"};function At(e){return XA(e)}function Bv(e,t={}){return Kc(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Nt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Lv?Lv[e]:e,config:{}};return Bv(n)}else return e instanceof zv?e:Bv(e)}var _1=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=We(e);let n=Vs(e);return this.maxValue!=null&&(n=Hn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};_1.className="ReLU";ue.registerClass(_1);var F1=class extends Qe{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=We(e);return Pc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};F1.className="LeakyReLU";ue.registerClass(F1);var $1=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Tt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Nt(e.alphaRegularizer),this.alphaConstraint=tn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=dt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=We(e),Vc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ft(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:en(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};$1.className="PReLU";ue.registerClass($1);var O1=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new ze(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=We(e);return Jl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};O1.className="ELU";ue.registerClass(O1);var P1=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=We(e);return z(n,pe(Gn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};P1.className="ThresholdedReLU";ue.registerClass(P1);var M1=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new E1().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=We(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}};M1.className="Softmax";ue.registerClass(M1);function hu(e,t,n){if(typeof e=="number")return ti(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!rP(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(a==="channelsFirst"&&(e=Ze(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=kh(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=qs(i,n)),i})}function Vv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=Hs()),Bt(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=z1(e,a);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ha.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Ze(l,[0,3,1,2])),l})}function BM(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=Hs()),Bt(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Wv(e,a);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=mA(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=qs(i,n)),a==="channelsFirst"&&(i=Ze(i,[0,4,1,2,3])),i})}var L1=class extends Qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",L1.verifyArgs(t),this.rank=e,dn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=hu(t.kernelSize,e,"kernelSize"),this.strides=hu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,vs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Bt(this.dataFormat),this.activation=xa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Tt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=tn(t.biasConstraint),this.biasRegularizer=Nt(t.biasRegularizer),this.activityRegularizer=Nt(t.activityRegularizer),this.dilationRate=hu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(dr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!ZA(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ya(this.activation),useBias:this.useBias,biasInitializer:Ft(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:en(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},od=class extends L1{constructor(e,t){super(e,t);this.kernel=null,od.verifyArgs(t),this.filters=t.filters,dn(this.filters,"filters"),this.kernelInitializer=Tt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=tn(t.kernelConstraint),this.kernelRegularizer=Nt(t.kernelRegularizer)}build(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return H(()=>{e=We(e);let n,s=this.bias==null?null:this.bias.read(),r=D3(this.activation.getClassName());if(r!=null&&this.rank===2)n=Vv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=LM(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Vv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=BM(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("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 r=0;r<n.length;++r){let a=Zs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Ft(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:en(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},id=class extends od{constructor(e){super(2,e);id.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ZA(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};id.className="Conv2D";ue.registerClass(id);var ld=class extends od{constructor(e){super(3,e);ld.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};ld.className="Conv3D";ue.registerClass(ld);var B1=class extends id{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=We(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=fr(i,d,u,this.padding),f=fr(l,p,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,1]));let g=Ih(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ze(g,[0,3,1,2])),this.bias!=null&&(g=qs(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=dt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=fr(t[s],i,a,this.padding),t[r]=fr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};B1.className="Conv2DTranspose";ue.registerClass(B1);var W1=class extends ld{constructor(e){super(e);if(this.inputSpec=[new jt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new jt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=We(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=fr(l,f,d,this.padding),y=fr(u,m,p,this.padding),x=fr(c,g,h,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,4,1]));let v=Db(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ze(v,[0,4,1,2,3])),this.bias!==null&&(v=qs(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=dt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=fr(t[s],u,o,this.padding),t[r]=fr(t[r],c,i,this.padding),t[a]=fr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};W1.className="Conv3DTranspose";ue.registerClass(W1);var Uv=class extends od{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Nt(t.depthwiseRegularizer),this.depthwiseConstraint=tn(t.depthwiseConstraint),this.pointwiseInitializer=Tt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Nt(t.pointwiseRegularizer),this.pointwiseConstraint=tn(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new jt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{e=We(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ze(e,[0,2,3,1])),n=FA(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=qs(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ze(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ft(this.depthwiseInitializer),e.pointwiseInitializer=Ft(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=en(this.depthwiseConstraint),e.pointwiseConstraint=en(this.pointwiseConstraint),e}};Uv.className="SeparableConv";var V1=class extends Uv{constructor(e){super(2,e)}};V1.className="SeparableConv2D";ue.registerClass(V1);var Rf=class extends od{constructor(e){super(1,e);Rf.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"&&!ZA(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Rf.className="Conv1D";ue.registerClass(Rf);var U1=class extends Qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=We(e),this.dataFormat==="channelsLast"){let n=lf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return lf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=lf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return lf(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}};U1.className="Cropping2D";ue.registerClass(U1);var H1=class extends Qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,tP(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return H(()=>{let n=We(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ze(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?De.resizeNearestNeighbor(n,[r,a]):De.resizeBilinear(n,[r,a]);return Ze(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?De.resizeNearestNeighbor(n,[r,a]):De.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};H1.className="UpSampling2D";ue.registerClass(H1);function WM(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=Hs()),Bt(r);let o=z1(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Yl(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ze(o,[0,3,1,2])),o})}var G1=class extends L1{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Tt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=tn(e.depthwiseConstraint),this.depthwiseRegularizer=Nt(e.depthwiseRegularizer)}build(e){if(e=dt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return H(()=>{e=We(e);let n=WM(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=qs(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],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Zs(t,this.kernelSize[0],this.padding,this.strides[0]),a=Zs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ft(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=en(this.depthwiseRegularizer),e}};G1.className="DepthwiseConv2D";ue.registerClass(G1);function Hv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function Gv(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(js(2,l));if(t=Ze(t,u),a!=null)throw new ze("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Lt(r,-1)),r=Ze(r,u)),s&&(t=is(t,0),r!=null&&(r=is(r,0)));let c=[],d,p=n,h=t.shape[0],f=Rn(t),m;r!=null&&(m=Rn(r));for(let A=0;A<h;++A){let y=f[A],x=H(()=>e(y,p));if(r==null)d=x[0],p=x[1];else{let b=H(()=>{let v=m[A],k=ye(os(v),v),S=le(z(x[0],v),z(p[0],k)),C=p.map((D,O)=>le(z(x[1][O],v),z(D,k)));return{output:S,newStates:C}});d=b.output,p=b.newStates}i&&c.push(d)}let g;return i&&(g=xn(c,1)),[d,g,p]})}var mr=class extends Qe{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Ff({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new jt({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 js(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){h1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new ze("Constants support is not implemented in RNN yet.");h1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new jt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new ze("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new jt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Or("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Mt([n,s])):this.states_=[Mt([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Mt([n,s])):this.states_[0]=Mt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(r.shape,o))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>cn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Hv(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new jt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Xs){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=We(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=Gv((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?c:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return H(()=>{let t=Mt(e.shape);return t=ke(t,[1,2]),t=Jc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?r1(t,[1,n]):t):this.cell.stateSize>1?[r1(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()===mr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Ks(s,n);return new e(Object.assign(t,{cell:r}))}};mr.className="RNN";ue.registerClass(mr);var ud=class extends Qe{},Df=class extends ud{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,dn(this.units,"units"),this.activation=xa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=tn(e.kernelConstraint),this.recurrentConstraint=tn(e.recurrentConstraint),this.biasConstraint=tn(e.biasConstraint),this.dropout=lu([1,ga([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=lu([1,ga([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 H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ba({ones:()=>os(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ba({ones:()=>os(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=pr(z(e,a),this.kernel.read()):r=pr(e,this.kernel.read()),this.bias!=null&&(r=qs(r,this.bias.read())),o!=null&&(n=z(n,o));let i=le(r,pr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ya(this.activation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),recurrentInitializer:Ft(this.recurrentInitializer),biasInitializer:Ft(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),recurrentConstraint:en(this.recurrentConstraint),biasConstraint:en(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Df.className="SimpleRNNCell";ue.registerClass(Df);var j1=class extends mr{constructor(e){e.cell=new Df(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};j1.className="SimpleRNN";ue.registerClass(j1);var _f=class extends ud{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,dn(this.units,"units"),this.activation=xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=tn(e.kernelConstraint),this.recurrentConstraint=tn(e.recurrentConstraint),this.biasConstraint=tn(e.biasConstraint),this.dropout=lu([1,ga([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=lu([1,ga([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 H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ba({ones:()=>os(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ba({ones:()=>os(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=pr(e,this.kernel.read());this.useBias&&(u=qs(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,a[0]));let c=this.recurrentKernel.read(),[d,p]=Ht(c,[2*this.units,this.units],c.rank-1),h=pr(s,d),[f,m,g]=Ht(u,3,u.rank-1),[A,y]=Ht(h,2,h.rank-1);o=this.recurrentActivation.apply(le(f,A)),i=this.recurrentActivation.apply(le(m,y));let x=pr(z(i,s),p);l=this.activation.apply(le(g,x));let b=le(z(o,s),z(le(1,Ct(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ya(this.activation),recurrentActivation:ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),recurrentInitializer:Ft(this.recurrentInitializer),biasInitializer:Ft(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),recurrentConstraint:en(this.recurrentConstraint),biasConstraint:en(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};_f.className="GRUCell";ue.registerClass(_f);var q1=class extends mr{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 _f(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};q1.className="GRU";ue.registerClass(q1);var cd=class extends ud{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,dn(this.units,"units"),this.activation=xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Tt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Nt(e.kernelRegularizer),this.recurrentRegularizer=Nt(e.recurrentRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.kernelConstraint=tn(e.kernelConstraint),this.recurrentConstraint=tn(e.recurrentConstraint),this.biasConstraint=tn(e.biasConstraint),this.dropout=lu([1,ga([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=lu([1,ga([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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends $s{apply(i,l){let u=r.apply([a]),c=new cf().apply([a]),d=r.apply([a*2]);return B3(B3(u,c),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ba({ones:()=>os(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ba({ones:()=>os(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=z(e,a[0]));let d=pr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,o[0])),d=le(d,pr(s,this.recurrentKernel.read())),this.useBias&&(d=qs(d,this.bias.read()));let[p,h,f,m]=Ht(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),u=le(z(l,r),z(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=z(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ya(this.activation),recurrentActivation:ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),recurrentInitializer:Ft(this.recurrentInitializer),biasInitializer:Ft(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),recurrentConstraint:en(this.recurrentConstraint),biasConstraint:en(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};cd.className="LSTMCell";ue.registerClass(cd);var X1=class extends mr{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 cd(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};X1.className="LSTM";ue.registerClass(X1);var Ff=class extends ud{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){h1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ri(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Ks(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return f1(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}m1(t)}};Ff.className="StackedRNNCells";ue.registerClass(Ff);function ba(e){let{ones:t,rate:n,training:s=!1,count:r=1}=e,a=()=>V3(t(),n),o=()=>ed(a,t,s);return!r||r<=1?cn(o().clone()):Array(r).fill(void 0).map(o).map(l=>cn(l.clone()))}var VM=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},jv=class extends mr{constructor(e){if(e.unroll)throw new ze("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ze("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new jt({ndim:5})]}call(e,t){return H(()=>{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Mt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Or("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Mt(r)):this.states_=[Mt(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Mt(r)):this.states_[0]=Mt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Z(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!w.arraysEqual(i.shape,l))throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>cn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=Zs(l,s[0],r,a[0],o[0]),d=Zs(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};jv.className="ConvRNN2D";var $f=class extends cd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,dn(this.filters,"filters"),this.kernelSize=hu(n,2,"kernelSize"),this.kernelSize.forEach(i=>dn(i,"kernelSize")),this.strides=hu(s||1,2,"strides"),this.strides.forEach(i=>dn(i,"strides")),this.padding=r||"valid",vs(this.padding),this.dataFormat=a||"channelsLast",Bt(this.dataFormat),this.dilationRate=hu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>dn(i,"dilationRate"))}build(e){var t;e=dt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends $s{apply(d,p){let h=l.apply([u]),f=as([u]),m=l.apply([u*2]);return s1([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ba({ones:()=>os(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(te,ne,se)=>!ne||!ne[se]?te:z(ne[se],te),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ba({ones:()=>os(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),y=3,[x,b,v,k]=Ht(this.kernel.read(),o,y),[S,C,D,O]=this.useBias?Ht(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,D,this.padding),p=this.inputConv(p,k,O,this.padding);let[E,R,T,P]=Ht(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,E),m=this.recurrentConv(m,R),g=this.recurrentConv(g,T),A=this.recurrentConv(A,P);let U=this.recurrentActivation.apply(le(u,f)),j=this.recurrentActivation.apply(le(c,m)),q=le(z(j,a),z(U,this.activation.apply(le(d,g)))),X=z(this.recurrentActivation.apply(le(p,A)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=VM(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Rr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?qs(r,n,this.dataFormat):r}recurrentConv(e,t){return Rr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};$f.className="ConvLSTM2DCell";ue.registerClass($f);var K1=class extends jv{constructor(e){let t=new $f(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};K1.className="ConvLSTM2D";ue.registerClass(K1);var Of=class extends Qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ed(()=>V3(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Of.className="Dropout";ue.registerClass(Of);var Z1=class extends Of{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Z1.className="SpatialDropout1D";ue.registerClass(Z1);var Y1=class extends Qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,dn(this.units,"units"),this.activation=xa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Tt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Tt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=tn(e.kernelConstraint),this.biasConstraint=tn(e.biasConstraint),this.kernelRegularizer=Nt(e.kernelRegularizer),this.biasRegularizer=Nt(e.biasRegularizer),this.activityRegularizer=Nt(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 H(()=>{this.invokeCallHook(e,t);let n=We(e),s=D3(this.activation.getClassName()),r;return s!=null?r=pr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=pr(n,this.kernel.read()),this.bias!=null&&(r=qs(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ya(this.activation),useBias:this.useBias,kernelInitializer:Ft(this.kernelInitializer),biasInitializer:Ft(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:en(this.kernelConstraint),biasConstraint:en(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Y1.className="Dense";ue.registerClass(Y1);var J1=class extends Qe{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 G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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n=We(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return V(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ty.className="Reshape";ue.registerClass(ty);var ny=class extends Qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=js(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new jt({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ze(We(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ny.className="Permute";ue.registerClass(ny);var sy=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=We(e),s=-1;return Dc(Yo(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e),s=-1,r=!0,a=Dc(Yo(n,this.maskValue),s,r);return z(n,pe(a,n.dtype))})}};sy.className="Masking";ue.registerClass(sy);var ry=class extends Qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(vt(e.inputLength))}this.inputDim=e.inputDim,dn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,dn(this.outputDim,"outputDim"),this.embeddingsInitializer=Tt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Nt(e.embeddingsRegularizer),this.activityRegularizer=Nt(e.activityRegularizer),this.embeddingsConstraint=tn(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return H(()=>this.maskZero?(e=We(e),Yo(e,Ye(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);n.dtype!=="int32"&&(n=of(n,"int32"));let s=W3(this.embeddings.read(),V(n,[n.size]));return V(s,dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ft(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:en(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};ry.className="Embedding";ue.registerClass(ry);var ui=class extends Qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[dt(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Concatenate";ue.registerClass(cy);function dd(e,t){for(;e<0;)e+=t;return e}function UM(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ze("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);t=V(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);e=V(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ke(z(e,t),a[0]):i=ke(z(Ze(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=Ue(e,t,l,u)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=st(i,u)}return i.shape.length===1&&(i=Lt(i,1)),i})}var dy=class extends ui{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>dd(r,e[a].shape.length)):s=[dd(this.axes,t.shape.length),dd(this.axes,n.shape.length)],this.normalize&&(t=wf(t,s[0]),n=wf(n,s[1])),UM(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[dd(this.axes,e.length),dd(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};dy.className="Dot";ue.registerClass(dy);var py=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return ed(()=>le(uf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};py.className="GaussianNoise";ue.registerClass(py);var hy=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return this.rate>0&&this.rate<1?ed(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return z(n,uf(n.shape,1,r))},()=>n,t.training||!1):n})}};hy.className="GaussianDropout";ue.registerClass(hy);var fy=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||We(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ed(()=>{let r=We(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=da(nu(n),this.rate);l=of(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=le(z(r,l),z(le(l,-1),i));return le(z(d,u),c)},()=>We(e),t.training||!1)}return e})}};fy.className="AlphaDropout";ue.registerClass(fy);function pd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=kb(e,t,n,s,r,a);else if(e.rank===3)o=Ib(e,t,n,s,r,a);else if(e.rank===4)o=Sb(e,t,n,s,r,a);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function HM(e,t,n,s,r=.001){return H(()=>{let a=_h(e,s),o=a.mean,i=a.variance;return[pd(e,o,i,n,t,r),o,i]})}function GM(e,t,n,s,r=.001){return H(()=>{let a=_h(e,s),o=a.mean,i=a.variance,l=[];for(let f of js(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=V(o,l),c=V(i,l),d=t==null?null:V(t,l),p=n==null?null:V(n,l);return[pd(e,u,c,p,d,r),o,i]})}function jM(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),js(0,e.rank-1))?HM(e,t,n,s,r):GM(e,t,n,s,r)}var my=class extends Qe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Tt(e.betaInitializer||"zeros"),this.gammaInitializer=Tt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Tt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Tt(e.movingVarianceInitializer||"ones"),this.betaConstraint=tn(e.betaConstraint),this.gammaConstraint=tn(e.gammaConstraint),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(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 G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new jt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,s=We(e),r=s.shape,a=r.length,o=js(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=ti(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,js(0,a).slice(0,a-1)),d=()=>{if(c){let A=V(this.movingMean.read(),l),y=V(this.movingVariance.read(),l),x=this.center?V(this.beta.read(),l):null,b=this.scale?V(this.gamma.read(),l):null;return pd(s,A,y,x,b,this.epsilon)}else return pd(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=jM(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,y,x)=>{H(()=>{let b=1-x,v=A.read(),k=z(ye(v,y),b);A.write(ye(v,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ft(this.betaInitializer),gammaInitializer:Ft(this.gammaInitializer),movingMeanInitializer:Ft(this.movingMeanInitializer),movingVarianceInitializer:Ft(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:en(this.betaConstraint),gammaConstraint:en(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};my.className="BatchNormalization";ue.registerClass(my);var gy=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Tt(e.betaInitializer||"zeros"),this.gammaInitializer=Tt(e.gammaInitializer||"ones"),this.betaRegularizer=Nt(e.betaRegularizer),this.gammaRegularizer=Nt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==fa(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=We(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=_h(n,this.axis,a),l=ti(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?V(f,l):f,c=u(this.gamma.read()),d=u(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=bs(o,p),i=bs(i,p),c=bs(c,h),d=bs(d,h),pd(n,o,i,d,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ft(this.betaInitializer),gammaInitializer:Ft(this.gammaInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};gy.className="LayerNormalization";ue.registerClass(gy);function qM(e,t,n){return H(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Hs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Dr(e,s)})}var Ay=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Hs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new jt({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 H(()=>qM(We(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="ZeroPadding2D";ue.registerClass(Ay);function Pf(e,t,n,s,r,a){return H(()=>{Bt(r),O3(a),vs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=Hs()),a==null&&(a="max"),e=z1(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Lc(e,t,n,i):o=Fc(e,t,n,i),r==="channelsFirst"&&(o=Ze(o,[0,3,1,2])),o})}function qv(e,t,n,s,r,a){return H(()=>{Bt(r),O3(a),vs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Hs()),a==null&&(a="max"),e=Wv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=TA(e,t,n,i):o=dA(e,t,n,i),r==="channelsFirst"&&(o=Ze(o,[0,4,1,2,3])),o})}var Xv=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(dn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);dn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,vs(this.padding),this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){e=dt(e);let t=Zs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return H(()=>{this.invokeCallHook(e,t),e=Jc(We(e),2);let n=this.poolingFunction(We(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return st(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},yy=class extends Xv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Pf(e,t,n,s,r,"max")}};yy.className="MaxPooling1D";ue.registerClass(yy);var xy=class extends Xv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Pf(e,t,n,s,r,"avg")}};xy.className="AveragePooling1D";ue.registerClass(xy);var Kv=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];dn(this.poolSize,"poolSize"),dn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),vs(this.padding),this.inputSpec=[new jt({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=Zs(t,this.poolSize[0],this.padding,this.strides[0]),n=Zs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(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}},by=class extends Kv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Pf(e,t,n,s,r,"max")}};by.className="MaxPooling2D";ue.registerClass(by);var vy=class extends Kv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),Pf(e,t,n,s,r,"avg")}};vy.className="AveragePooling2D";ue.registerClass(vy);var Zv=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];dn(this.poolSize,"poolSize"),dn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),vs(this.padding),this.inputSpec=[new jt({ndim:5})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Zs(t,this.poolSize[0],this.padding,this.strides[0]),n=Zs(n,this.poolSize[1],this.padding,this.strides[1]),s=Zs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(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}},wy=class extends Zv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),qv(e,t,n,s,r,"max")}};wy.className="MaxPooling3D";ue.registerClass(wy);var ky=class extends Zv{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Bt(r),vs(s),qv(e,t,n,s,r,"avg")}};ky.className="AveragePooling3D";ue.registerClass(ky);var Yv=class extends Qe{constructor(e){super(e);this.inputSpec=[new jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ze}},Iy=class extends Yv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=We(e);return _t(n,1)})}};Iy.className="GlobalAveragePooling1D";ue.registerClass(Iy);var Sy=class extends Yv{constructor(e){super(e||{})}call(e,t){return H(()=>{let n=We(e);return rs(n,1)})}};Sy.className="GlobalMaxPooling1D";ue.registerClass(Sy);var Jv=class extends Qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.inputSpec=[new jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ze}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Cy=class extends Jv{call(e,t){return H(()=>{let n=We(e);return this.dataFormat==="channelsLast"?_t(n,[1,2]):_t(n,[2,3])})}};Cy.className="GlobalAveragePooling2D";ue.registerClass(Cy);var Ty=class extends Jv{call(e,t){return H(()=>{let n=We(e);return this.dataFormat==="channelsLast"?rs(n,[1,2]):rs(n,[2,3])})}};Ty.className="GlobalMaxPooling2D";ue.registerClass(Ty);var Qv=class extends Qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=Ks(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Ny=class extends Qv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=dt(e),e.length<3)throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=dt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return H(()=>(e=We(e),Gv((a,o)=>[We(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ny.className="TimeDistributed";ue.registerClass(Ny);function XM(e){si(eP,"BidirectionalMergeMode",e)}var KM="concat",Ey=class extends Qv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ks(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Ks(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?KM:e.mergeMode,XM(this.mergeMode),e.weights)throw new ze("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 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JL=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Te(0),cn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Ps(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,cn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return un([],[0].concat(this.elementShape));let n=this.readMany(e);return Ps(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),xn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return un([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return Ps(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),gt(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,Rn(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];H(()=>{t=V(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],u=[0,l,0],c=[1,e[i],r];a[i]=V(_e(t,u,c),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},fd=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);Ps(t,r.shape,"TensorList shape mismatch: "),cn(r)}),this.idTensor=Te(0),this.maxNumElements=s,cn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new fd([...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.`);Ps(e,this.elementShape,"TensorList shape mismatch: ");let s=hd(this.elementShape,this.tensors,e);return H(()=>{let r=this.tensors.map(a=>V(a,s));return xn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=hd(this.elementShape,this.tensors,e),s=this.tensors.pop();return Ps(s.shape,e,"TensorList shape mismatch: "),V(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ps(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");cn(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.`);Ps(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=hd(this.elementShape,this.tensors,t);return V(this.tensors[e],s)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Ps(this.elementShape,t.shape,"TensorList shape mismatch: "),cn(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}`);Ps(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=hd(this.elementShape,this.tensors,n);return e.length===0?un([],[0].concat(s)):H(()=>{let r=e.map(a=>V(this.tensors[a],s));return xn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Ps(this.elementShape,t,"TensorList shape mismatch: ");let n=hd(this.elementShape,this.tensors,t);return this.size()===0?un([],[0].concat(n)):H(()=>{let s=this.tensors.map(r=>V(r,n));return gt(s,0)})}};function QL(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);Ps(r,t,"TensorList shape mismatch: ");let a=Rn(e);return new fd(a,t,s)}function eB(e,t,n){return new fd([],e,t,n)}function tB(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r} vs. ${s})`);let a=new fd([],n,e.dtype,s),o=Rn(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function nB(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
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tensor.shape[0], but sum of lengths is
|
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${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=Gy(a,n),i=s===0?0:e.size/s,l=H(()=>{let c=[];e=V(e,[1,s,i]);for(let d=0;d<t.length;++d){let p=d===0?0:r[d-1],h=[0,p,0],f=[1,t[d],i];c[d]=V(_e(e,h,f),o)}return e.dispose(),c}),u=new fd([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var sB=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),a=I("cond",e,t,n),o=I("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=I("body",e,t,n),r=I("cond",e,t,n),a=I("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(c=>c.id),l=await o[0].data();o.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=a;for(;l[0];){let c=u;u=await 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s=I("axis",e,t,n);return[st(I("x",e,t,n),s)]}case"Reshape":return[V(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[NA(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Dr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Wc(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[$c(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[gA(I("x",e,t,n),s,r)]}case"BroadcastTo":return[Kl(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[Cb(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Mw(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return H(()=>ZL(a,o,i));case"basic_math":return H(()=>YL(a,o,i));case"control":return sB(a,o,i);case"convolution":return H(()=>rB(a,o,i));case"creation":return 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${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 Lw(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(p=>ls(p)[0]),c=[];s!=null&&(c=s.map(p=>ls(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((Bw(p)||SB(p)||CB(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&u.indexOf(p.name)===-1&&c.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function vB(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>ls(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return u}var wB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],kB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],IB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Bw(e){return wB.indexOf(e.op)>=0}function SB(e){return kB.indexOf(e.op)>=0}function CB(e){return IB.indexOf(e.op)>=0}var qy=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 qy(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Lw(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return vB(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[ls(c)[0]]),r=t.map(c=>ls(c)[0]),a=r.map(c=>this.graph.nodes[c]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return H(()=>{let c=new zw(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=ls(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=Mw(m,d,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let A=i.filter(y=>!Bw(y)&&!_n(y.name,h,t)).map(y=>y.name);if(A.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. 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in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=ls(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=ls(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},TB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=Vn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Vn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=Vn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new qy(Dw.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Dw.Instance.transformGraph(e.modelInitializer);this.initializer=new qy(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Vn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ge)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function rt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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instanceof Promise)&&!t)}function OB(e){return e==null||PB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ge||w.isTypedArray(e)}function PB(e){return e===null||typeof e!="object"&&typeof e!="function"}function MB(e){return FB(e,zB)}function zB(e){return e instanceof Ge?{value:e.clone(),recurse:!1}:fu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var jw=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}},Xy=class extends jw{constructor(){super(Xy.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 s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};Xy.INITIAL_CAPACITY=32;function qw(e){return new WB(e)}function Ky(e){return new VB(e)}function LB(e,t){return new Kw(e,t)}function BB(e,t=va.FAIL){return new YB(e,t)}var pn=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 KB(this,e)}filter(e){return new qB(this,e)}map(e){return new XB(this,e)}mapAsync(e){return new Xw(this,e)}serialMapAsync(e){return new Xw(this,e).serial()}flatmap(e){return new ZB(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 jB(this,e,t)}columnMajorBatch(e,t=!0,n=Hw){return this.rowMajorBatch(e,t).map(r=>$B(r,n))}concatenate(e,t){return new Kw(qw([this,e]),t)}take(e){return e<0||e==null?this:new GB(this,e)}skip(e){return e<0||e==null?this:new HB(this,e)}prefetch(e){return new Zw(this,e)}shuffle(e,t){return new JB(this,e,t)}serial(){return new UB(this)}},WB=class extends pn{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:MB(e),done:!1}}},VB=class extends pn{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: ${e.message}`,e}}},UB=class extends pn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},HB=class extends pn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Z(e.value)}return this.upstream.next()}},GB=class extends pn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},jB=class extends pn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},qB=class extends pn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Z(e.value)}}},XB=class extends pn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=zs.getTensorsInContainer(e.value),n=this.transform(e.value),s=zs.getTensorsInContainer(n);for(let r of t)zs.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},KB=class extends pn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Xw=class extends pn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=zs.getTensorsInContainer(e.value),n=await this.transform(e.value),s=zs.getTensorsInContainer(n);for(let r of t)zs.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Zy=class extends pn{constructor(){super();this.outputQueue=new Xy,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},ZB=class extends Zy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=zs.getTensorsInContainer(e.value),n=this.transform(e.value),s=zs.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)zs.isTensorInList(r,s)||r.dispose();return!0}},Kw=class extends pn{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}},va;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(va||(va={}));var YB=class extends pn{constructor(e,t=va.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 s(a){return a instanceof pn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await Gw(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case va.FAIL:throw new Error(`Zipped streams should have the same length. 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${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),us(async()=>(await n.iterator()).columnMajorBatch(e,t,tW),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,us(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,us(async()=>(await t.iterator()).filter(s=>H(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return us(async()=>(await t.iterator()).map(n=>H(()=>e(n))),this.size)}mapAsync(e){let t=this;return us(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return us(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,us(async()=>{let s=Ky(async()=>({value:await t.iterator(),done:!1}));return LB(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,us(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=DB.alea(t||w.now().toString());return us(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.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,us(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};mu.MAX_BUFFER_SIZE=1e4;function us(e,t=null){return new class extends mu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function QB(e){return us(async()=>qw(e),e.length)}function eW(e){if(!fu(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 us(async()=>{let n=await Gw(e,s=>{if(s instanceof mu)return{value:s.iterator(),recurse:!1};if(fu(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return BB(n,va.SHORTEST)},t)}function tW(e){if(e===null)return null;let t=e[0];return OB(t)?{value:nW(e),recurse:!1}:{value:null,recurse:!0}}function nW(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ge?xn(e):un(e)}var Yw=class extends mu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Wf='"',md=Symbol("out"),Jw=Symbol("field"),Vf=Symbol("quote"),Yy=Symbol("quoteafterquote"),Qw=Symbol("quoteinquote"),e7=class extends mu{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 Yw(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=md;for(let o=0;o<r;o++)switch(a){case md:switch(e.charAt(o)){case Wf:s=o+1,a=Vf;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=md;break;default:a=Jw,s=o;break}break;case Jw:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=md,s=o+1;break;default:}break;case Vf:switch(e.charAt(o)){case Wf:a=Yy;break;default:}break;case Yy:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=md,s=o+1;break;case Wf:a=Vf;break;default:a=Qw;break}break;case Qw:switch(e.charAt(o)){case Wf:a=Vf;break;default:}break;default:}if(a===Yy?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},t7=class extends pn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new t7(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),un(n,t)}},n7=class extends pn{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=Gt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=Us([a,r,i,o],[1,4])}else this.cropBox=Us([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new n7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ds.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=Lt(pe(e,"float32"),0),n;n=De.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return V(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},s7=class{},r7=class extends pn{split(e){return new sW(this,e)}},sW=class extends r7{constructor(e,t){super();this.upstream=e,this.impl=new rW(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},rW=class extends Zy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},aW=class extends pn{decodeUTF8(){return new oW(this)}},oW=class extends r7{constructor(e){super();this.upstream=e,this.impl=new iW(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iW=class extends Zy{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=a5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},a7=class extends aW{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function lW(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=uW(e));let r=await w.fetch(n,s);if(r.ok){let a=new Uint8Array(await r.arrayBuffer());return new a7(a,t)}else throw new Error(r.statusText)}var uW=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function o7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var i7=class extends s7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(o7(this.input)&&Y().get("IS_NODE")){let e=Pi("fs");this.input=e.readFileSync(this.input.substr(7))}return new a7(this.input,this.options)}},l7=class extends s7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return o7(this.url)?new i7(this.url,this.fileOptions).iterator():lW(this.url,this.fileOptions)}};function cW(e,t={}){return new e7(new l7(e),t)}function dW(e){let t=Ky(e);return us(async()=>t)}function pW(e){return us(async()=>{let t=await e();return Ky(()=>t.next())})}async function hW(e,t){return n7.create(e,t)}async function fW(e){return t7.create(e)}var mW="3.9.0";function Ce(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var gW=cr.whereImpl,Jy=class extends nc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new yp(this,es())}nextDataId(){return Jy.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&_.warn(`
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|
============================
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|
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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|
============================`));let s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return _.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return je(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return es().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Ce([e],"where");let t=this.readSync(e.dataId);return gW(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Jy.nextDataId=0;var 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E7=qt((e,t)=>Math.max(e,t)),UW=hn(lo,E7),HW={kernelName:lo,backendName:"cpu",kernelFunc:UW},R7=qt((e,t)=>Math.min(e,t)),GW=hn(ho,R7),jW={kernelName:ho,backendName:"cpu",kernelFunc:GW},n2=qt((e,t)=>e*t),qW=Qy((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),Hf=hn(mo,n2,qW),XW={kernelName:mo,backendName:"cpu",kernelFunc:Hf};function D7(e,t,n){let s=w.createScalarValue(-1,n);return n2([],t,s,e,n)}function KW(e){let{inputs:t,backend:n}=e,{x:s}=t;Ce(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=D7(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var ZW={kernelName:ml,backendName:"cpu",kernelFunc:KW},_7=qt((e,t)=>e!==t?1:0),YW=hn(gl,_7,null,"bool"),JW={kernelName:gl,backendName:"cpu",kernelFunc:YW};function s2(e,t,n,s,r){let a=t.length,o=w.sizeFromShape(t),i=w.computeStrides(t),l=w.computeStrides(r),u=w.getTypedArrayFromDType(n,w.sizeFromShape(r));for(let c=0;c<o;++c){let d=w.indexToLoc(c,a,i),p=new Array(d.length);for(let f=0;f<p.length;f++)p[f]=d[s[f]];let 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indices.shape[0] = ${i}`);let g=w.getArrayFromDType(n,0),A=w.getArrayFromDType(r,0);return[g,[0,d],A,u,c]}let p=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let A=e[g*d];if(A<0)throw new Error(`indices(${g}, 0) is invalid: ${A} < 0`);if(A>=l)throw new Error(`indices(${g}, 0) is invalid: ${A} >= ${l}`);++f[A],p=p&&A>=h,h=A}let m=!0;for(let g=0;g<l;++g){let A=f[g]===0;u[g]=A,m=m&&!A,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,A=s;for(let y=0;y<i;++y)c[y]=y;return[g,[i,d],A,u,c]}else{let g=f[l-1],A=w.getArrayFromDType(n,g*d),y=w.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let b=0;b<i;++b){let v=e[b*d],k=x[v],S=(v===0?0:f[v-1])+k;x[v]++;for(let C=0;C<d;++C)A[S*d+C]=e[b*d+C];y[S]=s[b],c[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let k=b===0?0:f[b-1];A[k*d+0]=b;for(let S=1;S<d;++S)A[k*d+S]=0;y[k]=o}return[A,[g,d],y,u,c]}}function M7(e,t,n,s,r){let a=w.sizeFromShape(s),o=t[0],i=r.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let A=r[g];if(A===-1){if(c!==-1)throw new Error(`only one output dimension may be -1, not both ${c} and ${g}`);c=g,l.push(1)}else{if(A<0)throw new Error(`size ${g} must be non-negative, not ${A}`);u*=A,l.push(A)}}if(c!==-1){if(u<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/u);if(u*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
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dense values, but the requested shape requires a multiple of ${u}. inputShape=${s} outputShape= ${l}`);l[c]=g}let d=w.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${s} outputShape=${l}`);let p=s.length,h=[];if(p>0){h[p-1]=1;for(let g=p-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=w.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let A=0;for(let y=0;y<p;++y)A+=e[g*p+y]*h[y];for(let y=0;y<i;++y)m[g*i+y]=Math.trunc(A/f[y]),A%=f[y]}return[m,[o,i],l]}function a2(e,t,n,s,r,a=!1,o=0){let i=s.length;if(i!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],u=l[1],d=i>0?r[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let h=p.reduce((x,b)=>x*b,1),f=w.getArrayFromDType(n,h);if(i===0)return d>0&&f.fill(o),[f,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,A=0,y=r[m];for(;;){let x=0;if(g<i){if(x=r[g],y===x){++g;continue}if(y>=x)throw new Error("segment ids are not increasing")}if(y<0||y>=d)throw new Error(`Segment id ${y} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);y>A&&f.fill(o,A*u,y*u);for(let b=m;b<g;++b){let v=s[b];if(v<0||v>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<u;k++)f[y*u+k]+=e[v*u+k]}if(a)for(let b=0;b<u;b++)f[y*u+b]/=g-m;if(m=g,++g,A=y+1,y=x,g>i)break}return A<d&&f.fill(o,A*u,d*u),[f,p]}var iV=ka(e=>Math.sqrt(e)),lV=pt(No,e=>Math.sqrt(e)),uV={kernelName:No,backendName:"cpu",kernelFunc:lV},z7=qt((e,t)=>{let n=e-t;return n*n}),cV=hn(Do,z7),dV={kernelName:Do,backendName:"cpu",kernelFunc:cV};function L7(e,t,n,s){let r=je(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var pV=class{constructor(e,t,n,s,r,a){this.separator=w.encodeString(e),this.nGramWidths=t,this.leftPad=w.encodeString(n),this.rightPad=w.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),u=Math.max(0,i-(r-(o+1))),c=a-(l+u),d=t+(l>0?0:o-i),p=0;p+=l*this.leftPad.length;for(let A=0;A<c;++A)p+=e[d+A].length;p+=u*this.rightPad.length,p+=(l+u+c-1)*this.separator.length,n[s+o]=new Uint8Array(p);let f=n[s+o],m=0,g=A=>A.forEach(y=>f[m++]=y);for(let A=0;A<l;++A)g(this.leftPad),g(this.separator);for(let A=0;A<c-1;++A)g(e[d+A]),g(this.separator);if(c>0){g(e[d+c-1]);for(let A=0;A<u;++A)g(this.separator),g(this.rightPad)}else{for(let A=0;A<u-1;++A)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let u=t[l]>=i;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. 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c=t.slice();return c[c.length-1]=s,[je(c,n,l),je(c,"int32",u)]}function q7(e,t,n,s){let r=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[r]),l=new Yt(a,s,e),u=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let A=0;A<a[0];A++)for(let y=0;y<a[2];y++)g.push(l.get(A,f,y));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,u.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new Yt(d,s);u.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let A=0;A<a[2];A++)p.set(l.get(g,f,A),g,m,A)});let h=n.slice();return h[r]=d[1],{outputValues:p.values,outputShape:h,indices:i}}ql("cpu",()=>new Jy,1);var X7=pt(Ja,e=>e>=0?e:Math.exp(e)-1),gV={kernelName:Ja,backendName:"cpu",kernelFunc:X7};function K7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ce([r],"leakyRelu");let o=w.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=w.getTypedArrayFromDType("float32",o);for(let u=0;u<i.length;u++)l[u]=i[u]<0?a*i[u]:i[u];return n.makeTensorInfo(r.shape,"float32",l)}var AV={kernelName:ao,backendName:"cpu",kernelFunc:K7},yV=qt((e,t)=>e<0?t*e:e);function Z7(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Ce([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=yV(s.shape,r.shape,a,o,s.dtype);return n.makeTensorInfo(l,s.dtype,i)}var xV={kernelName:xo,backendName:"cpu",kernelFunc:Z7},Y7=pt(bo,e=>Math.max(0,e)),bV={kernelName:bo,backendName:"cpu",kernelFunc:Y7},J7=pt(wo,e=>Math.min(Math.max(0,e),6)),vV={kernelName:wo,backendName:"cpu",kernelFunc:J7};function i2(e,t,n,s,r){if(n==="linear")return gr({inputs:{x:t},backend:e});if(n==="relu")return Y7({inputs:{x:t},backend:e});if(n==="elu")return X7({inputs:{x:t},backend:e});if(n==="relu6")return J7({inputs:{x:t},backend:e});if(n==="prelu")return 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o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ws({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],_.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[c,d]=_.computeOutAndReduceShapes(l.shape,o),p=w.sizeFromShape(c),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let A=g*f,y=m[A],x=0;for(let b=0;b<f;++b){let v=m[A+b];v<y&&(y=v,x=b)}h[g]=x}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var LV={kernelName:ac,backendName:"cpu",kernelFunc:zV},BV=pt(Hi,e=>Math.asin(e)),WV={kernelName:Hi,backendName:"cpu",kernelFunc:BV},VV=pt(Gi,e=>Math.asinh(e)),UV={kernelName:Gi,backendName:"cpu",kernelFunc:VV},HV=pt(ji,e=>Math.atan(e)),GV={kernelName:ji,backendName:"cpu",kernelFunc:HV},jV=qt((e,t)=>Math.atan2(e,t)),qV=hn(Xi,jV),XV={kernelName:Xi,backendName:"cpu",kernelFunc:qV},KV=pt(qi,e=>Math.atanh(e)),ZV={kernelName:qi,backendName:"cpu",kernelFunc:KV};function l2(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=je(r.outShape,n),g=m.values,A=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;b<r.batchSize;++b){let v=b*A,k=b*s[0];for(let S=0;S<r.inChannels;++S)for(let C=0;C<r.outHeight;++C){let D=C*o-p,O=Math.max(0,D),E=Math.min(r.inHeight,c+D),R=v+C*y;for(let T=0;T<r.outWidth;++T){let P=T*i-h,U=Math.max(0,P),j=Math.min(r.inWidth,d+P),q=f,X=0,te=0;for(let se=O;se<E;se+=l){let ae=k+se*s[1];for(let Q=U;Q<j;Q+=u){let ce=ae+Q*s[2],de=e[ce+S];a==="max"&&de>q?q=de:a==="avg"&&(X+=de,te++)}if(isNaN(q))break}let ne=R+T*x+S;g[ne]=a==="avg"?X/te:q}}}return m}function e6(e,t,n,s,r=!1,a=!1){let o=je(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,u=s.dilationHeight,c=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=je(t,n,e);for(let g=0;g<s.batchSize;++g)for(let A=0;A<s.inChannels;++A)for(let y=0;y<s.outHeight;++y){let x=y*i-h,b=x;for(;b<0;)b+=u;let v=Math.min(s.inHeight,d+x);for(let k=0;k<s.outWidth;++k){let S=k*l-f,C=S;for(;C<0;)C+=c;let D=Math.min(s.inWidth,p+S),O=Number.NEGATIVE_INFINITY,E=-1;for(let R=b;R<v;R+=u){let T=R-x;for(let P=C;P<D;P+=c){let U=P-S,j=m.get(g,R,P,A);j>O&&(O=j,r?E=a?((g*s.inHeight+R)*s.inWidth+P)*s.inChannels+A:(R*s.inWidth+P)*s.inChannels+A:E=T*p+U)}}o.set(E,g,y,k,A)}}return o}function t6(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,u=r.dilationDepth,c=r.dilationHeight,d=r.dilationWidth,p=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,A=r.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=je(r.outShape,n),b=x.values,v=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],S=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let D=0;D<r.batchSize;++D){let O=D*v,E=D*s[0];for(let R=0;R<r.inChannels;++R)for(let T=0;T<r.outDepth;++T){let P=T*o-m,U=P;for(;U<0;)U+=u;let j=Math.min(r.inDepth,p+P),q=O+T*k;for(let X=0;X<r.outHeight;++X){let te=X*i-g,ne=te;for(;ne<0;)ne+=c;let se=Math.min(r.inHeight,h+te),ae=q+X*S;for(let Q=0;Q<r.outWidth;++Q){let ce=Q*l-A,de=ce;for(;de<0;)de+=d;let fe=Math.min(r.inWidth,f+ce),be=ae+Q*C,Ee=y,Re=0,Pe=0;for(let Me=U;Me<j;Me+=u){let mt=E+Me*s[1];for(let lt=ne;lt<se;lt+=c){let ut=mt+lt*s[2];for(let at=de;at<fe;at+=d){let ht=ut+at*s[3],Xe=e[ht+R];if(a==="max"&&Xe>Ee?Ee=Xe:a==="avg"&&(Re+=Xe,Pe++),isNaN(Ee))break}if(isNaN(Ee))break}if(isNaN(Ee))break}let Be=be+R;b[Be]=a==="avg"?Re/Pe:Ee}}}}return x}function YV(e,t){let n=je(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let A=0;A<t.outDepth;++A){let y=A*s-p,x=y;for(;x<0;)x+=o;let b=Math.min(t.inDepth,u+y);for(let v=0;v<t.outHeight;++v){let k=v*r-h,S=k;for(;S<0;)S+=i;let C=Math.min(t.inHeight,c+k);for(let D=0;D<t.outWidth;++D){let O=D*a-f,E=O;for(;E<0;)E+=l;let R=Math.min(t.inWidth,d+O),T=Number.NEGATIVE_INFINITY,P=-1;for(let U=x;U<b;U+=o){let j=U-y;for(let q=S;q<C;q+=i){let X=q-k;for(let te=E;te<R;te+=l){let ne=te-O,se=e.get(m,U,q,te,g);se>=T&&(T=se,P=j*c*d+X*c+ne)}}}n.set(P,m,A,v,D,g)}}}return n}function JV(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ce(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=gr({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),f=l2(p,r.shape,r.dtype,h,c,"avg");d=n.makeTensorInfo(c.outShape,r.dtype,f.values)}return d}var QV={kernelName:Wa,backendName:"cpu",kernelFunc:JV};function eU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;Ce(r,"avgPool3d");let c=_.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,p=t6(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var tU={kernelName:oc,backendName:"cpu",kernelFunc:eU};function nU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;Ce([r,a],"avgPool3DGrad");let c=_.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,p=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,A=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,S=b-1-c.padInfo.front,C=k-1-c.padInfo.left,D=v-1-c.padInfo.top,O=je(a.shape,"float32"),E=1/(f*m*g),R=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let P=0;P<c.inChannels;++P)for(let U=0;U<c.inDepth;++U)for(let j=0;j<c.inHeight;++j)for(let q=0;q<c.inWidth;++q){let X=U-S,te=j-D,ne=q-C,se=0;for(let ae=0;ae<b;ae+=A){let Q=(X+ae)/d;if(!(Q<0||Q>=c.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce<v;ce+=y){let de=(te+ce)/p;if(!(de<0||de>=c.outHeight||Math.floor(de)!==de))for(let fe=0;fe<k;fe+=x){let be=(ne+fe)/h;if(be<0||be>=c.outWidth||Math.floor(be)!==be)continue;se+=R.get(T,Q,de,be,P)}}}O.set(se*E,T,U,j,q,P)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var sU={kernelName:Ip,backendName:"cpu",kernelFunc:nU};function rU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ce([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=_.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,A=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=A-1-c.padInfo.top,v=je(o.shape,"float32"),k=1/(h*f),S=n.data.get(r.dataId).values,C=je(r.shape,"float32",S);for(let D=0;D<c.batchSize;++D)for(let O=0;O<c.inChannels;++O)for(let E=0;E<c.inHeight;++E)for(let R=0;R<c.inWidth;++R){let T=E-b,P=R-x,U=0;for(let j=0;j<A;j+=m){let q=(T+j)/d;if(!(q<0||q>=c.outHeight||Math.floor(q)!==q))for(let X=0;X<y;X+=g){let te=(P+X)/p;if(te<0||te>=c.outWidth||Math.floor(te)!==te)continue;U+=C.get(D,q,te,O)}}v.set(U*k,D,E,R,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var aU={kernelName:kp,backendName:"cpu",kernelFunc:rU};function oU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;w.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ce([r,i,l,a,o],"batchNorm");let{varianceEpsilon:u}=s;u==null&&(u=.001);let c=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(l.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,A=h.length,y=p.length,x=d.length,b=0,v=0,k=0,S=0;for(let C=0;C<c.length;++C)m[C]=f[b++]+(c[C]-d[v++])*h[k++]/Math.sqrt(p[S++]+u),b>=g&&(b=0),v>=x&&(v=0),k>=A&&(k=0),S>=y&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var iU={kernelName:no,backendName:"cpu",kernelFunc:oU};function lU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ce([r],"batchToSpaceND");let i=a.reduce((A,y)=>A*y),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=wt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=ws({inputs:{x:h},backend:n,attrs:{perm:u}}),m=wt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=di({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var uU={kernelName:Ki,backendName:"cpu",kernelFunc:lU};function cU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,u=e2(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var dU={kernelName:Sp,backendName:"cpu",kernelFunc:cU};function pU(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=_.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var hU={kernelName:wg,backendName:"cpu",kernelFunc:pU},fU=pt(ta,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),mU={kernelName:ta,backendName:"cpu",kernelFunc:fU},gU=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],d=l[u];s[u]=Math.hypot(c,d)}return n.makeOutput(s,t.shape,"float32")},AU={kernelName:ic,backendName:"cpu",kernelFunc:gU};function 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m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),x}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return wt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=_.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,p=t2(c,o,t[0].dtype,d),h=_.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var xU={kernelName:Zi,backendName:"cpu",kernelFunc:yu};function n6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;Ce([r,a],"conv2d");let d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,y=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new Yt(p.outShape,r.dtype),v=w.computeStrides(r.shape),k=w.computeStrides(a.shape),S=v[0],C=x?v[1]:v[2],D=x?v[2]:1,O=x?1:v[1],E=b.strides[0],R=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,P=x?1:b.strides[1],U=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X<p.batchSize;++X){let te=X*S,ne=X*E;for(let se=0;se<p.outHeight;++se){let ae=ne+se*R,Q=se*p.strideHeight-y;for(let ce=0;ce<h;++ce){let de=Q+ce*m;if(de<0||de>=p.inHeight)continue;let fe=ce*k[0],be=te+de*C;for(let Ee=0;Ee<p.outWidth;++Ee){let Re=ae+Ee*T,Pe=Ee*p.strideWidth-A;for(let Be=0;Be<f;++Be){let Me=Pe+Be*g;if(Me<0||Me>=p.inWidth)continue;let mt=fe+Be*k[1],lt=be+Me*D,ut=mt;for(let at=0;at<p.inChannels;++at){let ht=U[lt+at*O];for(let Xe=0;Xe<p.outChannels;++Xe)q[Re+Xe*P]+=ht*j[ut+Xe];ut+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,q)}var bU={kernelName:Ga,backendName:"cpu",kernelFunc:n6};function vU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s;Ce([r,a],"conv2dBackpropFilter");let d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,A=p.dataFormat==="channelsLast",y=new Yt(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=new Yt(r.shape,r.dtype,v),C=new Yt(a.shape,a.dtype,k);for(let D=0;D<m;++D){let O=Math.max(0,Math.ceil((b-D)/h)),E=Math.min(p.outHeight,(p.inHeight+b-D)/h);for(let R=0;R<g;++R){let T=Math.max(0,Math.ceil((x-R)/f)),P=Math.min(p.outWidth,(p.inWidth+x-R)/f);for(let U=0;U<p.inChannels;++U)for(let j=0;j<p.outChannels;++j){let q=0;for(let X=0;X<p.batchSize;++X)for(let te=O;te<E;++te){let ne=D+te*h-b;for(let se=T;se<P;++se){let ae=R+se*f-x;A?q+=S.get(X,ne,ae,U)*C.get(X,te,se,j):q+=S.get(X,U,ne,ae)*C.get(X,j,te,se)}}y.set(q,D,R,U,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var wU={kernelName:Tp,backendName:"cpu",kernelFunc:vU};function kU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s;Ce([r,a],"conv2dBackpropInput");let d=w.computeStrides(a.shape),p=w.computeStrides(r.shape),h=_.convertConv2DDataFormat(u),f=_.computeConv2DInfo(o,a.shape,i,1,l,c,!1,h),m=new Yt(f.inShape,"float32"),g=m.values,A=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[x,b,v]=d,{batchSize:k,filterHeight:S,filterWidth:C,inChannels:D,inHeight:O,inWidth:E,outChannels:R,outHeight:T,outWidth:P,strideHeight:U,strideWidth:j}=f;h=f.dataFormat;let q=S-1-f.padInfo.top,X=C-1-f.padInfo.left,te=h==="channelsLast",ne=m.strides[0],se=te?m.strides[1]:m.strides[2],ae=te?m.strides[2]:1,Q=te?1:m.strides[1],ce=p[0],de=te?p[1]:p[2],fe=te?p[2]:1,be=te?1:p[1];for(let Ee=0;Ee<k;++Ee)for(let Re=0;Re<D;++Re)for(let Pe=0;Pe<O;++Pe){let Be=Pe-q,Me=Math.max(0,Math.ceil(Be/U)),mt=Math.min(T,(S+Be)/U);for(let lt=0;lt<E;++lt){let ut=lt-X,at=Math.max(0,Math.ceil(ut/j)),ht=Math.min(P,(C+ut)/j),Xe=0;for(let Rt=Me;Rt<mt;++Rt){let Yn=Rt*U-Be;for(let mn=at;mn<ht;++mn){let Ts=mn*j-ut,Sn=ce*Ee+de*Rt+fe*mn,ms=x*(S-1-Yn)+b*(C-1-Ts)+v*Re;for(let gs=0;gs<R;++gs){let gn=A[Sn+be*gs],As=y[ms+gs];Xe+=gn*As}}}let Ln=ne*Ee+se*Pe+ae*lt+Q*Re;g[Ln]=Xe}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var IU={kernelName:ja,backendName:"cpu",kernelFunc:kU};function SU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Ce([r,a],"conv3d");let u=_.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:c,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=u,A=g.front,y=g.left,x=g.top,b=new Yt(u.outShape,r.dtype),v=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=b.values,C=w.computeStrides(r.shape),D=w.computeStrides(a.shape);for(let O=0;O<u.batchSize;++O){let E=O*C[0],R=O*b.strides[0];for(let T=0;T<u.outDepth;++T){let P=R+T*b.strides[1],U=T*u.strideDepth-A;for(let j=0;j<c;++j){let q=U+j*h;if(q<0||q>=u.inDepth)continue;let X=j*D[0],te=E+q*C[1];for(let ne=0;ne<u.outHeight;++ne){let se=P+ne*b.strides[2],ae=ne*u.strideHeight-x;for(let Q=0;Q<d;++Q){let ce=ae+Q*f;if(ce<0||ce>=u.inHeight)continue;let de=X+Q*D[1],fe=te+ce*C[2];for(let be=0;be<u.outWidth;++be){let Ee=se+be*u.outChannels,Re=be*u.strideWidth-y;for(let Pe=0;Pe<p;++Pe){let Be=Re+Pe*m;if(Be<0||Be>=u.inWidth)continue;let Me=de+Pe*D[2],mt=fe+Be*u.inChannels,lt=Me;for(let ut=0;ut<u.inChannels;++ut){let at=v[mt+ut];for(let ht=0;ht<u.outChannels;++ht)S[Ee+ht]+=at*k[lt+ht];lt+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var CU={kernelName:lc,backendName:"cpu",kernelFunc:SU};function TU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Ce([r,a],"conv3dBackpropFilterV2");let u=w.computeStrides(r.shape),c=w.computeStrides(a.shape),d=_.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,A=d.filterWidth,y=new Yt(d.filterShape,"float32"),x=y.values,[b,v,k,S]=y.strides,C=n.data.get(a.dataId).values,[D,O,E,R]=c,T=n.data.get(r.dataId).values,[P,U,j,q]=u,X=d.padInfo.front,te=d.padInfo.left,ne=d.padInfo.top;for(let se=0;se<m;++se){let ae=Math.max(0,Math.ceil((X-se)/p)),Q=Math.min(d.outDepth,(d.inDepth+X-se)/p),ce=se*b;for(let de=0;de<g;++de){let fe=Math.max(0,Math.ceil((ne-de)/h)),be=Math.min(d.outHeight,(d.inHeight+ne-de)/h),Ee=de*v+ce;for(let Re=0;Re<A;++Re){let Pe=Math.max(0,Math.ceil((te-Re)/f)),Be=Math.min(d.outWidth,(d.inWidth+te-Re)/f),Me=Re*k+Ee;for(let mt=0;mt<d.inChannels;++mt){let lt=mt*S+Me;for(let ut=0;ut<d.outChannels;++ut){let at=0;for(let ht=0;ht<d.batchSize;++ht){let Xe=ht*P,Ln=ht*D;for(let Rt=ae;Rt<Q;++Rt){let mn=(se+Rt*p-X)*U+Xe,Ts=Rt*O+Ln;for(let Sn=fe;Sn<be;++Sn){let gs=(de+Sn*h-ne)*j+mn,gn=Sn*E+Ts;for(let As=Pe;As<Be;++As){let Jn=(Re+As*f-te)*q+gs,er=As*R+gn;at+=T[Jn+mt]*C[er+ut]}}}}x[lt+ut]=at}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var NU={kernelName:Np,backendName:"cpu",kernelFunc:TU};function EU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ce([r],"conv3dBackpropInputV2");let u=w.computeStrides(r.shape),c=w.computeStrides(a.shape),d=_.computeConv3DInfo(l,a.shape,i,1,o),p=new Yt(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,y=n.data.get(r.dataId).values,[x,b,v,k]=u,S=n.data.get(a.dataId).values,[C,D,O,E]=c,{batchSize:R,filterDepth:T,filterHeight:P,filterWidth:U,inChannels:j,inDepth:q,inHeight:X,inWidth:te,outChannels:ne,outDepth:se,outHeight:ae,outWidth:Q,strideDepth:ce,strideHeight:de,strideWidth:fe}=d,be=T-1-d.padInfo.front,Ee=P-1-d.padInfo.top,Re=U-1-d.padInfo.left;for(let Pe=0;Pe<R;++Pe)for(let Be=0;Be<j;++Be)for(let Me=0;Me<q;++Me){let mt=Me-be,lt=Math.max(0,Math.ceil(mt/ce)),ut=Math.min(se,(T+mt)/ce);for(let at=0;at<X;++at){let ht=at-Ee,Xe=Math.max(0,Math.ceil(ht/de)),Ln=Math.min(ae,(P+ht)/de);for(let Rt=0;Rt<te;++Rt){let Yn=Rt-Re,mn=Math.max(0,Math.ceil(Yn/fe)),Ts=Math.min(Q,(U+Yn)/fe),Sn=0;for(let ms=lt;ms<ut;++ms){let gs=ms*ce-mt;for(let gn=Xe;gn<Ln;++gn){let As=gn*de-ht;for(let ys=mn;ys<Ts;++ys){let Jn=ys*fe-Yn,er=x*Pe+b*ms+v*gn+k*ys,vr=C*(T-1-gs)+D*(P-1-As)+O*(U-1-Jn)+E*Be;for(let Gr=0;Gr<ne;++Gr){let Si=y[er+Gr],tr=S[vr+Gr];Sn+=Si*tr}}}}h[f*Pe+m*Me+g*at+A*Rt+Be]=Sn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var RU={kernelName:Ep,backendName:"cpu",kernelFunc:EU},DU=pt(qa,e=>Math.cos(e)),_U={kernelName:qa,backendName:"cpu",kernelFunc:DU},FU=pt(Xa,e=>Math.cosh(e)),$U={kernelName:Xa,backendName:"cpu",kernelFunc:FU};function OU(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,A=je([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=w.computeStrides(r.shape),k=w.computeStrides(A.shape);for(let S=0;S<f;S++){let C=S*4,D=y[C],O=y[C+1],E=y[C+2],R=y[C+3],T=x[S];if(T>=c)continue;let P=m>1?(E-D)*(d-1)/(m-1):0,U=g>1?(R-O)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let q=m>1?D*(d-1)+j*P:.5*(D+E)*(d-1);if(q<0||q>d-1){for(let X=0;X<g;X++)for(let te=0;te<h;te++){let ne=te+X*k[2]+j*k[1]+S*k[0];A.values[ne]=u}continue}if(l==="bilinear"){let X=Math.floor(q),te=Math.ceil(q),ne=q-X;for(let se=0;se<g;se++){let ae=g>1?O*(p-1)+se*U:.5*(O+R)*(p-1);if(ae<0||ae>p-1){for(let fe=0;fe<h;fe++){let be=fe+se*k[2]+j*k[1]+S*k[0];A.values[be]=u}continue}let Q=Math.floor(ae),ce=Math.ceil(ae),de=ae-Q;for(let fe=0;fe<h;fe++){let be=fe+Q*v[2]+X*v[1]+T*v[0],Ee=b[be];be=fe+ce*v[2]+X*v[1]+T*v[0];let Re=b[be];be=fe+Q*v[2]+te*v[1]+T*v[0];let Pe=b[be];be=fe+ce*v[2]+te*v[1]+T*v[0];let Be=b[be],Me=Ee+(Re-Ee)*de,mt=Pe+(Be-Pe)*de;be=fe+se*k[2]+j*k[1]+S*k[0],A.values[be]=Me+(mt-Me)*ne}}}else for(let X=0;X<g;++X){let te=g>1?O*(p-1)+X*U:.5*(O+R)*(p-1);if(te<0||te>p-1){for(let ae=0;ae<h;ae++){let Q=ae+X*k[2]+j*k[1]+S*k[0];A.values[Q]=u}continue}let ne=Math.round(te),se=Math.round(q);for(let ae=0;ae<h;ae++){let Q=ae+ne*v[2]+se*v[1]+T*v[0],ce=ae+X*k[2]+j*k[1]+S*k[0];A.values[ce]=b[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var PU={kernelName:Yi,backendName:"cpu",kernelFunc:OU};function MU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ce(r,"cumsum");let l=_.getAxesPermutation([a],r.shape.length),u=r;l!=null&&(u=ws({inputs:{x:r},backend:n,attrs:{perm:l}}));let c=_.getInnerMostAxes(1,r.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let d=Rs(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),d),h=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=i?(A,y)=>A+f-y-1:(A,y)=>A+y;for(let A=0;A<h.length;A+=f)for(let y=0;y<f;y++){let x=m(A,y);if(y===0)p[x]=o?0:h[x];else{let b=m(A,y-1);p[x]=o?h[b]+p[b]:h[x]+p[b]}}let g=n.makeTensorInfo(u.shape,d,p);if(l!=null){let A=_.getUndoAxesPermutation(l),y=ws({inputs:{x:g},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(u),y}return g}var zU={kernelName:Ka,backendName:"cpu",kernelFunc:MU};function LU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=e2(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=p7(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var BU={kernelName:Rp,backendName:"cpu",kernelFunc:LU};function WU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,p=u*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let A=0;A<i;++A)for(let y=0;y<d;++y){let x=Math.floor(y/a),b=y%a;for(let v=0;v<p;++v){let k=Math.floor(v/a),S=v%a,C=(b*a+S)*h;for(let D=0;D<h;++D){let E=D+C+c*(k+u*(x+l*A));m[g++]=f[E]}}}return n.makeTensorInfo([i,d,p,h],r.dtype,m)}var VU={kernelName:Ji,backendName:"cpu",kernelFunc:WU};function s6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s;Ce([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),d=w.computeStrides(a.shape),p=l;p==null&&(p=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=_.computeConv2DInfo(r.shape,a.shape,o,p,i,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:y}=h,x=y.left,b=y.top,v=h.outChannels/h.inChannels,k=new Yt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,D=k.values;for(let O=0;O<h.batchSize;++O){let E=O*c[0],R=O*k.strides[0];for(let T=0;T<h.outHeight;++T){let P=R+T*k.strides[1],U=T*h.strideHeight-b;for(let j=0;j<f;++j){let q=U+j*g;if(q<0||q>=h.inHeight)continue;let X=j*d[0],te=E+q*c[1];for(let ne=0;ne<h.outWidth;++ne){let se=P+ne*k.strides[2],ae=ne*h.strideWidth-x;for(let Q=0;Q<m;++Q){let ce=ae+Q*A;if(ce<0||ce>=h.inWidth)continue;let de=X+Q*d[1],fe=te+ce*h.inChannels,be=se,Ee=de;for(let Re=0;Re<h.inChannels;++Re){let Pe=S[fe+Re];for(let Be=0;Be<v;++Be)D[be+Be]+=Pe*C[Ee+Be];be+=v,Ee+=v}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var UU={kernelName:Za,backendName:"cpu",kernelFunc:s6};function HU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s;Ce([r,a],"depthwiseConv2dNativeBackpropFilter");let d=_.computeConv2DInfo(r.shape,c,o,i,l,u,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Yt(d.filterShape,"float32"),A=d.padInfo.left,y=d.padInfo.top,x=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,v=new Yt(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,S=new Yt(a.shape,a.dtype,k);for(let C=0;C<f;++C){let D=Math.max(0,Math.ceil((y-C)/p)),O=Math.min(d.outHeight,(d.inHeight+y-C)/p);for(let E=0;E<m;++E){let R=Math.max(0,Math.ceil((A-E)/h)),T=Math.min(d.outWidth,(d.inWidth+A-E)/h);for(let P=0;P<d.outChannels;++P){let U=Math.trunc(P/x),j=P%x,q=0;for(let X=0;X<d.batchSize;++X)for(let te=D;te<O;++te){let ne=C+te*p-y;for(let se=R;se<T;++se){let ae=E+se*h-A;q+=v.get(X,ne,ae,U)*S.get(X,te,se,P)}}g.set(q,C,E,U,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var GU={kernelName:Dp,backendName:"cpu",kernelFunc:HU};function jU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s;Ce([r,a],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(r.shape),p=w.computeStrides(a.shape),h=_.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new Yt(h.inShape,"float32"),m=f.values,[g,A,y]=f.strides,x=n.data.get(r.dataId).values,[b,v,k]=d,S=n.data.get(a.dataId).values,[C,D,O]=p,{batchSize:E,filterHeight:R,filterWidth:T,inChannels:P,inHeight:U,inWidth:j,outChannels:q,outHeight:X,outWidth:te,strideHeight:ne,strideWidth:se}=h,ae=R-1-h.padInfo.top,Q=T-1-h.padInfo.left,ce=q/P;for(let de=0;de<E;++de)for(let fe=0;fe<P;++fe)for(let be=0;be<U;++be){let Ee=be-ae,Re=Math.max(0,Math.ceil(Ee/ne)),Pe=Math.min(X,(R+Ee)/ne);for(let Be=0;Be<j;++Be){let Me=Be-Q,mt=Math.max(0,Math.ceil(Me/se)),lt=Math.min(te,(T+Me)/se),ut=0;for(let at=Re;at<Pe;++at){let ht=at*ne-Ee;for(let Xe=mt;Xe<lt;++Xe){let Ln=Xe*se-Me,Rt=b*de+v*at+k*Xe,Yn=C*(R-1-ht)+D*(T-1-Ln)+O*fe;for(let mn=0;mn<ce;++mn){let Ts=fe*ce+mn,Sn=x[Rt+Ts],ms=S[Yn+mn];ut+=Sn*ms}}}m[g*de+A*be+y*Be+fe]=ut}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var qU={kernelName:_p,backendName:"cpu",kernelFunc:jU};function XU(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=je([r,r],s.dtype),i=o.values;for(let u=0;u<a.length;u++)i[u*r+u]=a[u];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var KU={kernelName:Fp,backendName:"cpu",kernelFunc:XU},ZU={kernelName:uc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(s.dataId).values,c=s.shape.length,d=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:A,outWidth:y,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:D,outShape:O}=_.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),E=w.sizeFromShape(O),R=O.length,T=w.getArrayFromDType(s.dtype,E);for(let U=0;U<h;++U)for(let j=0;j<A;++j){let q=j*b-x.top;for(let X=0;X<y;++X){let te=X*v-x.left;for(let ne=0;ne<g;++ne){let se=Number.MIN_SAFE_INTEGER;for(let Q=0;Q<k;++Q){let ce=q+Q*C;if(ce>=0&&ce<f)for(let de=0;de<S;++de){let fe=te+de*D;if(fe>=0&&fe<m){let be=w.locToIndex([U,ce,fe,ne],c,w.computeStrides(s.shape)),Ee=w.locToIndex([Q,de,ne],p,w.computeStrides(r.shape)),Re=u[be]+d[Ee];Re>se&&(se=Re)}}}let ae=w.locToIndex([U,j,X,ne],R,w.computeStrides(O));T[ae]=se}}}return{dataId:l.write(w.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},YU={kernelName:Op,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:D}=_.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===D.length,()=>`Error in ${Op}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let O=w.toNestedArray(D,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<p;++T)for(let P=0;P<g;++P){let U=P*x-y.top;for(let j=0;j<A;++j){let q=j*b-y.left;for(let X=0;X<m;++X){let 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uG={kernelName:Hp,backendName:"cpu",kernelFunc:lG};function cG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Ce([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=_.computePool2DInfo(i.shape,l,u,1,c,d),h=n.data.get(i.dataId).values,f=je(p.outShape,i.dtype,e6(h,i.shape,i.dtype,p).values),m=p.strideHeight,g=p.strideWidth,A=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterHeight,b=p.effectiveFilterWidth,v=b-1-p.padInfo.left,k=x-1-p.padInfo.top,S=je(i.shape,"float32"),C=n.data.get(r.dataId).values,D=je(r.shape,"float32",C);for(let O=0;O<p.batchSize;++O)for(let E=0;E<p.inChannels;++E)for(let R=0;R<p.inHeight;++R)for(let T=0;T<p.inWidth;++T){let P=R-k,U=T-v,j=0;for(let q=0;q<x;q+=A){let X=(P+q)/m;if(!(X<0||X>=p.outHeight||Math.floor(X)!==X))for(let te=0;te<b;te+=y){let ne=(U+te)/g;if(ne<0||ne>=p.outWidth||Math.floor(ne)!==ne)continue;let 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t.makeTensorInfo([i.length],a,i)}var jG={kernelName:mc,backendName:"cpu",kernelFunc:GG},qG=pt(kl,e=>1/e),XG={kernelName:kl,backendName:"cpu",kernelFunc:qG};function KG(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ce(r,"resizeBilinear");let l=w.computeStrides(r.shape),[u,c]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(w.sizeFromShape([d,u,c,f])),A=[a&&u>1?p-1:p,a&&c>1?h-1:h],y=[a&&u>1?u-1:u,a&&c>1?c-1:c],x=0,b=A[0]/y[0],v=A[1]/y[1];for(let k=0;k<d;k++)for(let S=0;S<u;S++){let C;o?C=b*(S+.5)-.5:C=b*S;let D=Math.max(0,Math.floor(C)),O=C-D,E=Math.min(p-1,Math.ceil(C)),R=k*l[0]+D*l[1],T=k*l[0]+E*l[1];for(let P=0;P<c;P++){let U;o?U=v*(P+.5)-.5:U=v*P;let j=Math.max(0,Math.floor(U)),q=U-j,X=Math.min(h-1,Math.ceil(U)),te=R+j*l[2],ne=T+j*l[2],se=R+X*l[2],ae=T+X*l[2];for(let Q=0;Q<f;Q++){let ce=m[te+Q],de=m[ne+Q],fe=m[se+Q],be=m[ae+Q],Ee=ce+(fe-ce)*q,Re=de+(be-de)*q,Pe=Ee+(Re-Ee)*O;g[x++]=Pe}}}return 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l=new Yt(r.shape,r.dtype),u=n.bufferSync(r);for(let c=0;c<l.size;c++){let d=l.indexToLoc(c),p=d.slice();i.forEach(h=>p[h]=r.shape[h]-1-p[h]),l.set(u.get(...p),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var rj={kernelName:ko,backendName:"cpu",kernelFunc:sj},aj={kernelName:Ll,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[u,c,d,p]=s.shape,[h,f]=_.getImageCenter(o,c,d),m=255,g=Math.sin(r),A=Math.cos(r),y=i.data.get(s.dataId).values;for(let b=0;b<u;b++){let v=b*d*c*p;for(let k=0;k<c;k++){let S=k*(d*p);for(let C=0;C<d;C++){let D=C*p;for(let O=0;O<p;O++){let E=[u,k,C,O],R=E[2],T=E[1],P=(R-h)*A-(T-f)*g,U=(R-h)*g+(T-f)*A;P=Math.round(P+h),U=Math.round(U+f);let j=a;if(typeof a!="number"&&(O===3?j=m:j=a[O]),P>=0&&P<d&&U>=0&&U<c){let X=U*(d*p),te=P*p,ne=v+X+te+O;j=y[ne]}let 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cj(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Ce([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=Rs(r.dtype,a.dtype),d=w.makeZerosTypedArray(w.sizeFromShape(r.shape),c),p=0,h=o===0||o>1||r.shape.length===1?1:w.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=l[f]:d[p++]=u[f];return n.makeTensorInfo(r.shape,c,d)}var dj={kernelName:Cl,backendName:"cpu",kernelFunc:cj},pj=_.SELU_SCALEALPHA,hj=_.SELU_SCALE,fj=pt(Tl,e=>e>=0?hj*e:pj*(Math.exp(e)-1)),mj={kernelName:Tl,backendName:"cpu",kernelFunc:fj},gj=pt(Rl,e=>e<0?-1:e>0?1:0),Aj={kernelName:Rl,backendName:"cpu",kernelFunc:gj},yj=pt(Co,e=>Math.sin(e)),xj={kernelName:Co,backendName:"cpu",kernelFunc:yj},bj=pt(El,e=>Math.sinh(e)),vj={kernelName:El,backendName:"cpu",kernelFunc:bj},wj=11920928955078125e-23,d6=Math.log(wj)+2,kj=pt(Dl,e=>{let t=e>-d6,n=e<d6,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),Ij={kernelName:Dl,backendName:"cpu",kernelFunc:kj};function Sj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Ce([r],"spaceToBatchND");let i=w.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let u=u6.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),c=_.getReshaped(u.shape,a,i,!1),d=_.getPermuted(c.length,a.length,!1),p=_.getReshapedPermuted(u.shape,a,i,!1),m=wt({inputs:{x:u},backend:n,attrs:{shape:c}}),y=ws({inputs:{x:m},backend:n,attrs:{perm:d}}),v=wt({inputs:{x:y},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),v}var Cj={kernelName:_l,backendName:"cpu",kernelFunc:Sj};function Tj(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
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|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
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|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values[0],[d,p,h,f,m]=P7(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Nj={kernelName:Zp,backendName:"cpu",kernelFunc:Tj};function Ej(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[u,c,d]=M7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Rj={kernelName:Yp,backendName:"cpu",kernelFunc:Ej};function Dj(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=a2(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var _j={kernelName:Jp,backendName:"cpu",kernelFunc:Dj};function Fj(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[u,c]=a2(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var $j={kernelName:Qp,backendName:"cpu",kernelFunc:Fj};function Oj(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:d,outputSize:p}=_.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],A=c6(f,m,i,p,c,u,l,d,g,h);return n.makeTensorInfo(i,A.dtype,A.values)}var Pj={kernelName:eh,backendName:"cpu",kernelFunc:Oj};function Mj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=_.prepareSplitSize(r,a,i),u=new Array(r.shape.length).fill(0),c=r.shape.slice();return l.map(d=>{let p=[...c];p[i]=d;let h=di({inputs:{x:r},backend:n,attrs:{begin:u,size:p}});return u[i]+=d,h})}var zj={kernelName:Fl,backendName:"cpu",kernelFunc:Mj},Lj={kernelName:Ac,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;Ce(n,"square");let r=s.data.get(n.dataId).values,a=new Float32Array(r.length);for(let i=0;i<r.length;++i){let l=r[i];a[i]=l*l}return{dataId:s.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},Bj=pt(sa,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),Wj={kernelName:sa,backendName:"cpu",kernelFunc:Bj};function Vj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s;Ce(r,"stridedSlice");let{nonStrided:h,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=En.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=wt({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=di({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=wt({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else{let 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s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function Fq(e,t){let n=f2(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function M6(e){return e!==2?!1:Ar(e).fenceSync!=null}function bu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=Y();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>A2(2)?2:A2(1)?1:0);Fe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Fe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Fe.get("WEBGL_VERSION")===2);Fe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Fe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Fe.registerFlag("WEBGL_PACK",()=>Fe.getBool("HAS_WEBGL"));Fe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_CLIP",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_REDUCE",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_CONV_IM2COL",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>_6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>F6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:$6(e)});Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Fe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Nc.isMobile());Fe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>O6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Fe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Fe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Fe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>P6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>M6(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Fe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Fe.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}.`)});Fe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Nc.isMobile()&&Fe.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Fe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Fe.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Fe.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Fe.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Fn(){let e,t,n,s,r,a,o,i,l,u;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,l="",u=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,u=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function mi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function n0(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function $q(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Oq(e,t,n="index"){let s=e.map((a,o)=>o),r=$q(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function x2(e){let t=w.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function b2(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var z6=`
|
|
const float FLOAT_MAX = 1.70141184e38;
|
|
const float FLOAT_MIN = 1.17549435e-38;
|
|
|
|
lowp vec4 encode_float(highp float v) {
|
|
if (isnan(v)) {
|
|
return vec4(255, 255, 255, 255);
|
|
}
|
|
|
|
highp float av = abs(v);
|
|
|
|
if(av < FLOAT_MIN) {
|
|
return vec4(0.0, 0.0, 0.0, 0.0);
|
|
} else if(v > FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
|
|
} else if(v < -FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
|
|
}
|
|
|
|
highp vec4 c = vec4(0,0,0,0);
|
|
|
|
highp float e = floor(log2(av));
|
|
highp float m = exp2(fract(log2(av))) - 1.0;
|
|
|
|
c[2] = floor(128.0 * m);
|
|
m -= c[2] / 128.0;
|
|
c[1] = floor(32768.0 * m);
|
|
m -= c[1] / 32768.0;
|
|
c[0] = floor(8388608.0 * m);
|
|
|
|
highp float ebias = e + 127.0;
|
|
c[3] = floor(ebias / 2.0);
|
|
ebias -= c[3] * 2.0;
|
|
c[2] += floor(ebias) * 128.0;
|
|
|
|
c[3] += 128.0 * step(0.0, -v);
|
|
|
|
return c / 255.0;
|
|
}
|
|
`,{getBroadcastDims:L6}=_;function Pq(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=v2(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>Mq(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Fn(),l=Bq(i),u,c,d=Uq(i);return t.isPacked?(u=zq(t.logicalShape,o,n.enableShapeUniforms),c=Vq(i)):(u=Lq(t.logicalShape,o,n.enableShapeUniforms),c=Wq(i)),n.packedInputs&&(d+=qq),[d,l,c,r,u,a,n.userCode].join(`
|
|
`)}function vu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return aX(e,t);case 1:return iX(e,t);case 2:return uX(e,t);case 3:return dX(e,t);case 4:return hX(e,t);case 5:return fX(e);case 6:return mX(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function B6(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return rX(e);case 1:return oX(e,t);case 2:return lX(e,t);case 3:return cX(e,t);default:return pX(e,t)}}function Mq(e,t,n=!1,s){let r="";n?r+=B6(e,s):r+=vu(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=gX(e,t):r+=AX(e,t)),r}function zq(e,t,n){switch(e.length){case 0:return W6();case 1:return Xq(e,t,n);case 2:return nX(e,t,n);case 3:return Zq(e,t,n);default:return Jq(e,t,n)}}function Lq(e,t,n){switch(e.length){case 0:return W6();case 1:return Kq(e,t,n);case 2:return sX(e,t,n);case 3:return Yq(e,t,n);case 4:return Qq(e,t,n);case 5:return eX(e,t);case 6:return tX(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Bq(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function Wq(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function Vq(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function Uq(e){return`${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);
|
|
}
|
|
|
|
${Hq}
|
|
${Gq}
|
|
${jq}
|
|
`}var Hq=`
|
|
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);
|
|
}
|
|
`,Gq=`
|
|
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);
|
|
}
|
|
`,jq=`
|
|
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);
|
|
}
|
|
`,qq=`
|
|
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 W6(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function Xq(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${s[1]}.0);
|
|
}
|
|
`:s[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${s[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
|
|
}
|
|
`}function Kq(e,t,n){return t[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function Zq(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function Yq(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${n0(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=mi(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function Jq(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
|
|
int b${u} = index / ${o};
|
|
index -= b${u} * ${o};
|
|
`+i,l=`b${u}, `+l;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec${e.length}(${l});
|
|
}
|
|
`}function Qq(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${n0(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=mi(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function eX(e,t){let n=mi(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function tX(e,t){let n=mi(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function nX(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function sX(e,t,n){return w.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function gi(e){return`offset${e}`}function rX(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Fn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function aX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
|
|
float ${s}() {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let o=gi(n);if(t)return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let[i,l]=e.shapeInfo.texShape;return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function oX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Fn();if(t)return`
|
|
vec4 ${s}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${s}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${o[0]}, ${o[1]}, index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`}function iX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${wu(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
|
|
float ${s}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=gi(n);return o===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:a===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function lX(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Fn();if(a!=null&&w.arraysEqual(n,a))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`}function uX(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let p=a[0],h=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}let{newShape:o,keptDims:i}=w.squeezeShape(n),l=o;if(l.length<n.length){let p=ku(e,l),h=["row","col"];return`
|
|
${vu(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${Iu(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${wu(e)}
|
|
}
|
|
`;let u=a[0],c=a[1],d=gi(s);return c===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s}Shape[1] + col + ${d};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${u}, ${c}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function cX(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=ku(e,p),m=["b","row","col"];return`
|
|
${B6(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${Iu(m,h)});
|
|
}
|
|
`}let i=Fn();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`;let l=o[0],u=o[1],c=Math.ceil(n[2]/2),d=c*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${u}, ${d}, ${c}, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`}function dX(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=w.squeezeShape(n),u=i;if(u.length<n.length){let m=ku(e,u),g=["row","col","depth"];return`
|
|
${vu(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${Iu(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${a}, ${o}, 1)));
|
|
${wu(e)}
|
|
}
|
|
`;let c=e.shapeInfo.texShape,d=c[0],p=c[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${s}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(p===o&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let f=gi(s);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${s}Shape[1] * ${s}Shape[2];
|
|
int stride1 = ${s}Shape[2];
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function pX(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Fn();if(t)return`
|
|
vec4 ${s}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${n}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
|
|
vec4 ${s}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${c};
|
|
int texC = index - texR * ${c};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function hX(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=w.squeezeShape(n);if(l.length<n.length){let y=ku(e,l),x=["row","col","depth","depth2"];return`
|
|
${vu(y,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${Iu(x,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, 1)));
|
|
${wu(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${o}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(h===a&&c==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n[1]*n[2]}, ${n[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let A=gi(s);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} +
|
|
depth * ${a} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function fX(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=ku(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${vu(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${Iu(g,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, ${r})) +
|
|
depth3;
|
|
${wu(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&c==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${o}, ${a}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&c==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=gi(n);return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} + depth * ${a} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function mX(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ku(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${vu(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${Iu(A,a)});
|
|
}
|
|
`}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${o}, 1)));
|
|
${wu(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===c&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${i}, ${o})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===o&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=gi(n);return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${c} + col * ${u} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function wu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function gX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=L6(e.shapeInfo.logicalShape,t.logicalShape),l=yt(o),u=o-a,c,d=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${d[y+u]} = 0;`).join(`
|
|
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,x)=>`coords.${d[x+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,A=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!A)o===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let y=a-2,x=a-1;i.indexOf(y)>-1&&i.indexOf(x)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${s}(${p});
|
|
${h}
|
|
}
|
|
`}function AX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let u=yt(l),c=L6(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&c.length>=1?p="coords = 0;":p=c.map(m=>`coords.${h[m+d]} = 0;`).join(`
|
|
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
|
|
float ${r}() {
|
|
${u} coords = getOutputCoords();
|
|
${p}
|
|
return get${s}(${f});
|
|
}
|
|
`}function yt(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 ${e} is not yet supported`)}function v2(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function ku(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Iu(e,t){return t.map(n=>e[n]).join(", ")}function yX(e,t,n,s){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=Pq(r,o,t),l=e.createProgram(i),u=null,c=e.getUniformLocation(l,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(l,"INFINITY",!1));let d=!1,p={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];p[b]=e.getUniformLocation(l,b,d),p[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,g,A;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),A=e.getUniformLocation(l,"outShapeStrides",d),g=e.getUniformLocation(l,"outTexShape",d));let y=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{y[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:p,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:c,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:A,outTexShapeLocation:g}}function V6(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function xX(e,t,n,s,r){t.program.enableShapeUniforms||(V6(t.inShapeInfos,n),V6([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],d=t.uniformLocations[c],p=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=v2(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],d=r[u];if(l.type==="float")e.gl.uniform1fv(c,d);else if(l.type==="vec2")e.gl.uniform2fv(c,d);else if(l.type==="vec3")e.gl.uniform3fv(c,d);else if(l.type==="vec4")e.gl.uniform4fv(c,d);else if(l.type==="int")e.gl.uniform1iv(c,d);else if(l.type==="ivec2")e.gl.uniform2iv(c,d);else if(l.type==="ivec3")e.gl.uniform3iv(c,d);else if(l.type==="ivec4")e.gl.uniform4iv(c,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function bX(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:d}=v2(e.packedInputs,o.shape,l),p="",h="",f="";if(c.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let v=w.computeStrides(c);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,l),A=w.sizeFromShape(o.shape)===1,y=_.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${u?d:""}_${c.length}_${A}_${y}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Y().getNumber("WEBGL_VERSION")}`,a}function Ss(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var vX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=bd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Fn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?n0(["r","c","d"],e):mi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},wX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=bd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Fn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?n0(["r","c","d"],e):mi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},kX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ks.DOWNLOAD;let t=Fn();this.outputShape=e,this.userCode=`
|
|
${z6}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},IX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ks.DOWNLOAD;let t=Fn();this.outputShape=e,this.userCode=`
|
|
${z6}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},SX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Fn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?b2():x2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
vec4 values = ${n.texture2D}(A, uv);
|
|
|
|
float result;
|
|
|
|
if(offset == 0) {
|
|
result = values[0];
|
|
} else if(offset == 1) {
|
|
result = values[1];
|
|
} else if(offset == 2) {
|
|
result = values[2];
|
|
} else {
|
|
result = values[3];
|
|
}
|
|
|
|
${n.output} = vec4(${s}, 0., 0., 0.);
|
|
}
|
|
`}},CX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Fn();this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
|
|
localCoords[2] += ${o};
|
|
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
|
|
localCoords[1] += ${a};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${i}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${i}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${i}] = values[2];
|
|
} else {
|
|
result[${i}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?b2():x2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},U6={};Le(U6,{bindVertexProgramAttributeStreams:()=>J6,createBufferFromOutputTexture:()=>t4,createFloat16MatrixTexture:()=>X6,createFloat16PackedMatrixTexture:()=>Y6,createFloat32MatrixTexture:()=>q6,createIndexBuffer:()=>j6,createPackedMatrixTexture:()=>Z6,createUnsignedBytesMatrixTexture:()=>K6,createVertexBuffer:()=>G6,createVertexShader:()=>H6,downloadByteEncodedFloatMatrixFromOutputTexture:()=>s4,downloadFloat32MatrixFromBuffer:()=>n4,downloadMatrixFromPackedOutputTexture:()=>a4,downloadPackedMatrixFromBuffer:()=>r4,getInternalFormatForFloat16MatrixTexture:()=>k2,getInternalFormatForFloat16PackedMatrixTexture:()=>C2,getInternalFormatForFloat32MatrixTexture:()=>w2,getInternalFormatForPackedMatrixTexture:()=>S2,getInternalFormatForUnsignedBytesMatrixTexture:()=>I2,uploadDenseMatrixToTexture:()=>Q6,uploadPixelDataToTexture:()=>e4});function H6(e){let t=Fn(),n=`${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 g6(e,n)}function G6(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 b6(e,t)}function j6(e){let t=new Uint16Array([0,1,2,2,1,3]);return v6(e,t)}function Sd(e,t,n,s,r,a){k6(t,n);let o=w6(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function w2(e){return e.internalFormatFloat}function q6(e,t,n,s){let[r,a]=vd(t,n);return Sd(e,r,a,w2(s),s.textureFormatFloat,e.FLOAT)}function k2(e){return e.internalFormatHalfFloat}function X6(e,t,n,s){let[r,a]=vd(t,n);return Sd(e,r,a,k2(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function I2(e){return e.downloadTextureFormat}function K6(e,t,n,s){let[r,a]=vd(t,n);return Sd(e,r,a,I2(s),e.RGBA,e.UNSIGNED_BYTE)}function S2(e){return e.internalFormatPackedFloat}function Z6(e,t,n,s){let[r,a]=xu(t,n);return Sd(e,r,a,S2(s),e.RGBA,e.FLOAT)}function C2(e){return e.internalFormatPackedHalfFloat}function Y6(e,t,n,s){let[r,a]=xu(t,n);return Sd(e,r,a,C2(s),e.RGBA,s.textureTypeHalfFloat)}function J6(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),m2(e,t,"clipSpacePos",n,3,a,s)&&m2(e,t,"uv",n,2,a,r)}function Q6(e,t,n,s,r,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function e4(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function t4(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function n4(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function s4(e,t,n,s){let[r,a]=vd(t,n),o=4,i=new Uint8Array(vq(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function r4(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(wq(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function a4(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var s0=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Xf(t,e)):this.gl=Ar(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=wd(this.gl,r),Is(this.gl,a))this.textureHalfFloatExtension=wd(this.gl,a);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Is(this.gl,s))this.colorBufferHalfFloatExtension=wd(this.gl,s);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Is(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Is(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=G6(this.gl),this.indexBuffer=j6(this.gl),this.framebuffer=I6(this.gl),this.textureConfig=f2(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),q6(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),X6(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),K6(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),e4(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),Q6(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Y6(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Z6(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(g2(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>s4(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return r4(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return n4(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=t4(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>a4(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=A6(t,e);this.vertexShader==null&&(this.vertexShader=H6(t));let s=y6(t);return Ie(t,()=>t.attachShader(s,this.vertexShader)),Ie(t,()=>t.attachShader(s,n)),x6(t,s),this.debug&&Zf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=J6(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Zf(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?C6(this.gl,e,t):T6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(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(),N6(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=xu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Zf(this.gl,this.program),kd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=wd(this.gl,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=TX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Yf(this.gl,e,this.framebuffer),this.debug&&kd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Yf(this.gl,this.outputTexture,this.framebuffer),this.debug&&kd(this.gl)):g2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;Yf(s,e,this.framebuffer),this.debug&&kd(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function TX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:NX,bincountImpl:o4,bincountReduceImpl:EX,ceilImpl:RX,concatImpl:DX,equalImpl:_X,expImpl:FX,expm1Impl:$X,floorImpl:OX,gatherNdImpl:PX,gatherV2Impl:MX,greaterImpl:zX,greaterEqualImpl:LX,lessImpl:BX,lessEqualImpl:WX,linSpaceImpl:VX,logImpl:UX,maxImpl:HX,maximumImpl:GX,minimumImpl:jX,multiplyImpl:qX,negImpl:XX,notEqualImpl:KX,prodImpl:ZX,rangeImpl:YX,rsqrtImpl:JX,sigmoidImpl:QX,simpleAbsImpl:i4,sliceImpl:eK,sparseFillEmptyRowsImpl:tK,sparseReshapeImpl:nK,sparseSegmentReductionImpl:l4,sqrtImpl:sK,stridedSliceImpl:rK,stringNGramsImpl:aK,stringSplitImpl:oK,stringToHashBucketFastImpl:iK,subImpl:lK,tileImpl:uK,topKImpl:cK,transposeImpl:T2,uniqueImpl:dK}=u7;function u4(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function $n(e,t){return t===1?[e]:u4(e,t)}function pK(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var hK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let n=$n("rc",t),s=yt(t),r=mK(t,e,n),a=gK(t,e[e.length-1],e[e.length-2],n),o=AK(e,n);this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${o}));
|
|
}
|
|
}
|
|
`}}};function fK(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function mK(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function gK(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
|
|
int r = ${r[0]};
|
|
int c = ${r[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function AK(e,t){let n=e.length,s=fK(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${s[0]}),
|
|
cEdge ? 0. : getA(${s[1]}),
|
|
rEdge ? 0. : getA(${s[2]}),
|
|
rEdge || cEdge ? 0. : getA(${s[3]})`}var c4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${s}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${s>0?"}":""}
|
|
`}this.userCode=`
|
|
${yK(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?b2():x2(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function yK(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?Oq(["r","c","d"],"inputShape"):mi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var xK=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=p4(t,n),r=h4(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=d4(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===bn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===bn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===bn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===bn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===bn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=p4(n,s),a=h4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=d4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function bK(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function d4(e,t,n,s,r){let a=vK(t,s),o;if(r){let[l,u]=xu(e[0],e[1]);o=l*u}else{let[l,u]=vd(e[0],e[1]);o=l*u}let i=bK(n,a);return o*i}function vK(e,t){switch(e){case bn.PACKED_2X2_FLOAT32:return S2(t);case bn.PACKED_2X2_FLOAT16:return C2(t);case bn.UNPACKED_FLOAT32:return w2(t);case bn.UNPACKED_FLOAT16:return k2(t);case bn.PACKED_4X1_UNSIGNED_BYTE:return I2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function wK(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?bn.PACKED_2X2_FLOAT32:bn.UNPACKED_FLOAT32:e?bn.PACKED_2X2_FLOAT16:bn.UNPACKED_FLOAT16}function p4(e,t){if(e===ks.UPLOAD)return bn.PACKED_2X2_FLOAT32;if(e===ks.RENDER||e==null)return wK(t);if(e===ks.DOWNLOAD||e===ks.PIXELS)return bn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function h4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ia=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},Js="if (isnan(x)) return x;",kK="return x;",f4="return abs(x);",IK="return (x >= 0.0) ? x : (exp(x) - 1.0);",SK=Js+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,CK=Js+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,r0="return x;",TK="return 1.0 / (1.0 + exp(-1.0 * x));",NK="return x;",EK=`
|
|
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;
|
|
`,RK=`
|
|
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;
|
|
`,DK=`
|
|
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;
|
|
`,_K="return 1.0 / (1.0 + exp(-1.0 * x));",Su=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},FK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=$n("rc",t),s=yt(t),r=pK(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},$K=cr.whereImpl,OK=1e-7,PK=1e-4,a0={};function MK(e){return e in a0||(a0[e]={}),a0[e]}var zK=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),LK=600;function BK(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*LK/1024/1024}var Cu=class extends nc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Ar(Y().getNumber("WEBGL_VERSION"));this.binaryCache=MK(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new s0(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 xK(this.gpgpu),this.numMBBeforeWarning=BK(),this.texData=new yp(this,es())}nextDataId(){return Cu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:ks.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:ks.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Su(o,r0):d=new Ia(o,r0);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);c=_.mergeRealAndImagArrays(d,p)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new Su(s,r0):h=new Ia(s,r0);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Kf(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=_.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&es().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return je(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!f6(n))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Kf(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Jf(t):t,i=a?new IX(o):new kX(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=zK){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return $K(e.shape,t)}packedUnaryOp(e,t,n){let s=new Su(e.shape,t),r=this.compileAndRun(s,[e],n);return es().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=i4(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,f4,e.dtype);let t=new Ia(e.shape,f4),n=this.compileAndRun(t,[e]);return es().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return es().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new FK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new hK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[hi(e.shape),...fi(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[hi(t),...fi(t)],a=new c4(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=Jf(s),o,i=Kf(a);n?o=new wX(a):o=new vX(a);let l=!0,u=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:s,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===bd.DENSE){let m=Kf(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&w.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!Id(g.shape,m.shape)){let A=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),A.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},c=bX(e,l,u),d=this.getAndSaveBinary(c,()=>yX(this.gpgpu,e,l,u)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),xX(this.gpgpu,d,l,u,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=H(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Te(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?OK:PK}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let c=t.texShape;if(c==null&&(c=D6(n,i),t.texShape=c),r!=null){let d=Jf(n),p,h=c[1],f=c[0],m=r instanceof Uint8Array;i?([h,f]=xu(c[0],c[1]),p=new CX(d,m)):p=new SX(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=ks.PIXELS:this.texData.get(g.dataId).usage=ks.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],y=!0,x=this.runWebGLProgram(p,[g],s,A,y),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(c,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=WK(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}};Cu.nextDataId=0;function WK(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var VK="3.9.0";function m4(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Nc.isBrowser()&&ql("webgl",()=>new Cu,2);var UK={forceHalfFloat:m4},g4=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Tu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Ss(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},o0=`
|
|
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;
|
|
`,Cd=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Ss(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${yt(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?a+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=$n("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function ds(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var HK={kernelName:ro,backendName:"webgl",kernelFunc:ds};function Sa(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=ds({inputs:{x:s},backend:n}),l=ds({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var GK={kernelName:Cp,backendName:"webgl",kernelFunc:Sa},A4="return (a < 0.) ? b * a : a;",y4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function jK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cd(y4,r.shape,o.shape):new Tu(A4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var qK={kernelName:ao,backendName:"webgl",kernelFunc:jK},x4="return (a < 0.) ? b * a : a;",b4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function XK(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cd(b4,s.shape,r.shape):new Tu(x4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var KK={kernelName:xo,backendName:"webgl",kernelFunc:XK},v4="if (isnan(x)) return x;",ZK=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,YK=`
|
|
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 tt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Su(o.shape,t):c=new Ia(o.shape,e),i.runWebGLProgram(c,[o],l)}}function vn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},C=new Tu(e,l.shape,u.shape);return c.runWebGLProgram(C,[k,S],Rs(b.dtype,v.dtype))}),y=Sa({inputs:{real:g,imag:A},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(A),y}let d=a||Rs(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?_.fromUint8ToStringArray(f):f,A=l.dtype==="string"?_.fromUint8ToStringArray(m):m,[y,x]=r(l.shape,u.shape,g,A,d),b=c.makeTensorInfo(x,d),v=c.texData.get(b.dataId);return v.values=y,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Cd(t,l.shape,u.shape,n):h=new Tu(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],d)}}function i0(e,t=!1){if(e==="linear")return t?NK:kK;if(e==="relu")return t?RK:SK;if(e==="elu")return t?EK:IK;if(e==="relu6")return t?DK:CK;if(e==="prelu")return t?b4:x4;if(e==="leakyrelu")return t?y4:A4;if(e==="sigmoid")return t?_K:TK;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var w4=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Ss(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${o}
|
|
}`,g="result = activation(result);");let A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let y="rc.x",x="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${c}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${c}; i++) {
|
|
int batchA = ${y};
|
|
int batchB = ${x};
|
|
vec4 a = getMatrixA(batchA, ${d});
|
|
vec4 b = getMatrixB(batchB, ${p});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${f[0]});
|
|
result += (${h[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${A}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},k4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},I4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},S4="return a * b;";function N2(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=_.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new I4(k4.REAL,s.shape,r.shape),c=new I4(k4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=Sa({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=qX(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(c,a),p=n.texData.get(d.dataId);return p.values=u,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Cd(S4,s.shape,r.shape):o=new Tu(S4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var JK={kernelName:mo,backendName:"webgl",kernelFunc:N2};function QK(e,t,n){let s=[hi(e.shape),...fi(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[hi(t),...fi(t)],o=new c4(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(l);w.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Id(r.shape,l)&&!(c.texture!==null&&Id(c.shape,l))?QK(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var eZ={kernelName:Il,backendName:"webgl",kernelFunc:ve},C4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${u}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${o}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${o};
|
|
if (${i===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},tZ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,d=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,p="vec4";t==="all"?(o="1.0",d=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,p="bvec4"):t==="any"&&(o="0.0",d=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,p="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${o});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${c===1}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===2}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===3}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function nZ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Ai(e,t,n,s){let r=nZ(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,d;n==="mean"?c=o===0?new C4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new C4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new tZ({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),d=a,a=s.runWebGLProgram(c,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var sZ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=yt(this.rank),r=rZ(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function rZ(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var aZ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=yt(this.rank),r=u4("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${i}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${i}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function l0(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aZ(e.shape,t):new sZ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function oZ(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,l=_.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=l0(e,l,s),i=_.getInnerMostAxes(i.length,a)),_.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=_.computeOutAndReduceShapes(c.shape,i),h=d;n&&(h=_.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,A=ve({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=dh(e.dtype),x=Ai(A,y,"sum",s),b=ve({inputs:{x},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(x),u&&s.disposeIntermediateTensorInfo(c),b}function u0(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return oZ(r,a,o,n)}var iZ={kernelName:Eo,backendName:"webgl",kernelFunc:u0};function On(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=T2(d,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(u.dataId);h.values=p}else u=l0(r,a,o);return u}var lZ={kernelName:Oo,backendName:"webgl",kernelFunc:On},T4=1e3;function c0({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],p=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=w.sizeFromShape(m),y=w.sizeFromShape(g),x=A===y||A===1||y===1;w.assert(u>=2&&c>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let v=(A>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[A,d,h]:[A,h,d],S=s?[y,f,p]:[y,p,f],C=ve({inputs:{x:e},backend:r,attrs:{shape:k}}),D=ve({inputs:{x:t},backend:r,attrs:{shape:S}}),O=[C,D],E=Math.max(A,y),R=n?C.shape[1]:C.shape[2],T=a!=null,P=o!=null,U=l==="leakyrelu",j=l!=null?i0(l,!0):null,q=T||P||U||j!=null,X;if((h===1||f===1)&&R>T4&&q===!1){let ne=C,se=D;n&&(ne=On({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),O.push(ne)),s&&(se=On({inputs:{x:D},backend:r,attrs:{perm:[0,2,1]}}),O.push(se));let ae=f!==1,Q=f===1,ce=ne;ae&&(ce=ve({inputs:{x:ne},backend:r,attrs:{shape:[E,R,1]}}),O.push(ce));let de=f===1?2:1,fe=se;Q&&(fe=ve({inputs:{x:se},backend:r,attrs:{shape:[E,1,R]}}),O.push(fe));let be=N2({inputs:{a:ce,b:fe},backend:r});X=u0({inputs:{x:be},backend:r,attrs:{axis:de,keepDims:!0}}),O.push(be)}else{let ne=Rs(e.dtype,t.dtype),se=new w4(k,S,[E,h,f],n,s,T,j,P,U),ae=[C,D];if(a!=null&&ae.push(a),P&&ae.push(o),U){let Q=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));ae.push(Q),O.push(Q)}X=r.runWebGLProgram(se,ae,ne)}let te=ve({inputs:{x:X},backend:r,attrs:{shape:v}});O.push(X);for(let ne of O)r.disposeIntermediateTensorInfo(ne);return te}function uZ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s;return c0({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}var cZ={kernelName:Po,backendName:"webgl",kernelFunc:uZ},N4="return abs(x);";function dZ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=i4(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Su(s.shape,N4):r=new Ia(s.shape,N4),n.runWebGLProgram(r,[s],s.dtype)}var pZ={kernelName:Li,backendName:"webgl",kernelFunc:dZ},hZ=Js+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,fZ=tt({opSnippet:hZ}),mZ={kernelName:Bi,backendName:"webgl",kernelFunc:fZ},gZ=Js+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,AZ=tt({opSnippet:gZ}),yZ={kernelName:Wi,backendName:"webgl",kernelFunc:AZ},E4="return a + b;",xZ=vn({opSnippet:E4,packedOpSnippet:E4,supportsComplex:!0,cpuKernelImpl:NX}),bZ={kernelName:ea,backendName:"webgl",kernelFunc:xZ},vZ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},wZ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}};function d0(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ds({inputs:{x:s[0]},backend:n});if(s.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=d0({inputs:s.slice(0,l),backend:n}),c=d0({inputs:s.slice(l),backend:n});return d0({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Rs(l,u)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new wZ(s[0].shape,a):new vZ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var kZ={kernelName:La,backendName:"webgl",kernelFunc:d0};function IZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=r;c!=null&&(d=On({inputs:{x:r},backend:n,attrs:{perm:c}}),u=_.getInnerMostAxes(u.length,i)),_.assertAxesAreInnerMostDims("all",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Ai(m,m.dtype,"all",n),A;if(o){let y=_.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var SZ={kernelName:Vi,backendName:"webgl",kernelFunc:IZ};function CZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=r;c!=null&&(d=On({inputs:{x:r},backend:n,attrs:{perm:c}}),u=_.getInnerMostAxes(u.length,i)),_.assertAxesAreInnerMostDims("any",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Ai(m,m.dtype,"any",n),A;if(o){let y=_.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var TZ={kernelName:Ui,backendName:"webgl",kernelFunc:CZ},NZ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${s};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${s}; i++) {
|
|
int inIdx = ${i};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},EZ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=yt(i),u=$n("coords",i),c,d;if(a===1){d=i+1;let S=yt(d);c=`
|
|
${S} sourceLocR = ${S}(${u.join()}, 0);
|
|
++${u[i-1]};
|
|
${S} sourceLocG = ${S}(${u.join()}, 0);
|
|
++${u[i-2]};
|
|
${S} sourceLocA = ${S}(${u.join()}, 0);
|
|
--${u[i-1]};
|
|
${S} sourceLocB = ${S}(${u.join()}, 0);
|
|
--${u[i-2]};`}else d=i,c=`
|
|
${l} sourceLocR = coords;
|
|
++${u[i-1]};
|
|
${l} sourceLocG = coords;
|
|
++${u[i-2]};
|
|
${l} sourceLocA = coords;
|
|
--${u[i-1]};
|
|
${l} sourceLocB = coords;
|
|
--${u[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=$n("sourceLocR",d-1).concat("inIdx.r"),g=$n("sourceLocG",d-1).concat("inIdx.g"),A=$n("sourceLocB",d-1).concat("inIdx.b"),y=$n("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()})));`,v=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,k=s?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}
|
|
${k}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${u[i-1]} < ${o[i-1]-1};
|
|
bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
|
|
${c}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${v};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${v};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function R4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=_.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new NZ(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let d=R4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function D4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=_.computeOptimalWindowSize(a),i=new EZ(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=D4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function _4(e,t,n,s){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=_.computeOutAndReduceShapes(l.shape,r),d=w.sizeFromShape(c),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=R4(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return D4(e,t,s)}function RZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=On({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=_4(n,l,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var DZ={kernelName:Ba,backendName:"webgl",kernelFunc:RZ};function _Z(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=_.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=On({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=_.getInnerMostAxes(o.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=_4(n,l,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),c}var FZ={kernelName:ac,backendName:"webgl",kernelFunc:_Z},$Z=Js+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,OZ=tt({opSnippet:$Z}),PZ={kernelName:Hi,backendName:"webgl",kernelFunc:OZ},MZ=Js+"return log(x + sqrt(x * x + 1.0));",zZ=tt({opSnippet:MZ}),LZ={kernelName:Gi,backendName:"webgl",kernelFunc:zZ},BZ=Js+`
|
|
return atan(x);
|
|
`,WZ=tt({opSnippet:BZ}),VZ={kernelName:ji,backendName:"webgl",kernelFunc:WZ},UZ=ZK+`
|
|
return atan(a, b);
|
|
`,HZ=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+YK+`
|
|
return result;
|
|
`,GZ=vn({opSnippet:UZ,packedOpSnippet:HZ}),jZ={kernelName:Xi,backendName:"webgl",kernelFunc:GZ},qZ=Js+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,XZ=tt({opSnippet:qZ}),KZ={kernelName:qi,backendName:"webgl",kernelFunc:XZ},Td=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
wC += ${u}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${S} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,v=a%4,k=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${y}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
const float initializationValue = ${A};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${A});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
getValue(batch, xR, xC + 3 * ${u}, d)
|
|
);
|
|
|
|
${k}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${v===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},E2=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${A});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${c}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${d}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xD, xR, xC, ch);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${D} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,C=`
|
|
if (${y}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${A});
|
|
const float initializationValue = ${x};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${x});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${c}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${k}; wC += 4) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${S===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${S===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${S===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${v});
|
|
}
|
|
}
|
|
`}};function ZZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;bu(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return ds({inputs:{x:r},backend:n});let d=new Td(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var YZ={kernelName:Wa,backendName:"webgl",kernelFunc:ZZ};function JZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=_.computePool3DInfo(r.shape,a,o,c,i,l,u),p=new E2(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var QZ={kernelName:oc,backendName:"webgl",kernelFunc:JZ},eY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${u}, ${c});
|
|
const float avgMultiplier = float(${d});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${i};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC+= ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},tY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${f}, ${m});
|
|
const float avgMultiplier = float(${g});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${i}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${u}) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function nY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,l,d,u,c),h=new tY(p);return n.runWebGLProgram(h,[r],o.dtype)}var sY={kernelName:Ip,backendName:"webgl",kernelFunc:nY};function rY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;bu([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=_.computePool2DInfo(o.shape,i,l,1,u),d=new eY(c);return n.runWebGLProgram(d,[r],o.dtype)}var aY={kernelName:kp,backendName:"webgl",kernelFunc:rY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return c0({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var iY={kernelName:Va,backendName:"webgl",kernelFunc:oY},lY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${o};
|
|
float scale = ${i};
|
|
float inv = scale * inversesqrt(variance + float(${a}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},uY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${o};
|
|
vec4 scale = ${i};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},cY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new uY(s.shape,r.shape,a.shape,c,d,l):new lY(s.shape,r.shape,a.shape,c,d,l);return t.runWebGLProgram(p,u,u[0].dtype)},dY={kernelName:no,backendName:"webgl",kernelFunc:cY},pY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=hY(this.rank),s,r=e.map((a,o)=>`sourceLoc.${R2[o]} = start[${o}] + coords.${R2[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},R2=["x","y","z","w","u","v"];function hY(e){if(e===1)return"sourceLoc";if(e<=6)return R2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var fY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),n=$n("coords",this.rank),s=$n("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
|
|
result.x = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.y = ${a};
|
|
--${s[this.rank-1]};
|
|
}
|
|
`,i=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${s[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${o}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function mY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=En.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function Nu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=En.parseSliceParams(r,a,o);if(En.assertParamsValid(r,i,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=eK(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:u}=n.texData.get(r.dataId),c=En.isSliceContinous(r.shape,i,l);if(u||!c){let d=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fY(l):new pY(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),mY(r,i,l,n)}var gY={kernelName:Nl,backendName:"webgl",kernelFunc:Nu},AY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=On({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:c}}),A=Nu({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},yY={kernelName:Ki,backendName:"webgl",kernelFunc:AY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),u=o4(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var bY={kernelName:Sp,backendName:"webgl",kernelFunc:xY},vY="return float(a != b);",F4=vn({opSnippet:vY,cpuKernelImpl:KX,dtype:"bool"}),wY={kernelName:gl,backendName:"webgl",kernelFunc:F4};function Nd(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ds({inputs:{x:r.complexTensorInfos.real},backend:n})}var kY={kernelName:qp,backendName:"webgl",kernelFunc:Nd},IY="return float(int(x));";function SY(e,t){let n=new Ia(e.shape,IY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ds({inputs:{x:r},backend:n});let o=Mt(r.shape),i=D2({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Sa({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Nd({inputs:{input:r},backend:n}),i=D2({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=ds({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return SY(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),l=F4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var CY={kernelName:Ua,backendName:"webgl",kernelFunc:D2},$4="return ceil(x);",TY=tt({opSnippet:$4,packedOpSnippet:$4,cpuKernelImpl:RX}),NY={kernelName:Ha,backendName:"webgl",kernelFunc:TY},EY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}},RY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function DY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Y().getBool("WEBGL_PACK_CLIP")?i=new RY(r.shape):i=new EY(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var _Y={kernelName:ta,backendName:"webgl",kernelFunc:DY},FY=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 O4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function $Y(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new FY(s.shape),o=[O4(s,r.complexTensorInfos.real),O4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var OY={kernelName:ic,backendName:"webgl",kernelFunc:$Y},PY=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},MY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=yt(s),a=$n("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),d=`if (${l} < ${i[0]}) {
|
|
return getChannel(
|
|
getT0(${c}), vec2(${u.join()}));
|
|
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
|
|
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${p0(o,l,m)}),
|
|
vec2(${p0(u,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${p0(o,l,h)}),
|
|
vec2(${p0(u,l,h)}));`,this.userCode=`
|
|
float getValue(${o.map(f=>"int "+f)}) {
|
|
${d}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[s-1]} = ${a[s-1]} + 1;
|
|
if (${a[s-1]} < ${n[s-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[s-2]} = ${a[s-2]} + 1;
|
|
if (${a[s-2]} < ${n[s-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[s-1]} = ${a[s-1]} - 1;
|
|
if (${a[s-2]} < ${n[s-2]} &&
|
|
${a[s-1]} < ${n[s-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function p0(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function h0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ds({inputs:{x:r.complexTensorInfos.imag},backend:n})}var zY={kernelName:Bp,backendName:"webgl",kernelFunc:h0};function Eu(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Nd({inputs:{input:m},backend:n})),d=e.map(m=>h0({inputs:{input:m},backend:n})),p=Eu(c,t,n),h=Eu(d,t,n),f=Sa({inputs:{real:p,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(A=>{let y=w.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,y]}})}),d=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=_.computeOutShape(c.map(A=>A.shape),1),h=c[0].shape[0]===1,f=DX(d,p,s,h),m=_.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),d=Eu(e.slice(0,c),t,n),p=Eu(e.slice(c),t,n),h=Eu([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new MY(e.map(d=>d.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=LY(e,t,n),i=new PY(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function LY(e,t,n){let s=_.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function P4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=_.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return ds({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return _.assertParamsConsistent(l,a),Eu(i,a,n)}var BY={kernelName:Zi,backendName:"webgl",kernelFunc:P4},M4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,A=m?2:3,y=m?3:1,x="",b="";n&&(s?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${y}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${A}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${v}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},WY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${a}, ${o});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${s});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d2 = coords.u;
|
|
|
|
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xFCorner = xFRCCorner.x;
|
|
int xRCorner = xFRCCorner.y;
|
|
int xCCorner = xFRCCorner.z;
|
|
|
|
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
|
|
// y(yF, yR, yC, d2). ? = to be determined. : = across all
|
|
// values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${c}; wF++) {
|
|
int xF = xFCorner + wF * ${i};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},VY=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Ss(this.outputShape.length);let{dataFormat:n}=t,s=Fn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
|
|
blockIndex = rc.y + ${c};
|
|
pos = rc.x + ${u};
|
|
|
|
${i}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${a}] && d0 >= 0) {
|
|
// Use custom imod instead mod. On Intel GPU, mod may generate
|
|
// unexpected value.
|
|
// https://github.com/tensorflow/tfjs/issues/5447
|
|
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
|
|
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
|
|
inChannels);
|
|
|
|
if(d1 < inputShape[${o}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${u*2+c}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${u*2+c}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${l}
|
|
|
|
${s.output} = result;
|
|
}
|
|
`}};function z4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,A=[];if(!((d===1||p===1)&&c>T4)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Id(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let S=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let C=c0({a:v,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),D=s.texData.get(C.dataId);w.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,D.shape=n.outShape,g=ds({inputs:{x:C},backend:s}),g.shape=n.outShape,A.push(C)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=c0({a:v,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),A.push(v),A.push(k),A.push(S)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function L4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=p*d,A=[m,g],y=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(k);let S=new VY(A,n),C=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],D=s.runWebGLProgram(S,[v],"float32",C),O=ve({inputs:{x:D},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(D),b.push(O);let E=r!=null,R=a!=null,T=i==="leakyrelu",P=i?i0(i,!0):null,U=new w4(O.shape,k.shape,[1,g,n.outChannels],y,x,E,P,R,T),j=[O,k];if(r&&j.push(r),R&&j.push(a),T){let ne=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(ne),b.push(ne)}let q=s.runWebGLProgram(U,j,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],te=ve({inputs:{x:q},backend:s,attrs:{shape:X}});b.push(q);for(let ne of b)s.disposeIntermediateTensorInfo(ne);return te}function UY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=z4({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=L4({x:r,filter:a,convInfo:p,backend:n});else{let m=new M4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var HY={kernelName:Ga,backendName:"webgl",kernelFunc:UY},GY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int d2 = coords.w;
|
|
|
|
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${a}) {
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
} else {
|
|
float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},jY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${c}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${a}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},qY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${s} - ${o};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},XY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${i}, ${l}, ${u});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${s} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function KY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),h=new GY(p);return n.runWebGLProgram(h,[r,a],"float32")}var ZY={kernelName:Tp,backendName:"webgl",kernelFunc:KY};function YY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,d=_.convertConv2DDataFormat(u),p=_.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),h=new jY(p);return n.runWebGLProgram(h,[r,a],"float32")}var JY={kernelName:ja,backendName:"webgl",kernelFunc:YY};function QY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=_.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new WY(u);return n.runWebGLProgram(c,[r,a],"float32")}var eJ={kernelName:lc,backendName:"webgl",kernelFunc:QY};function tJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=_.computeConv3DInfo(r.shape,l,o,1,i),c=new qY(u);return n.runWebGLProgram(c,[r,a],"float32")}var nJ={kernelName:Np,backendName:"webgl",kernelFunc:tJ};function sJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=_.computeConv3DInfo(l,a.shape,i,1,o),c=new XY(u);return n.runWebGLProgram(c,[r,a],"float32")}var rJ={kernelName:Ep,backendName:"webgl",kernelFunc:sJ},aJ=v4+`
|
|
return cos(x);
|
|
`,oJ=tt({opSnippet:aJ}),iJ={kernelName:qa,backendName:"webgl",kernelFunc:oJ},lJ=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,uJ=tt({opSnippet:lJ}),cJ={kernelName:Xa,backendName:"webgl",kernelFunc:uJ},dJ=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${y});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${a}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${A};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},pJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new dJ(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},hJ={kernelName:Yi,backendName:"webgl",kernelFunc:pJ},B4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${W4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${yt(s)} coords = getOutputCoords();
|
|
int end = ${V4(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${V4(s,"coords")} = idx;
|
|
val += getX(${W4(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function W4(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 ${e} is not yet supported`)}function V4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function fJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,u=_.getAxesPermutation([a],l),c=r;u!=null&&(c=On({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=_.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=c.shape[d],h=ds({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new B4(c.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new B4(c.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=_.getUndoAxesPermutation(u),m=On({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var mJ={kernelName:Ka,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=o4(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=EX(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var AJ={kernelName:Rp,backendName:"webgl",kernelFunc:gJ},yJ=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 xJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=u*a,h=c/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new yJ(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var bJ={kernelName:Ji,backendName:"webgl",kernelFunc:xJ},U4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ss(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,u="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${l}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${i};
|
|
int q = d2 - d1 * ${i};
|
|
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
|
|
for (int wR = 0; wR < ${a}; wR++) {
|
|
int xR = xRCorner + wR * dilations[0];
|
|
|
|
if (xR < 0 || xR >= inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${o}; wC++) {
|
|
int xC = xCCorner + wC * dilations[1];
|
|
|
|
if (xC < 0 || xC >= inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${c}
|
|
${u}
|
|
setOutput(result);
|
|
}
|
|
`}},H4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ss(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,d=c,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)p+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;for(let g=0;g<u;g++){for(let A=0;A<c;A++)p+=`
|
|
xTexelC${A*2} = vec4(0.0);
|
|
xTexelC${A*2}Ready = 0;
|
|
xTexelC${A*2+1} = vec4(0.0);
|
|
xTexelC${A*2+1}Ready = 0;
|
|
xC${A} = vec4(0.0);`;p+=`
|
|
xR = xRCorner + ${g} * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let A=0;A<(d+1)/2;A++){let y=A*2;if(p+=`
|
|
xC = xCCorner + ${y*l};
|
|
`,i===1){if(y<c&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`,l===1&&y>0?p+=`
|
|
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
|
|
`:p+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
|
|
} else {
|
|
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
|
|
}
|
|
`):p+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xC${y} = xTexelC${y};
|
|
`,y+1<c)){let x=o%2==0?w.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
`,l>1&&(p+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`),p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
|
|
`):x===1?p+=`
|
|
xC${y+1} = xTexelC${y};
|
|
`:p+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y+1} = xTexelC${y+1};
|
|
`}}else y<c&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`,y+1<c&&(p+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
|
|
`)):(p+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(
|
|
xTexelC${y}.xy, xTexelC${y+1}.xy);
|
|
`,y+1<c&&(p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`)));y<c&&(p+=`
|
|
wTexel = getW(${g}, ${y}, d1, q);
|
|
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
|
|
`,y+1<c&&(p+=`
|
|
wTexel = getW(${g}, ${y+1}, d1, q);
|
|
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}p+=`
|
|
}
|
|
`}let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:h=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${h}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${a};
|
|
int q = d2 - d1 * ${a};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${p}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${m}
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=_.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new H4(d):p=new U4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var wJ={kernelName:Za,backendName:"webgl",kernelFunc:vJ},kJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int dm = coords.w;
|
|
int d2 = d1 * ${a} + dm;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
// TO DO: Vec4 over the batch size
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},IJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
ivec2 dyCorner = coords.yz - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${i}; dm++) {
|
|
int d2 = d1 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function SJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=_.computeConv2DInfo(r.shape,c,o,i,l,u,!0),p=new kJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var CJ={kernelName:Dp,backendName:"webgl",kernelFunc:SJ};function TJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=_.computeConv2DInfo(c,a.shape,o,i,l,u,!0),p=new IJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var NJ={kernelName:_p,backendName:"webgl",kernelFunc:TJ},EJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function RJ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new EJ(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var DJ={kernelName:Fp,backendName:"webgl",kernelFunc:RJ},_J=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${a});
|
|
const ivec2 pads = ivec2(${c}, ${d});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${o}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${i}; w++) {
|
|
int wIn = wBeg + w * ${u};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function FJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=_.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new _J(u);c=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),p}var $J={kernelName:uc,backendName:"webgl",kernelFunc:FJ};function OJ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=_.decodeEinsumEquation(r,a.length);_.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=_.getEinsumComputePath(i,l),d=c.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of c[m]){let{permutationIndices:A,expandDims:y}=_.getEinsumPermutation(h,l[g]),x;_.isIdentityPermutation(A)?x=a[g]:(x=On({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(x));let b=x.shape.slice();for(let v=0;v<y.length;++v)b.splice(y[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=N2({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(u[m]>=0&&(p=u0({inputs:{x:p},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var PJ={kernelName:Pp,backendName:"webgl",kernelFunc:OJ},MJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",zJ=`
|
|
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;
|
|
`,LJ=tt({opSnippet:MJ,packedOpSnippet:zJ}),BJ={kernelName:Ja,backendName:"webgl",kernelFunc:LJ},WJ="return (b >= 1.0) ? a : a * (b + 1.0);",VJ=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,UJ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cd(VJ,s.shape,r.shape):new Tu(WJ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},HJ={kernelName:Mp,backendName:"webgl",kernelFunc:UJ},GJ=`
|
|
return vec4(equal(a, b));
|
|
`,jJ="return float(a == b);",qJ=vn({opSnippet:jJ,packedOpSnippet:GJ,dtype:"bool",cpuKernelImpl:_X}),XJ={kernelName:el,backendName:"webgl",kernelFunc:qJ},KJ=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${_.ERF_P};
|
|
float a1 = ${_.ERF_A1};
|
|
float a2 = ${_.ERF_A2};
|
|
float a3 = ${_.ERF_A3};
|
|
float a4 = ${_.ERF_A4};
|
|
float a5 = ${_.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,ZJ=tt({opSnippet:KJ}),YJ={kernelName:Qi,backendName:"webgl",kernelFunc:ZJ},G4="return exp(x);",j4=tt({opSnippet:G4,packedOpSnippet:G4,cpuKernelImpl:FX}),JJ={kernelName:Qa,backendName:"webgl",kernelFunc:j4};function _2(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var QJ={kernelName:tl,backendName:"webgl",kernelFunc:_2},q4="return exp(x) - 1.0;",eQ=tt({opSnippet:q4,packedOpSnippet:q4,cpuKernelImpl:$X}),tQ={kernelName:nl,backendName:"webgl",kernelFunc:eQ},X4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${o}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${s});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${s}; i++) {
|
|
// x = (-2|2 * PI / N) * index * i;
|
|
float x = exponentMultiplierTimesIndexRatio * float(i);
|
|
float expR = cos(x);
|
|
float expI = sin(x);
|
|
float real = getReal(batch, i);
|
|
float imag = getImag(batch, i);
|
|
|
|
result +=
|
|
unaryOpComplex(real, expR, imag, expI) / ${a};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function K4(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new X4("real",l,t),c=new X4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(c,d,"float32"),f=Sa({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function nQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return K4(s,!1,n)}var sQ={kernelName:zp,backendName:"webgl",kernelFunc:nQ},rQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}};function Ed(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new rQ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var aQ={kernelName:cc,backendName:"webgl",kernelFunc:Ed},oQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${t} - x - 1;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},iQ={kernelName:sl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new oQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Z4="return floor(x);",lQ=tt({opSnippet:Z4,packedOpSnippet:Z4,cpuKernelImpl:OX}),uQ={kernelName:eo,backendName:"webgl",kernelFunc:lQ},cQ=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,dQ=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,pQ=vn({opSnippet:cQ,packedOpSnippet:dQ,dtype:"int32"}),hQ={kernelName:to,backendName:"webgl",kernelFunc:pQ},fQ=class{constructor(e){this.variableNames=["A"];let t=Fn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},mQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Fn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}.0, ${n}.0);
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},gQ={kernelName:ah,backendName:"webgl",kernelFunc:AQ},Ru;function AQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(Ru==null&&(Ru=document.createElement("canvas").getContext("2d")),Ru.canvas.width=l,Ru.canvas.height=u,Ru.drawImage(r,0,0,l,u),r=Ru.canvas);let p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=ks.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new mQ(d):new fQ(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function yQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=_.convertConv2DDataFormat(c),g=_.computeConv2DInfo(r.shape,a.shape,l,d,u,p,!1,m),A,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))A=z4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=L4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,v=i!=null,k=h==="leakyrelu",S=h?i0(h,!1):null,C=new M4(g,b,S,v,k),D=[r,a];if(o&&D.push(o),i&&D.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));D.push(O),y.push(O)}A=n.runWebGLProgram(C,D,"float32")}let x=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var xQ={kernelName:Mo,backendName:"webgl",kernelFunc:yQ};function bQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=_.computeConv2DInfo(r.shape,a.shape,l,m,u,d,!0),A=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=p?i0(p,A):null,x=[r,a],b=o!=null,v=i!=null,k=p==="leakyrelu";if(b&&x.push(o),v&&x.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(O),f.push(O)}let S;A?S=new H4(g,b,y,v,k):S=new U4(g,b,y,v,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],D=n.runWebGLProgram(S,x,"float32",C);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),D}var vQ={kernelName:zo,backendName:"webgl",kernelFunc:bQ},wQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=yt(t.length),r=yt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${s} strides = ${s}(${this.strides});
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${a};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function kQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[l,u,c,d]=_.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),y=n.bufferSync(s),x=PX(A,y,s.dtype,u,o,c,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new wQ(o,d,[u,c]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var IQ={kernelName:al,backendName:"webgl",kernelFunc:kQ},SQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=yt(this.rank),s=CQ(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function CQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function Y4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=w.parseAxisParam(o,r.shape)[0],u=_.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=w.sizeFromShape(a.shape),d=[],p=ve({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ve({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});d.push(p),d.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),x=n.bufferSync(p),b=MX(x,y,f);return d.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new SQ(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let A=ve({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}var TQ={kernelName:rl,backendName:"webgl",kernelFunc:Y4},NQ="return float(a > b);",EQ=`
|
|
return vec4(greaterThan(a, b));
|
|
`,RQ=vn({opSnippet:NQ,packedOpSnippet:EQ,cpuKernelImpl:zX,dtype:"bool"}),DQ={kernelName:ol,backendName:"webgl",kernelFunc:RQ},_Q="return float(a >= b);",FQ=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,$Q=vn({opSnippet:_Q,packedOpSnippet:FQ,dtype:"bool",cpuKernelImpl:LX}),OQ={kernelName:so,backendName:"webgl",kernelFunc:$Q};function PQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return K4(s,!0,n)}var MQ={kernelName:Lp,backendName:"webgl",kernelFunc:PQ},zQ="return float(!isnan(x) && !isinf(x));",LQ=tt({opSnippet:zQ,dtype:"bool"}),BQ={kernelName:il,backendName:"webgl",kernelFunc:LQ},WQ="return float(isinf(x));",VQ=tt({opSnippet:WQ,dtype:"bool"}),UQ={kernelName:ll,backendName:"webgl",kernelFunc:VQ},HQ="return float(isnan(x));",GQ=tt({opSnippet:HQ,dtype:"bool"}),jQ={kernelName:ul,backendName:"webgl",kernelFunc:GQ},qQ="return float(a < b);",XQ=`
|
|
return vec4(lessThan(a, b));
|
|
`,KQ=vn({opSnippet:qQ,packedOpSnippet:XQ,cpuKernelImpl:BX,dtype:"bool"}),ZQ={kernelName:cl,backendName:"webgl",kernelFunc:KQ},YQ="return float(a <= b);",JQ=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,QQ=vn({opSnippet:YQ,packedOpSnippet:JQ,cpuKernelImpl:WX,dtype:"bool"}),eee={kernelName:dl,backendName:"webgl",kernelFunc:QQ};function tee(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=VX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var nee={kernelName:Wp,backendName:"webgl",kernelFunc:tee},see=`if (x < 0.0) return NAN;
|
|
return log(x);`,ree=`
|
|
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;
|
|
`,aee=tt({opSnippet:see,packedOpSnippet:ree,cpuKernelImpl:UX}),oee={kernelName:oo,backendName:"webgl",kernelFunc:aee},iee="return log(1.0 + x);",lee=tt({opSnippet:iee}),uee={kernelName:pl,backendName:"webgl",kernelFunc:lee},cee="return float(a >= 1.0 && b >= 1.0);",dee=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,pee=vn({opSnippet:cee,packedOpSnippet:dee,dtype:"bool"}),hee={kernelName:hl,backendName:"webgl",kernelFunc:pee},fee="return float(!(x >= 1.0));",mee=tt({opSnippet:fee}),gee={kernelName:dc,backendName:"webgl",kernelFunc:mee},Aee="return float(a >= 1.0 || b >= 1.0);",yee=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,xee=vn({opSnippet:Aee,packedOpSnippet:yee,dtype:"bool"}),bee={kernelName:pc,backendName:"webgl",kernelFunc:xee},vee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${a}; j <= ${a}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${o}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},wee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${a};
|
|
vec2 cache = vec2(0.);
|
|
if(firstChannel >= 0){
|
|
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
|
|
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
|
|
if(hasNextRow){
|
|
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
|
|
}
|
|
}
|
|
|
|
ivec2 depth = ivec2(d, d + 1);
|
|
for (int j = - ${a}; j <= ${a}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
|
|
|
|
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
|
|
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
|
|
|
|
if(depthInRange || depthPlusOneInRange){
|
|
vec4 z = vec4(0.);
|
|
vec4 xFragAtCurrentDepth;
|
|
z.xz = cache.xy;
|
|
if(depthPlusOneInRange && hasNextCol){
|
|
xFragAtCurrentDepth = idx.y != d ?
|
|
getX(b, r, c, idx.y) : xFragAtOutputCoords;
|
|
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
|
|
if(hasNextRow){
|
|
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
|
|
}
|
|
}
|
|
cache.xy = z.yw;
|
|
sum += z * z;
|
|
}
|
|
}
|
|
vec4 result = xAtOutputCoords * ${i};
|
|
setOutput(result);
|
|
}
|
|
`}},kee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new wee(r.shape,a,o,i,l):new vee(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},Iee={kernelName:hc,backendName:"webgl",kernelFunc:kee},See=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${t})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${t} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${s}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${s})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},Cee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new See(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Tee={kernelName:Vp,backendName:"webgl",kernelFunc:Cee};function Nee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Ai(i,e.dtype,"max",s),u=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function J4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=c!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[c[S]];let v=T2(x,r.shape,r.dtype,c,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=v}else h=l0(r,c,n);u=_.getInnerMostAxes(u.length,i)}_.assertAxesAreInnerMostDims("max",u,i);let[f,m]=_.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=_.expandShapeToKeepDim(f,l));let A;if(p){let x=n.texData.get(h.dataId).values,b=HX(x,w.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(A.dataId);v.values=b}else A=Nee(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var Eee={kernelName:io,backendName:"webgl",kernelFunc:J4},Ree=g4+`
|
|
return max(a, b);
|
|
`,Dee=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+o0+`
|
|
return result;
|
|
`,_ee=vn({opSnippet:Ree,packedOpSnippet:Dee,cpuKernelImpl:GX}),Fee={kernelName:lo,backendName:"webgl",kernelFunc:_ee};function $ee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;bu(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return ds({inputs:{x:r},backend:n});let d=new Td(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Oee={kernelName:uo,backendName:"webgl",kernelFunc:$ee};function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=_.computePool3DInfo(r.shape,a,o,c,i,u,l),p=new E2(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Mee={kernelName:fc,backendName:"webgl",kernelFunc:Pee},zee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${r};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${t}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Lee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${c}, ${d}, ${p});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${i};
|
|
wD += ${r}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${u};
|
|
wC += ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${u} +
|
|
wR * ${u} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Bee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,l,d,u,c),h=new E2(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Lee(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Wee={kernelName:Hp,backendName:"webgl",kernelFunc:Bee};function Vee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;bu([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=_.computePool2DInfo(i.shape,l,u,1,c,d),h=!0,f=new Td(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new zee(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var Uee={kernelName:Up,backendName:"webgl",kernelFunc:Vee};function Hee(e,t,n,s){let r=new Td(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Td(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Gee={kernelName:Gp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];w.assert(_.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=_.computePool2DInfo(s.shape,r,a,u,o),[d,p]=Hee(s,i,c,l);return[d,p]}};function jee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Ai(i,"float32","mean",s),u=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var qee={kernelName:co,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=_.getAxesPermutation(u,i),d=c!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,v=new Array(i);for(let C=0;C<v.length;C++)v[C]=s.shape[c[C]];let k=T2(b,s.shape,s.dtype,c,v);f=o.makeTensorInfo(v,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=l0(s,c,o);h.push(f),u=_.getInnerMostAxes(u.length,i)}_.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=_.computeOutAndReduceShapes(f.shape,u),A=m;r&&(A=_.expandShapeToKeepDim(m,l));let y=jee(f,g,A,o);for(let x of h)o.disposeIntermediateTensorInfo(x);return y}};function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=r;c!=null&&(d=On({inputs:{x:r},backend:n,attrs:{perm:c}}),u=_.getInnerMostAxes(u.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Ai(m,m.dtype,"min",n),A;if(o){let y=_.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:y}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(d),A}var Kee={kernelName:po,backendName:"webgl",kernelFunc:Xee},Zee=g4+`
|
|
return min(a, b);
|
|
`,Yee=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+o0+`
|
|
return result;
|
|
`,Jee=vn({opSnippet:Zee,packedOpSnippet:Yee,cpuKernelImpl:jX}),Qee={kernelName:ho,backendName:"webgl",kernelFunc:Jee},ete=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=yt(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${s}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}},tte=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=yt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=$n("rc",s),l=$n("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${d};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${d};
|
|
}
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${d}) +
|
|
gte * ((end - 1) * 2 - source + ${d});
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${c});
|
|
${i[s-1]} += 1;
|
|
if(${u}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},nte=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tte(s.shape,r,a):new ete(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},ste={kernelName:fo,backendName:"webgl",kernelFunc:nte},rte=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,ate=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+o0+`
|
|
return result;
|
|
`,ote=vn({opSnippet:rte,packedOpSnippet:ate}),ite={kernelName:fl,backendName:"webgl",kernelFunc:ote},lte=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}},ute=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,cte=`
|
|
// 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;
|
|
`,Q4=vn({opSnippet:ute,packedOpSnippet:cte,checkOutOfBounds:!0}),dte={kernelName:Ya,backendName:"webgl",kernelFunc:Q4},ek="return a - b;",tk=vn({opSnippet:ek,packedOpSnippet:ek,supportsComplex:!0,cpuKernelImpl:lK}),pte={kernelName:_o,backendName:"webgl",kernelFunc:tk};function nk(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=J4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=_.expandShapeToKeepDim(i.shape,o),u=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),c=tk({inputs:{a:r,b:u},backend:n}),d=j4({inputs:{x:c},backend:n}),p=u0({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=Q4({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var hte={kernelName:Ro,backendName:"webgl",kernelFunc:nk};function fte(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:nk({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new lte(u,c,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var mte={kernelName:jp,backendName:"webgl",kernelFunc:fte},sk="return -x;";function gte(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=XX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Su(s.shape,sk):r=new Ia(s.shape,sk),n.runWebGLProgram(r,[s],s.dtype)}var Ate={kernelName:ml,backendName:"webgl",kernelFunc:gte},yte=cr.nonMaxSuppressionV3Impl;function xte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=yte(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var bte={kernelName:Al,backendName:"webgl",kernelFunc:xte},vte=cr.nonMaxSuppressionV4Impl;function wte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=vte(c,d,o,i,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var kte={kernelName:yl,backendName:"webgl",kernelFunc:wte},Ite=cr.nonMaxSuppressionV5Impl;function Ste(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:A}=Ite(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var Cte={kernelName:xl,backendName:"webgl",kernelFunc:Ste},Tte=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${s}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},Nte=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=w.sizeFromShape(r.shape),u=new Tte(l,a,o,i),c=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Ete={kernelName:go,backendName:"webgl",kernelFunc:Nte};function f0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Nd({inputs:{input:s},backend:n}),a=f0({inputs:{x:r},backend:n}),o=h0({inputs:{input:s},backend:n}),i=f0({inputs:{x:o},backend:n}),l=Sa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Ed({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Rte={kernelName:zl,backendName:"webgl",kernelFunc:f0};function rk(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Nd({inputs:{input:s},backend:n}),a=rk({inputs:{x:r},backend:n}),o=h0({inputs:{input:s},backend:n}),i=f0({inputs:{x:o},backend:n}),l=Sa({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Ed({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Dte={kernelName:bl,backendName:"webgl",kernelFunc:rk};function _te(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return _2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=_2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=P4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Fte={kernelName:vl,backendName:"webgl",kernelFunc:_te},$te=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=yt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
}
|
|
`}},Ote=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=yt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=$n("rc",s),l=$n("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
|
|
if(${u}) {
|
|
`,s===1?"":`}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
|
|
if(${u}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
|
|
${d[f]}
|
|
if (${p}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`;h+=s===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},ak=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let u=a.map((c,d)=>c[0]+r.shape[d]+c[1]);return Ed({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ote(r.shape,a,o):new $te(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Pte={kernelName:Ao,backendName:"webgl",kernelFunc:ak},Mte=`
|
|
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);
|
|
`,zte=`
|
|
// 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));
|
|
`+o0+`
|
|
return result;
|
|
`,Lte=vn({opSnippet:Mte,packedOpSnippet:zte}),Bte={kernelName:yo,backendName:"webgl",kernelFunc:Lte};function Wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=w.parseAxisParam(a,r.shape),c=u,d=_.getAxesPermutation(c,i),p=r;d!=null&&(p=On({inputs:{x:r},backend:n,attrs:{perm:d}}),c=_.getInnerMostAxes(c.length,i),l.push(p)),_.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=ZX(p.shape,p.dtype,f,c);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=_.computeOutAndReduceShapes(p.shape,c),g=w.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=dh(r.dtype),x=Ai(A,y,"prod",n);h=ve({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(o){l.push(h);let f=_.expandShapeToKeepDim(h.shape,u);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Vte={kernelName:wl,backendName:"webgl",kernelFunc:Wte},ok=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=YX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Ute={kernelName:mc,backendName:"webgl",kernelFunc:ok},Hte="return 1.0 / x;",Gte=tt({opSnippet:Hte}),jte={kernelName:kl,backendName:"webgl",kernelFunc:Gte},qte=Js+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Xte=`
|
|
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;
|
|
`,Kte=tt({opSnippet:qte,packedOpSnippet:Xte}),Zte={kernelName:bo,backendName:"webgl",kernelFunc:Kte},Yte=Js+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Jte=`
|
|
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;
|
|
`,Qte=tt({opSnippet:Yte,packedOpSnippet:Jte}),ene={kernelName:wo,backendName:"webgl",kernelFunc:Qte},tne=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${d};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},nne=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${d};
|
|
|
|
// Compute the four integer indices.
|
|
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
|
|
ivec3 sourceCeilRC = ivec3(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function sne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new nne(r.shape,l,u,a,o):new tne(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var rne={kernelName:vo,backendName:"webgl",kernelFunc:sne},ane=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function one(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new ane(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var ine={kernelName:Kp,backendName:"webgl",kernelFunc:one},lne=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},une=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function cne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new une(r.shape,l,u,a,o):new lne(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var dne={kernelName:gc,backendName:"webgl",kernelFunc:cne},pne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${i[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${i[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${s}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function hne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new pne(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var fne={kernelName:Xp,backendName:"webgl",kernelFunc:hne},mne=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=yt(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},gne=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=$n("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=yt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${i(s.slice())};
|
|
if(${r}){
|
|
result.g = ${l(s.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${u(s.slice())};
|
|
if(${r}) {
|
|
result.a = ${c(s.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Ane(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return ds({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gne(r.shape,i):new mne(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var yne={kernelName:ko,backendName:"webgl",kernelFunc:Ane},xne=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${t.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},bne={kernelName:Ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new xne(s.shape,a),[u,c]=_.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},vne=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,wne=tt({opSnippet:vne}),kne={kernelName:Io,backendName:"webgl",kernelFunc:wne},Ine="return inversesqrt(x);",Sne=tt({opSnippet:Ine,cpuKernelImpl:JX}),Cne={kernelName:So,backendName:"webgl",kernelFunc:Sne},ik=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=yt(r.length),l=yt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${i} strides = ${i}(${r});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${c});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${p};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function Tne(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=_.calculateShapes(a,r,o),p=[d/u,u];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new ik(l,i,h.shape.length,f.shape.length,c,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),y=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),y}var Nne={kernelName:Sl,backendName:"webgl",kernelFunc:Tne},Ene=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=yt(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function Rne(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Ene(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Rs(r.dtype,a.dtype))}var Dne={kernelName:Cl,backendName:"webgl",kernelFunc:Rne},_ne=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${_.SELU_SCALEALPHA};
|
|
float scale = ${_.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Fne=tt({opSnippet:_ne}),$ne={kernelName:Tl,backendName:"webgl",kernelFunc:Fne},lk="return 1.0 / (1.0 + exp(-1.0 * x));",One=tt({opSnippet:lk,packedOpSnippet:lk,cpuKernelImpl:QX}),Pne={kernelName:To,backendName:"webgl",kernelFunc:One},Mne=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,zne=tt({opSnippet:Mne}),Lne={kernelName:Rl,backendName:"webgl",kernelFunc:zne},Bne=v4+`
|
|
return sin(x);
|
|
`,Wne=tt({opSnippet:Bne}),Vne={kernelName:Co,backendName:"webgl",kernelFunc:Wne},Une=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Hne=tt({opSnippet:Une}),Gne={kernelName:El,backendName:"webgl",kernelFunc:Hne},jne=`
|
|
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;
|
|
`,qne=tt({opSnippet:jne}),Xne={kernelName:Dl,backendName:"webgl",kernelFunc:qne},Kne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let u=[],c=ak({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=_.getReshaped(c.shape,a,i,!1),p=_.getPermuted(d.length,a.length,!1),h=_.getReshapedPermuted(c.shape,a,i,!1),f=ve({inputs:{x:c},backend:n,attrs:{shape:d}}),m=On({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},Zne={kernelName:_l,backendName:"webgl",kernelFunc:Kne};function Yne(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,p,h,f,m]=tK(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Jne={kernelName:Zp,backendName:"webgl",kernelFunc:Yne};function Qne(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=nK(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var ese={kernelName:Yp,backendName:"webgl",kernelFunc:Qne};function tse(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=l4(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var nse={kernelName:Jp,backendName:"webgl",kernelFunc:tse};function sse(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=l4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var rse={kernelName:Qp,backendName:"webgl",kernelFunc:sse};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=_.calculateShapes(a,r,i),p=!1,h=new ik(u,l,r.shape.length,a.shape.length,c,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var ose={kernelName:eh,backendName:"webgl",kernelFunc:ase};function ise(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=_.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=Nu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,f})}var lse={kernelName:Fl,backendName:"webgl",kernelFunc:ise},uk="return sqrt(x);",use=tt({opSnippet:uk,packedOpSnippet:uk,cpuKernelImpl:sK}),cse={kernelName:No,backendName:"webgl",kernelFunc:use},dse="return x * x;",pse=tt({opSnippet:dse}),hse={kernelName:Ac,backendName:"webgl",kernelFunc:pse},ck="return (a - b) * (a - b);",fse=vn({opSnippet:ck,packedOpSnippet:ck}),mse={kernelName:Do,backendName:"webgl",kernelFunc:fse};function gse({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=Js+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new Ia(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Ase={kernelName:sa,backendName:"webgl",kernelFunc:gse},yse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=yt(n.length),a=yt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}};function xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:A,outShape:y}=En.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=ve({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=Nu({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=ve({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,D=je(x.shape,x.dtype,C),O=rK(y,D,m,f);b=n.makeTensorInfo(y,x.dtype,O.values)}else{let S=new yse(f,m,y);b=n.runWebGLProgram(S,[x],x.dtype)}let v=ve({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var bse={kernelName:$l,backendName:"webgl",kernelFunc:xse};function vse(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[f,m]=aK(p,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var wse={kernelName:th,backendName:"webgl",kernelFunc:vse};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,d]=oK(i,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Ise={kernelName:nh,backendName:"webgl",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=iK(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Cse={kernelName:sh,backendName:"webgl",kernelFunc:Sse},Tse="return tan(x);",Nse=tt({opSnippet:Tse}),Ese={kernelName:Fo,backendName:"webgl",kernelFunc:Nse},Rse=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Dse=tt({opSnippet:Rse}),_se={kernelName:$o,backendName:"webgl",kernelFunc:Dse},Fse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=yt(this.rank),r=$se(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function $se(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function dk(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(p=>w.decodeString(p)):l,c=je(r.shape,r.dtype,u),d=uK(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Fse(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Ose={kernelName:na,backendName:"webgl",kernelFunc:dk},Pse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced above,
|
|
// Figure5(a) shows that element[1] is in the
|
|
// second half of the group when group size is 2, but it is in the
|
|
// first half of the group when group size is 4.
|
|
|
|
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
|
|
int i = isFirstInPair ? elemIdx : elemIdx - inc;
|
|
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
|
|
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
|
|
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
|
|
|
|
// Denotes which direction indices are in (ascending or descending).
|
|
bool reverse = imod(elemIdx, 2 * dir) >= dir;
|
|
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) { // Elements in opposite order of direction
|
|
int iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutput(float(i0));
|
|
} else {
|
|
setOutput(float(i1));
|
|
}
|
|
}
|
|
`}},Mse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
|
|
// we only need to output the indices at positions |, the indices at
|
|
// positions _ can be thrown away, see Figure5(b) After Phase 2
|
|
// (Merge phase) in the Bitonic Top K paper referenced above.
|
|
// For example, the paper shows we only need to output the orange bars.
|
|
// The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back
|
|
// to the previous sequence to find the corresponding value,
|
|
// we need to double the index. When we double the index,
|
|
// we basically interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
|
|
// of each 2k positions by - elemIdx % k. E.g. for output at
|
|
// index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
|
|
|
|
float x0 = getX(batch, i0);
|
|
float x1 = i1 < n ? getX(batch, i1) : x0;
|
|
|
|
setOutput(x0 >= x1 ? float(i0) : float(i1));
|
|
}
|
|
`}};function yi(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function pk(e){let t=1;for(;t<e;)t*=2;return t}function zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let O=n.readSync(r.dataId),[E,R]=cK(O,u,r.dtype,a,o);return[n.makeTensorInfo(E.shape,E.dtype,E.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,Ed({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=w.sizeFromShape(u)/c,g=ve({inputs:{x:h},attrs:{shape:[m,c]},backend:n});p&&yi(n,h);let A=pk(a),y=pk(c),x=null,b=()=>x===null?[g,g]:[g,x],v=(O,E,R)=>{let T=b(),P=new Pse(R),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[O],[E]],q=x;x=n.runWebGLProgram(P,T,"int32",j),yi(n,q)};for(let O=1;O<A;O*=2){let E=O*2;for(let R=O;R>=1;R/=2)v(E,R,[m,y])}for(let O=y;O>A;O/=2){let E=b(),R=new Mse([m,O/2]),P=[[c],[x===null?1:0],[A]],U=x;x=n.runWebGLProgram(R,E,"int32",P),yi(n,U);let j=A/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=Nu({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),yi(n,k);let S=Y4({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});yi(n,g);let C=u.slice(0,-1);C.push(a),k=x,x=ve({inputs:{x},attrs:{shape:C},backend:n}),yi(n,k);let D=S;return S=ve({inputs:{x:S},attrs:{shape:C},backend:n}),yi(n,D),[S,x]}var Lse={kernelName:Ol,backendName:"webgl",kernelFunc:zse},Bse=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${i} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${i} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${i} == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
} else {
|
|
return outCoord;
|
|
}
|
|
}
|
|
|
|
float readWithFillValue(int batch, int coordY, int coordX,
|
|
int channel) {
|
|
float outputValue;
|
|
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
float outputValue;
|
|
int batch = coords[0];
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
int channel = coords[3];
|
|
float xf = float(x);
|
|
float yf = float(y);
|
|
float a1 = getTransforms(batch, 0);
|
|
float a2 = getTransforms(batch, 1);
|
|
float a3 = getTransforms(batch, 2);
|
|
float b1 = getTransforms(batch, 3);
|
|
float b2 = getTransforms(batch, 4);
|
|
float b3 = getTransforms(batch, 5);
|
|
float c1 = getTransforms(batch, 6);
|
|
float c2 = getTransforms(batch, 7);
|
|
float projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = float(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${o} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function Wse(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,p,h]=r.shape,[f,m]=u!=null?u:[d,p],g=[c,f,m,h],A=new Bse(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var Vse={kernelName:Pl,backendName:"webgl",kernelFunc:Wse};function Use(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;bu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=dK(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var Hse={kernelName:rh,backendName:"webgl",kernelFunc:Use};function Gse(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=Nu({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ve({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var jse={kernelName:Ml,backendName:"webgl",kernelFunc:Gse},qse=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,d=`
|
|
sumValue += dot(values, segFilter);
|
|
`,p="";r%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${p}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${a})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${a})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${c===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${d}
|
|
} else if (${c===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
0
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function Xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=_.getAxesPermutation([u],i),d=r;c!=null&&(d=On({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(d),u=_.getInnerMostAxes(1,i)[0]);let p=_.segment_util.computeOutShape(d.shape,u,o),h=w.sizeFromShape([d.shape[u]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=dh(r.dtype),g=(b,v,k,S,C)=>{let D=b.shape[0],O=b.shape[1],E=_.segment_util.segOpComputeOptimalWindowSize(O,C),R={windowSize:E,inSize:O,batchSize:D,numSegments:C},T=new qse(R,v),P=n.compileAndRun(T,[b,k],S);if(l.push(P),P.shape[1]===C)return P;let U=ok({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),j=dk({inputs:{x:U},backend:n,attrs:{reps:[O/E]}});return l.push(U),l.push(j),g(P,v,j,S,C)},A=g(f,"unsortedSegmentSum",a,m,o),y=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),x=y;if(c!=null){l.push(y);let b=_.getUndoAxesPermutation(c);x=On({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Kse={kernelName:yc,backendName:"webgl",kernelFunc:Xse},Zse=[Iee,Tee,cZ,pZ,mZ,yZ,bZ,kZ,SZ,TZ,DZ,FZ,PZ,LZ,jZ,VZ,KZ,QZ,YZ,sY,aY,iY,dY,yY,bY,CY,NY,_Y,OY,GK,BY,ZY,JY,HY,nJ,rJ,eJ,iJ,cJ,hJ,mJ,AJ,bJ,CJ,NJ,wJ,DJ,$J,PJ,BJ,HJ,XJ,YJ,JJ,QJ,tQ,sQ,aQ,iQ,uQ,hQ,gQ,xQ,vQ,IQ,TQ,DQ,OQ,HK,MQ,zY,BQ,UQ,jQ,qK,ZQ,eee,nee,uee,oee,hee,gee,bee,Eee,Mee,Oee,Wee,Uee,Gee,Fee,qee,Kee,Qee,ste,ite,mte,JK,Ate,bte,kte,Cte,wY,Ete,Dte,Fte,Pte,Bte,KK,Vte,Ute,kY,dte,jte,ene,Zte,eZ,rne,ine,dne,fne,yne,bne,kne,Cne,Nne,Dne,$ne,Pne,Lne,Vne,Gne,gY,hte,Xne,Zne,Jne,ese,nse,rse,ose,lse,cse,hse,mse,Ase,bse,wse,Ise,Cse,pte,iZ,Ese,_se,Ose,Lse,Vse,lZ,Hse,jse,Kse,Rte];for(let e of Zse)ra(e);var Kn;(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"})(Kn||(Kn={}));var Rd;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Rd||(Rd={}));var hk;function Yse(e){hk=e.wasm.cwrap(Po,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Jse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let C=n.dataIdMap.get(o.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);f=C.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=Rd[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],y=u?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,A,y],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return hk(p,k,r.shape.length,h,S,a.shape.length,l,u,g,f,m,d||0,v),b}var Qse={kernelName:Po,backendName:"wasm",setupFunc:Yse,kernelFunc:Jse};function wn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var ere=wn(Li);function Pn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,p=i.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,f=_.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),A=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,u.shape.length,p,A,c.shape.length,Kn[u.dtype],y);if(t&&u.dtype==="float32")return x(),m;let b=_.getBroadcastDims(u.shape,f),v=_.getBroadcastDims(c.shape,f),k=b.every((C,D)=>C===D),S=v.every((C,D)=>C===D);if(k&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var tre=!0,nre=Pn(ea,tre),fk;function sre(e){fk=e.wasm.cwrap(La,null,["array","number","number","number"])}function rre(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return fk(a,r.length,Kn[s.dtype],o),s}var are={kernelName:La,backendName:"wasm",setupFunc:sre,kernelFunc:rre};function m0(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var ore={kernelName:ro,backendName:"wasm",kernelFunc:m0},mk;function ire(e){mk=e.wasm.cwrap(Oo,null,["number","array","number","number","number","array","number"])}function Du(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=ure(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=lre(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=m0({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,l.dtype),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return mk(c,h,l.shape.length,Kn[l.dtype],d,p,a.length),u}function lre(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function ure(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var cre={kernelName:Oo,backendName:"wasm",kernelFunc:Du,setupFunc:ire};function Ca(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=_.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=_.getInnerMostAxes(o.length,r),l=Du({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(u=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:u}}var gk;function dre(e){gk=e.wasm.cwrap(Vi,null,["number, number, number"])}function pre(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ca(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;_.assertAxesAreInnerMostDims("all",d,f);let[m,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;gk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var hre={kernelName:Vi,backendName:"wasm",setupFunc:dre,kernelFunc:pre},Ak;function fre(e){Ak=e.wasm.cwrap(Ui,null,["number, number, number"])}function mre(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ca(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let f=u.shape.length;_.assertAxesAreInnerMostDims("any",d,f);let[m,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;Ak(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var gre={kernelName:Ui,backendName:"wasm",setupFunc:fre,kernelFunc:mre},yk;function Are(e){yk=e.wasm.cwrap(Ba,null,["number","number","number","number","number"])}function yre(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:u,axes:c,inputWasTransposed:d}=Ca(a,r,t);if(d){let A=t.dataIdMap.get(u.dataId).id;A!==o&&(l=u,i=A)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=l.shape[c[0]];return yk(i,Kn[l.dtype],m,g,f),d&&t.disposeData(u.dataId),h}var xre={kernelName:Ba,backendName:"wasm",kernelFunc:yre,setupFunc:Are},xk;function bre(e){xk=e.wasm.cwrap(Wa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vre(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=_.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,A=c.strideHeight,y=c.strideWidth,x=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=s.makeOutput(c.outShape,"float32"),v=s.dataIdMap.get(b.dataId).id;return xk(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,y,x,v),b}var wre={kernelName:Wa,backendName:"wasm",setupFunc:bre,kernelFunc:vre};function Zn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var kre={kernelName:Il,backendName:"wasm",kernelFunc:Zn},bk;function Ire(e){bk=e.wasm.cwrap(Va,null,["number","array","number","number","array","number","number","number","number"])}function Sre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),A=w.sizeFromShape(m),y=g===A||g===1||A===1;w.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Dd(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=En.parseSliceParams(t,n,s),i=En.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(i){let f=En.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Gf(l,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let p=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Tre(l,c[0],p,a,o);else if(h===3)Nre(l,c[0],c[1],p,a,o);else if(h===4)Ere(l,c[0],c[1],c[2],p,a,o);else{let f=Gf(l,a,o,t.shape,t.dtype);p.set(f)}return u}function Tre(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Nre(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],u=r[2],c=i+a[0],d=l+a[1];for(let p=i;p<c;p++)for(let h=l;h<d;h++){let f=p*t+h*n+u;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Ere(e,t,n,s,r,a,o){let i=0,l=a[0],u=a[1],c=a[2],d=l+o[0],p=u+o[1],h=c+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=u;g<p;g++)for(let A=c;A<h;A++){let y=m*t+g*n+A*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Rre={kernelName:Nl,backendName:"wasm",kernelFunc:Dd};function Dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,y)=>A*y),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=Zn({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Du({inputs:{x:h},backend:n,attrs:{perm:u}}),m=Zn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=Dd({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var _re={kernelName:Ki,backendName:"wasm",kernelFunc:Dre};function g0(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Fre={kernelName:Ua,backendName:"wasm",kernelFunc:g0},$re=wn(Ha),vk;function Ore(e){vk=e.wasm.cwrap(ta,null,["number","number","number","number"])}function Pre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return vk(i,a,o,u),l}var Mre={kernelName:ta,backendName:"wasm",setupFunc:Ore,kernelFunc:Pre};function wk(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return m0({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(_.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=w.sizeFromShape(x.shape.slice(s));return Zn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=_.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=t2(f,r,t[0].dtype,m),A=_.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=_.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return u+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*u;for(let m=0;m<d.length;m++){let g=c[m],A=h*g,y=d[m].subarray(A,A+g);p.set(y,f),f+=g}}return o}var zre={kernelName:Zi,backendName:"wasm",kernelFunc:wk},kk;function Lre(e){kk=e.wasm.cwrap(Ga,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d,dataFormat:p}=n,h=_.convertConv2DDataFormat(p),f=_.computeConv2DInfo(r.shape,a.shape,l,u,c,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,y=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,C=f.strideWidth,D=f.inChannels,O=f.outChannels,E=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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a[v]=a[v]||A(i,l),a[v]},x=function(v=null){var D,O;let k=null,S=null,C=!1;t===0?k=n:k=(D=y(r))==null?void 0:D.texture,t++,s&&!(v&f.INTERMEDIATE)?(S=null,C=t%2==0):(r=(r+1)%2,S=(O=y(r))==null?void 0:O.fbo),m.bindTexture(m.TEXTURE_2D,k),m.bindFramebuffer(m.FRAMEBUFFER,S),m.uniform1f(c.uniform.flipY,C?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(v){if(g(v.width,v.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,v),o.length===0)return x(),p;for(let k=0;k<o.length;k++){s=k===o.length-1;let S=o[k];S.func.apply(this,S.args||[])}return p};let b=function(v){if(h[v])return c=h[v],m.useProgram(c.id),c;let k={};k.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
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|
`),k.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
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|
`),c=new kle(m,k.VERTEX_IDENTITY,v);let S=Float32Array.BYTES_PER_ELEMENT,C=4*S;return m.enableVertexAttribArray(c.attribute.pos),m.vertexAttribPointer(c.attribute.pos,2,m.FLOAT,!1,C,0*S),m.enableVertexAttribArray(c.attribute.uv),m.vertexAttribPointer(c.attribute.uv,2,m.FLOAT,!1,C,2*S),h[v]=c,c};d.colorMatrix=function(v){let k=new Float32Array(v);k[4]/=255,k[9]/=255,k[14]/=255,k[19]/=255;let S=k[18]===1&&k[3]===0&&k[8]===0&&k[13]===0&&k[15]===0&&k[16]===0&&k[17]===0&&k[19]===0?d.colorMatrix.SHADER.WITHOUT_ALPHA:d.colorMatrix.SHADER.WITH_ALPHA,C=b(S);m.uniform1fv(C.uniform.m,k),x()},d.colorMatrix.SHADER={},d.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
|
|
`),d.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
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|
`),d.brightness=function(v){let k=(v||0)+1;d.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},d.saturation=function(v){let k=(v||0)*2/3+1,S=(k-1)*-.5;d.colorMatrix([k,S,S,0,0,S,k,S,0,0,S,S,k,0,0,0,0,0,1,0])},d.desaturate=function(){d.saturation(-1)},d.contrast=function(v){let k=(v||0)+1,S=-128*(k-1);d.colorMatrix([k,0,0,0,S,0,k,0,0,S,0,0,k,0,S,0,0,0,1,0])},d.negative=function(){d.contrast(-2)},d.hue=function(v){v=(v||0)/180*Math.PI;let k=Math.cos(v),S=Math.sin(v),C=.213,D=.715,O=.072;d.colorMatrix([C+k*(1-C)+S*-C,D+k*-D+S*-D,O+k*-O+S*(1-O),0,0,C+k*-C+S*.143,D+k*(1-D)+S*.14,O+k*-O+S*-.283,0,0,C+k*-C+S*-(1-C),D+k*-D+S*D,O+k*(1-O)+S*O,0,0,0,0,0,1,0])},d.desaturateLuminance=function(){d.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},d.sepia=function(){d.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},d.brownie=function(){d.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},d.vintagePinhole=function(){d.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},d.kodachrome=function(){d.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},d.technicolor=function(){d.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},d.polaroid=function(){d.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},d.shiftToBGR=function(){d.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},d.convolution=function(v){let k=new Float32Array(v),S=1/i,C=1/l,D=b(d.convolution.SHADER);m.uniform1fv(D.uniform.m,k),m.uniform2f(D.uniform.px,S,C),x()},d.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
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|
`),d.detectEdges=function(){d.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},d.sobelX=function(){d.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},d.sobelY=function(){d.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},d.sharpen=function(v){let k=v||1;d.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},d.emboss=function(v){let k=v||1;d.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},d.blur=function(v){let k=v/7/i,S=v/7/l,C=b(d.blur.SHADER);m.uniform2f(C.uniform.px,0,S),x(f.INTERMEDIATE),m.uniform2f(C.uniform.px,k,0),x()},d.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
|
|
`),d.pixelate=function(v){let k=v/i,S=v/l,C=b(d.pixelate.SHADER);m.uniform2f(C.uniform.size,k,S),x()},d.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
|
|
`)}var v0=2048,Oe,Et,Xt;function ps(e,t){let n;return ie.browser?ie.offscreen?n=new OffscreenCanvas(e,t):(n=document.createElement("canvas"),n.width=e,n.height=t):typeof ie.Canvas!="undefined"?n=new ie.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t)),n}function _u(e,t){let n;if(!e)return t.debug&&re("input is missing"),{tensor:null,canvas:null};if(!(e instanceof Ge)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ie.Canvas!="undefined"&&e instanceof ie.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input type is not recognized");if(e instanceof Ge){if(e.isDisposedInternal)throw new Error("input tensor is disposed");if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Bs(e);else throw new Error(`input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`)}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&re("input stream is not ready"),{tensor:null,canvas:Oe};let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!r)return t.debug&&re("cannot determine input dimensions"),{tensor:null,canvas:Oe};let a=s,o=r;if(a>v0&&(a=v0,o=Math.trunc(a*r/s)),o>v0&&(o=v0,a=Math.trunc(o*s/r)),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("input cannot determine dimension");(!Oe||(Oe==null?void 0:Oe.width)!==a||(Oe==null?void 0:Oe.height)!==o)&&(Oe=ps(a,o));let i=Oe.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),t.filter.enabled&&ie.webgl.supported){if((!Xt||!Et||Oe.width!==Et.width||(Oe==null?void 0:Oe.height)!==(Et==null?void 0:Et.height))&&(Et=ps(Oe==null?void 0:Oe.width,Oe==null?void 0:Oe.height),(Et==null?void 0:Et.width)!==(Oe==null?void 0:Oe.width)&&(Et.width=Oe==null?void 0:Oe.width),(Et==null?void 0:Et.height)!==(Oe==null?void 0:Oe.height)&&(Et.height=Oe==null?void 0:Oe.height),Xt=ie.browser?new I8({canvas:Et}):null),!Xt)return{tensor:null,canvas:Oe};Xt.reset(),Xt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Xt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Xt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Xt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Xt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Xt.addFilter("hue",t.filter.hue),t.filter.negative&&Xt.addFilter("negative"),t.filter.sepia&&Xt.addFilter("sepia"),t.filter.vintage&&Xt.addFilter("brownie"),t.filter.sepia&&Xt.addFilter("sepia"),t.filter.kodachrome&&Xt.addFilter("kodachrome"),t.filter.technicolor&&Xt.addFilter("technicolor"),t.filter.polaroid&&Xt.addFilter("polaroid"),t.filter.pixelate!==0&&Xt.addFilter("pixelate",t.filter.pixelate),Xt.apply(Oe)}else Et=Oe,Xt&&(Xt=null);if(!n){let l;if(Et.data){let u=[Et.height,Et.width,3];l=fh(Et.data,u,"float32")}else if(typeof ImageData!="undefined"&&Et instanceof ImageData)l=Ds?Ds.fromPixels(Et):null;else if(t.backend==="webgl"||t.backend==="humangl"){let u=ps(a,o);u.width=a,u.height=o;let c=u.getContext("2d");c==null||c.drawImage(Et,0,0);try{l=Ds&&ie.browser?Ds.fromPixels(u):null}catch(d){throw new Error("browser webgl error")}}else{let u=ps(a,o);if(!u)return{tensor:null,canvas:Oe};u.width=a,u.height=o;let c=u.getContext("2d");if(!c)return{tensor:null,canvas:Oe};c.drawImage(Et,0,0);let d=c.getImageData(0,0,a,o);Ds&&ie.browser?l=Ds.fromPixels(d):l=H(()=>{let p=un(Array.from(d.data),[a,o,4]),h=Ht(p,4,2),f=xn([h[0],h[1],h[2]],2);return V(f,[p.shape[0],p.shape[1],3])})}if(l){let u=pe(l,"float32");n=Lt(u,0),Z(l),Z(u)}else throw n=Mt([1,a,o,3]),new Error("cannot create tensor from input")}}return{tensor:n,canvas:t.filter.return?Et:null}}var V2=0,S8=1;async function C8(e,t){if(e.cacheSensitivity===0)return!1;let n=32;if(!t.shape[1]||!t.shape[2])return!1;let s=De.resizeBilinear(t,[Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=await s.data();Z(s);let a=0;for(let l=0;l<r.length/3;l++)a+=r[3*l+2];let o=100*(Math.max(a,V2)/Math.min(a,V2)-1);V2=a;let i=o<Math.max(e.cacheSensitivity,S8);return S8=o>10*e.cacheSensitivity?0:o,i}var ie={browser:void 0,node:void 0,worker:void 0,platform:void 0,agent:void 0,initial:!0,backends:[],offscreen:void 0,tfjs:{version:void 0},wasm:{supported:void 0,backend:void 0,simd:void 0,multithread:void 0},webgl:{supported:void 0,backend:void 0,version:void 0,renderer:void 0},webgpu:{supported:void 0,backend:void 0,adapter:void 0},kernels:[],Canvas:void 0,Image:void 0,ImageData:void 0};async function Ile(){var n;ie.backends=Object.keys(es().registryFactory),ie.wasm.supported=typeof WebAssembly!="undefined",ie.wasm.backend=ie.backends.includes("wasm"),ie.wasm.supported&&ie.wasm.backend&&or()==="wasm"&&(ie.wasm.simd=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),ie.wasm.multithread=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let e=ps(100,100),t=e?e.getContext("webgl2"):void 0;if(ie.webgl.supported=typeof t!="undefined",ie.webgl.backend=ie.backends.includes("webgl"),ie.webgl.supported&&ie.webgl.backend&&(or()==="webgl"||or()==="humangl")){let s=Er().gpgpu!=="undefined"?await Er().getGPGPUContext().gl:null;s&&(ie.webgl.version=s.getParameter(s.VERSION),ie.webgl.renderer=s.getParameter(s.RENDERER))}ie.webgpu.supported=ie.browser&&typeof navigator.gpu!="undefined",ie.webgpu.backend=ie.backends.includes("webgpu"),ie.webgpu.supported&&(ie.webgpu.adapter=(n=await navigator.gpu.requestAdapter())==null?void 0:n.name),ie.kernels=Tr(or()).map(s=>s.kernelName.toLowerCase())}async function w0(){if(ie.browser=typeof navigator!="undefined",ie.node=typeof process!="undefined",ie.worker=ie.browser?typeof WorkerGlobalScope!="undefined":void 0,ie.tfjs.version=gh,ie.offscreen=typeof ie.offscreen=="undefined"?typeof OffscreenCanvas!="undefined":ie.offscreen,typeof navigator!="undefined"){let e=navigator.userAgent.match(/\(([^()]+)\)/g);if(e&&e[0]){let t=e[0].match(/\(([^()]+)\)/g);ie.platform=t&&t[0]?t[0].replace(/\(|\)/g,""):"",ie.agent=navigator.userAgent.replace(e[0],""),ie.platform[1]&&(ie.agent=ie.agent.replace(e[1],"")),ie.agent=ie.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(ie.platform=`${process.platform} ${process.arch}`,ie.agent=`NodeJS ${process.version}`);await Ile()}async function T8(e){ie=Vt(ie,e)}var U2=yr.leftEyeLower0,H2=yr.rightEyeLower0,Fu={leftBounds:[U2[0],U2[U2.length-1]],rightBounds:[H2[0],H2[H2.length-1]]},N8={count:468,mouth:13,symmetryLine:[13,yr.midwayBetweenEyes[0]]},Sle={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},$u={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function k0(e,t,n,s){for(let r=0;r<W2.length;r++){let{key:a,indices:o}=W2[r],i=yr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var G2=class{constructor(t,n,s){xe(this,"storedBoxes");xe(this,"boundingBoxDetector");xe(this,"meshDetector");xe(this,"irisModel");xe(this,"boxSize");xe(this,"meshSize");xe(this,"irisSize");xe(this,"irisEnlarge");xe(this,"skipped");xe(this,"detectedFaces");var r,a;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=s,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2]),this.irisSize=(s==null?void 0:s.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,s,r){let a=Od({startPoint:n.startPoint,endPoint:n.endPoint}),o=t.map(d=>[a[0]/this.meshSize*(d[0]-this.meshSize/2),a[1]/this.meshSize*(d[1]-this.meshSize/2),d[2]]),i=s!==0?B2(s,[0,0]):b0,l=s!==0?o.map(d=>[...x8(d,i),d[2]]):o,u=s!==0?y8(r):b0,c=[...Pd({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(d=>[Math.round(d[0]+Ta(c,u[0])),Math.round(d[1]+Ta(c,u[1])),Math.round(d[2])])}getLeftToRightEyeDepthDifference(t){let n=t[Fu.leftBounds[0]][2],s=t[Fu.rightBounds[0]][2];return n-s}getEyeBox(t,n,s,r,a=!1){let o=x0(y0(L2([t[s],t[r]]),this.irisEnlarge)),i=Od(o),l=De.cropAndResize(n,[[o.startPoint[1]/this.meshSize,o.startPoint[0]/this.meshSize,o.endPoint[1]/this.meshSize,o.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);if(a&&ie.kernels.includes("flipleftright")){let u=De.flipLeftRight(l);Z(l),l=u}return{box:o,boxSize:i,crop:l}}getEyeCoords(t,n,s,r=!1){let a=[];for(let o=0;o<$u.numCoordinates;o++){let i=t[o*3],l=t[o*3+1],u=t[o*3+2];a.push([(r?1-i/this.irisSize:i/this.irisSize)*s[0]+n.startPoint[0],l/this.irisSize*s[1]+n.startPoint[1],u])}return{rawCoords:a,iris:a.slice($u.index)}}getAdjustedIrisCoords(t,n,s){let r=t[yr[`${s}EyeUpper0`][$u.upperCenter]][2],a=t[yr[`${s}EyeLower0`][$u.lowerCenter]][2],o=(r+a)/2;return n.map((i,l)=>{let u=o;return l===2?u=r:l===4&&(u=a),[i[0],i[1],u]})}correctFaceRotation(t,n,s){let[r,a]=n.landmarks.length>=N8.count?N8.symmetryLine:Sle.symmetryLine,o=m8(n.landmarks[r],n.landmarks[a]),i=Pd({startPoint:n.startPoint,endPoint:n.endPoint}),l=[i[0]/s.shape[2],i[1]/s.shape[1]],u=De.rotateWithOffset(s,o,0,l),c=B2(-o,i),d=t.face.mesh.enabled?Md({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.meshSize,this.meshSize]):Md({startPoint:n.startPoint,endPoint:n.endPoint},u,[this.boxSize,this.boxSize]),p=he(d,255);return Z(d),Z(u),[o,c,p]}async augmentIris(t,n,s){if(!this.irisModel)return s.debug&&re("face mesh detection requested, but model is not loaded"),t;let{box:r,boxSize:a,crop:o}=this.getEyeBox(t,n,Fu.leftBounds[0],Fu.leftBounds[1],!0),{box:i,boxSize:l,crop:u}=this.getEyeBox(t,n,Fu.rightBounds[0],Fu.rightBounds[1]),c=gt([o,u]);Z(o),Z(u);let d=this.irisModel.predict(c);Z(c);let p=await d.data();Z(d);let h=p.slice(0,$u.numCoordinates*3),{rawCoords:f,iris:m}=this.getEyeCoords(h,r,a,!0),g=p.slice($u.numCoordinates*3),{rawCoords:A,iris:y}=this.getEyeCoords(g,i,l),x=this.getLeftToRightEyeDepthDifference(t);Math.abs(x)<30?(k0(t,f,"left",null),k0(t,A,"right",null)):x<1?k0(t,f,"left",["EyeUpper0","EyeLower0"]):k0(t,A,"right",["EyeUpper0","EyeLower0"]);let b=this.getAdjustedIrisCoords(t,m,"left"),v=this.getAdjustedIrisCoords(t,y,"right");return t.concat(b).concat(v)}async predict(t,n){let s=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t,n),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes){let l=await i.box.startPoint.data(),u=await i.box.endPoint.data(),c=await i.landmarks.array();this.storedBoxes.push({startPoint:l,endPoint:u,landmarks:c,confidence:i.confidence})}this.storedBoxes.length>0&&(s=!0)}if(s){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let l=h8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),u=y0(l),c=x0(u),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:p,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{Z(i.box.startPoint),Z(i.box.endPoint),Z(i.landmarks)});let a=[],o=[];for(let i of this.storedBoxes){let l,u=0,c;if(n.face.detector.rotation&&n.face.mesh.enabled&&ie.kernels.includes("rotatewithoffset"))[u,c,l]=this.correctFaceRotation(n,i,t);else{c=b0;let d=t.clone(),p=n.face.mesh.enabled?Md({startPoint:i.startPoint,endPoint:i.endPoint},d,[this.meshSize,this.meshSize]):Md({startPoint:i.startPoint,endPoint:i.endPoint},d,[this.boxSize,this.boxSize]);l=he(p,255),Z(p),Z(d)}if(!n.face.mesh.enabled)a.push({mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:l});else if(!this.meshDetector)n.debug&&re("face mesh detection requested, but model is not loaded");else{let[d,p,h]=this.meshDetector.execute(l);Z(d);let f=(await p.data())[0];Z(p);let m=V(h,[-1,3]),g=await m.array();if(Z(h),Z(m),f<n.face.detector.minConfidence)i.confidence=f,Z(l);else{n.face.iris.enabled&&(g=await this.augmentIris(g,l,n));let A=this.transformRawCoords(g,i,u,c);i={...y0(L2(A),1.5),confidence:i.confidence},n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&ie.kernels.includes("rotatewithoffset")&&(Z(l),[u,c,l]=this.correctFaceRotation(n,i,t)),a.push({mesh:A,box:i,faceConfidence:f,boxConfidence:i.confidence,confidence:f,image:l}),i={...x0(i),confidence:i.confidence,faceConfidence:f}}}o.push(i)}return n.face.mesh.enabled&&(this.storedBoxes=o.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=a.length,a}};var kt=[null,null,null],j2;async function E8(e,t){let n=await j2.predict(e,t),s=[],r=0;for(let a of n||[]){if(!a||a.isDisposedInternal)continue;let o=a.mesh.map(c=>[c[0]/(e.shape[2]||0),c[1]/(e.shape[1]||0),c[2]/j2.meshSize]),i={};if(a.mesh&&a.mesh.length>0)for(let c of Object.keys(yr))i[c]=yr[c].map(d=>a.mesh[d]);let l=a.box?[Math.trunc(Math.max(0,a.box.startPoint[0])),Math.trunc(Math.max(0,a.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,a.box.endPoint[0])-Math.max(0,a.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,a.box.endPoint[1])-Math.max(0,a.box.startPoint[1]))]:[0,0,0,0],u=a.box?[a.box.startPoint[0]/(e.shape[2]||0),a.box.startPoint[1]/(e.shape[1]||0),(a.box.endPoint[0]-a.box.startPoint[0])/(e.shape[2]||0),(a.box.endPoint[1]-a.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];s.push({id:r++,score:Math.round(100*a.faceConfidence||100*a.boxConfidence||0)/100,boxScore:Math.round(100*a.boxConfidence)/100,faceScore:Math.round(100*a.faceConfidence)/100,box:l,boxRaw:u,mesh:a.mesh,meshRaw:o,annotations:i,tensor:a.image})}return s}async function I0(e){var t;return ie.initial&&(kt=[null,null,null]),!kt[0]&&e.face.enabled||!kt[1]&&e.face.mesh.enabled||!kt[2]&&e.face.iris.enabled||ie.initial?(kt=await Promise.all([!kt[0]&&e.face.enabled?k8(e):null,!kt[1]&&e.face.mesh.enabled?rt(it(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!kt[2]&&e.face.iris.enabled?rt(it(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!kt[1]||!kt[1].modelUrl?re("load model failed:",e.face.mesh.modelPath):e.debug&&re("load model:",kt[1].modelUrl)),e.face.iris.enabled&&(!kt[2]||!kt[2].modelUrl?re("load model failed:",e.face.iris.modelPath):e.debug&&re("load model:",kt[2].modelUrl))):e.debug&&(kt[0]&&re("cached model:",kt[0].model.modelUrl),kt[1]&&re("cached model:",kt[1].modelUrl),kt[2]&&re("cached model:",kt[2].modelUrl)),j2=new G2(kt[0],kt[1],kt[2]),[((t=kt[0])==null?void 0:t.model)||null,kt[1],kt[2]]}var R8=bi,D8=zd;var Mn,S0=[],_8=0,q2=Number.MAX_SAFE_INTEGER;async function F8(e){var n,s;let t=it(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return ie.initial&&(Mn=null),Mn?e.debug&&re("cached model:",t):(Mn=await rt(t),Mn?e.debug&&re("load model:",t):re("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),Mn}function X2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let s=5*e.map((a,o)=>Math.abs(e[o]-t[o])**n).reduce((a,o)=>a+o,0)**(1/n);return Math.max(0,100-s)/100}function $8(e,t,n=0){let s={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return s;for(let r of t)if(r.embedding&&r.name){let a=X2(e,r.embedding);a>n&&a>s.similarity&&(s={...r,similarity:a})}return s}function K2(e){return H(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Ge))return null;let s=[[.05,.15,.85,.85]];if(!(Mn==null?void 0:Mn.inputs[0].shape))return null;let r=n.shape.length===3?De.cropAndResize(Lt(n,0),s,[0],[Mn.inputs[0].shape[2],Mn.inputs[0].shape[1]]):De.cropAndResize(n,s,[0],[Mn.inputs[0].shape[2],Mn.inputs[0].shape[1]]);return z(r,255)})}async function Z2(e,t,n,s){var r,a,o;return Mn?q2<(((r=t.face.description)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&_8===s&&((a=S0[n])==null?void 0:a.age)&&((o=S0[n])==null?void 0:o.age)>0?(q2++,S0[n]):(q2=0,new Promise(async i=>{var d,p;let l=K2(e),u,c={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(((d=t.face.description)==null?void 0:d.enabled)&&(u=await(Mn==null?void 0:Mn.predict(l))),Z(l),u){let h=await u.find(b=>b.shape[1]===1).data(),f=Math.trunc(200*Math.abs(h[0]-.5))/100;f>(((p=t.face.description)==null?void 0:p.minConfidence)||0)&&(c.gender=h[0]<=.5?"female":"male",c.genderScore=Math.min(.99,f));let m=Ws(u.find(b=>b.shape[1]===100),1),g=(await m.data())[0];Z(m);let A=await u.find(b=>b.shape[1]===100).data();c.age=Math.round(A[g-1]>A[g+1]?10*g-100*A[g-1]:10*g+100*A[g+1])/10;let x=await u.find(b=>b.shape[1]===1024).data();c.descriptor=[...x],u.forEach(b=>Z(b))}S0[n]=c,_8=s,i(c)})):null}var Cle=["angry","disgust","fear","happy","sad","surprise","neutral"],nn,C0=[],O8=0,Y2=Number.MAX_SAFE_INTEGER,J2=[.2989,.587,.114];async function P8(e){var t;return ie.initial&&(nn=null),nn?e.debug&&re("cached model:",nn.modelUrl):(nn=await rt(it(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!nn||!nn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",nn.modelUrl)),nn}async function Q2(e,t,n,s){var r;return nn?Y2<(((r=t.face.emotion)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&O8===s&&C0[n]&&C0[n].length>0?(Y2++,C0[n]):(Y2=0,new Promise(async a=>{var g,A;let o=De.resizeBilinear(e,[(nn==null?void 0:nn.inputs[0].shape)?nn.inputs[0].shape[2]:0,(nn==null?void 0:nn.inputs[0].shape)?nn.inputs[0].shape[1]:0],!1),[i,l,u]=Ht(o,3,3);Z(o);let c=z(i,J2[0]),d=z(l,J2[1]),p=z(u,J2[2]);Z(i),Z(l),Z(u);let h=bh([c,d,p]);Z(c),Z(d),Z(p);let f=H(()=>z(ye(h,.5),2));Z(h);let m=[];if((g=t.face.emotion)==null?void 0:g.enabled){let y=await(nn==null?void 0:nn.predict(f)),x=await y.data();Z(y);for(let b=0;b<x.length;b++)x[b]>(((A=t.face.emotion)==null?void 0:A.minConfidence)||0)&&m.push({score:Math.min(.99,Math.trunc(100*x[b])/100),emotion:Cle[b]});m.sort((b,v)=>v.score-b.score)}Z(f),C0[n]=m,O8=s,a(m)})):null}var Ld=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],M8=Ld.length,Bd=Ld.reduce((e,t,n)=>(e[t]=n,e),{}),Tle=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Nle=Tle.map(([e,t])=>[Bd[e],Bd[t]]),z8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function L8(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function B8(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(u,c)=>({id:c,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/s,u.box[2]/r,u.box[3]/s],box:[Math.trunc(u.box[0]*o),Math.trunc(u.box[1]*a),Math.trunc(u.box[2]*o),Math.trunc(u.box[3]*a)],keypoints:u.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]}))});return e.map((u,c)=>i(u,c))}var ex=class{constructor(t,n){xe(this,"priorityQueue");xe(this,"numberOfElements");xe(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function 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s={};s.batched=this.model.predict(t),s.predictions=st(s.batched),s.scores=H(()=>st(Un(_e(s.predictions,[0,0],[-1,1]))));let r=await s.scores.data();s.boxes=_e(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await De.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=_e(s.norm,[i,0],[1,-1]),u=H(()=>V(this.normalizeLandmarks(_e(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:u,confidence:r[i]})}for(let i of Object.keys(s))Z(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=H(()=>ye(he(De.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);Z(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let u=await l.box.data(),c=u.slice(0,2),d=u.slice(2,4),p=await l.palmLandmarks.array();Z(l.box),Z(l.palmLandmarks),i.push(q8({startPoint:c,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function Ole(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function K8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Ole(n)}var Z8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Na(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function Ple(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function Y8(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Na(e[r],Ple(t,a)))}return n}function ix(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=Z8(t[0],t[1]),o=Y8(a,r),i=Z8(-t[0],-t[1]);return Y8(o,i)}function J8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Na(t[0],n),-Na(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function lx(e,t){return[Na(e,t[0]),Na(e,t[1])]}var 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s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(!e||!Wt[1])return s;if(n.hand.landmarks){let r={};if(!t.yxBox)return s;r.crop=De.cropAndResize(e,[t.yxBox],[0],[Ur[1][0],Ur[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=he(r.cast,255),[r.score,r.keypoints]=Wt[1].execute(r.div);let a=Math.round(100*(await r.score.data())[0]/100);if(a>(n.hand.minConfidence||0)){s.fingerScore=a,r.reshaped=V(r.keypoints,[-1,3]);let o=await r.reshaped.array();s.keypoints=o.map(l=>[t.box[2]*l[0]/Ur[1][0]+t.box[0],t.box[3]*l[1]/Ur[1][1]+t.box[1],(t.box[2]+t.box[3])/2/Ur[1][0]*l[2]]);let i=_0(s.keypoints,Ud,Mu);t.box=i.box,t.boxRaw=i.boxRaw,t.yxBox=i.yxBox,s.box=t.box,s.landmarks=D0(s.keypoints);for(let l of Object.keys(dI))s.annotations[l]=dI[l].map(u=>s.landmarks&&s.keypoints[u]?s.keypoints[u]:null);xr.tmpBoxes.push(t)}Object.keys(r).forEach(o=>Z(r[o]))}return s}async 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vI(o,t,e,Ra[a]);r=r.concat(i),Object.keys(s).forEach(l=>Z(s[l]))}if(r.length!==t.body.maxDetected&&Ix>(t.body.skipFrames||0)){s.resized=De.resizeBilinear(e,[wi,wi],!1),s.cast=pe(s.resized,"int32"),s.res=await(rn==null?void 0:rn.predict(s.cast));let a=await s.res.array();r=s.res.shape[2]===17?await bI(a,t,e,[0,0,1,1]):await vI(a,t,e,[0,0,1,1]),Object.keys(s).forEach(o=>Z(s[o])),Ra.length=0,Ix=0}if(t.skipFrame){Ra.length=0;for(let a=0;a<r.length;a++)if(r[a].keypoints.length>10){let o=r[a].keypoints.map(l=>l.position),i=_0(o,1.5,[e.shape[2],e.shape[1]]);Ra.push([...i.yxBox])}}n(r)})}var Lu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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A=c[m].annotations&&c[m].annotations.leftEyeIris&&c[m].annotations.rightEyeIris&&c[m].annotations.leftEyeIris.length>0&&c[m].annotations.rightEyeIris.length>0&&c[m].annotations.leftEyeIris[0]!==null&&c[m].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[m].annotations.leftEyeIris[3][0]-c[m].annotations.leftEyeIris[1][0]),Math.abs(c[m].annotations.rightEyeIris[4][1]-c[m].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y=e.config.face.detector.return?st(c[m].tensor):null;Z(c[m].tensor),c[m].tensor&&delete c[m].tensor,u.push({...c[m],id:m,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:g,tensor:y}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var NI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position.y<a.position.y&&r.position.y<a.position.y?t.push({body:n,gesture:"i give up"}):a&&s&&s.position.y<a.position.y?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position.y<a.position.y&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position.y>i.position.y?"left":"right"}`})}return t},EI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},RI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(u=!1),p>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(u=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},DI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=iI(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Hr={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},Ii=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("invalid canvas")},z0=e=>Math.round(e*180/Math.PI);function Fx(e,t,n,s=0,r){e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Gd(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else 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p=[bi[d*3+0],bi[d*3+1],bi[d*3+2]].map(h=>u.mesh[h]);$x(r,p,s)}if(u.annotations&&u.annotations.leftEyeIris){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((o=(a=u.rotation)==null?void 0:a.gaze)==null?void 0:o.strength)&&((l=(i=u.rotation)==null?void 0:i.gaze)==null?void 0:l.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.beginPath();let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]),r.lineTo(d[0],d[1]);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];r.moveTo(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]),r.lineTo(p[0],p[1]),r.stroke()}}}}}async function Mx(e,t,n){var a;let s=Vt(Hr,n);if(!t||!e)return;let r=Ii(e);r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(Gd(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Fx(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&(r.font=s.font,t[o].keypoints))for(let i of 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function zx(e,t,n){let s=Vt(Hr,n);if(!t||!e)return;let r=Ii(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Gd(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`${a.label}:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`${a.label}:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Fx(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 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s=et(),r=Vt(Hr,n),a=Promise.all([Px(e,t.face,r),Mx(e,t.body,r),zx(e,t.hand,r),Lx(e,t.object,r),Ox(e,t.gesture,r)]);return t.performance.draw=Math.trunc(et()-s),a}function OI(e,t,n,s,r){var i,l,u,c,d,p,h,f,m,g,A,y,x,b,v,k;let a=0,o=[];for(let S of e){let C={id:a++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let P of t)S.box[0]>P.box[0]&&S.box[0]<P.box[0]+P.box[2]&&S.box[1]+S.box[3]>P.box[1]&&S.box[1]+S.box[3]<P.box[1]+P.box[3]&&(C.body=P);if(C.body)for(let P of n)P.box[0]+P.box[2]>C.body.box[0]&&P.box[0]+P.box[2]<C.body.box[0]+C.body.box[2]&&P.box[1]+P.box[3]>C.body.box[1]&&P.box[1]+P.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=P),P.box[0]<C.body.box[0]+C.body.box[2]&&P.box[0]>C.body.box[0]&&P.box[1]+P.box[3]>C.body.box[1]&&P.box[1]+P.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=P);for(let P of s)P.face!==void 0&&P.face===S.id?(i=C.gestures)==null||i.push(P):P.iris!==void 0&&P.iris===S.id?(l=C.gestures)==null||l.push(P):P.body!==void 0&&P.body===((u=C.body)==null?void 0:u.id)?(c=C.gestures)==null||c.push(P):P.hand!==void 0&&P.hand===((p=(d=C.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=C.gestures)==null||h.push(P):P.hand!==void 0&&P.hand===((m=(f=C.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=C.gestures)==null||g.push(P));let D=[],O=[],E=P=>{P&&P.length===4&&(D.push(P[0],P[0]+P[2]),O.push(P[1],P[1]+P[3]))};E((A=C.face)==null?void 0:A.box),E((y=C.body)==null?void 0:y.box),E((b=(x=C.hands)==null?void 0:x.left)==null?void 0:b.box),E((k=(v=C.hands)==null?void 0:v.right)==null?void 0:k.box);let R=Math.min(...D),T=Math.min(...O);C.box=[R,T,Math.max(...D)-R,Math.max(...O)-T],r&&r[1]&&r[2]&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),o.push(C)}return o}var $e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function PI(e){var s,r,a,o,i,l,u,c,d,p,h,f,m,g,A,y,x,b,v,k,S;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t+1):1;if($e.canvas=e.canvas,!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let C=0;C<e.body.length;C++){let D=e.body[C].box.map((R,T)=>((n-1)*$e.body[C].box[T]+R)/n),O=e.body[C].boxRaw.map((R,T)=>((n-1)*$e.body[C].boxRaw[T]+R)/n),E=e.body[C].keypoints.map((R,T)=>({score:R.score,part:R.part,position:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[0]+R.position[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].position[1]+R.position[1])/n:R.position[1]],positionRaw:[$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[0]+R.positionRaw[0])/n:R.position[0],$e.body[C].keypoints[T]?((n-1)*$e.body[C].keypoints[T].positionRaw[1]+R.positionRaw[1])/n:R.position[1]]}));$e.body[C]={...e.body[C],box:D,boxRaw:O,keypoints:E}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let C=0;C<e.hand.length;C++){let D=e.hand[C].box.map((T,P)=>((n-1)*$e.hand[C].box[P]+T)/n),O=e.hand[C].boxRaw.map((T,P)=>((n-1)*$e.hand[C].boxRaw[P]+T)/n);$e.hand[C].keypoints.length!==e.hand[C].keypoints.length&&($e.hand[C].keypoints=e.hand[C].keypoints);let E=e.hand[C].keypoints&&e.hand[C].keypoints.length>0?e.hand[C].keypoints.map((T,P)=>T.map((U,j)=>((n-1)*($e.hand[C].keypoints[P][j]||1)+(U||0))/n)):[],R={};if(Object.keys($e.hand[C].annotations).length!==Object.keys(e.hand[C].annotations).length&&($e.hand[C].annotations=e.hand[C].annotations),e.hand[C].annotations)for(let T of Object.keys(e.hand[C].annotations))R[T]=e.hand[C].annotations[T]&&e.hand[C].annotations[T][0]?e.hand[C].annotations[T].map((P,U)=>P.map((j,q)=>((n-1)*$e.hand[C].annotations[T][U][q]+j)/n)):null;$e.hand[C]={...e.hand[C],box:D,boxRaw:O,keypoints:E,annotations:R}}if(!$e.face||e.face.length!==$e.face.length)$e.face=JSON.parse(JSON.stringify(e.face));else for(let C=0;C<e.face.length;C++){let D=e.face[C].box.map((R,T)=>((n-1)*$e.face[C].box[T]+R)/n),O=e.face[C].boxRaw.map((R,T)=>((n-1)*$e.face[C].boxRaw[T]+R)/n),E={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};E.matrix=(s=e.face[C].rotation)==null?void 0:s.matrix,E.angle={roll:((n-1)*(((a=(r=$e.face[C].rotation)==null?void 0:r.angle)==null?void 0:a.roll)||0)+(((i=(o=e.face[C].rotation)==null?void 0:o.angle)==null?void 0:i.roll)||0))/n,yaw:((n-1)*(((u=(l=$e.face[C].rotation)==null?void 0:l.angle)==null?void 0:u.yaw)||0)+(((d=(c=e.face[C].rotation)==null?void 0:c.angle)==null?void 0:d.yaw)||0))/n,pitch:((n-1)*(((h=(p=$e.face[C].rotation)==null?void 0:p.angle)==null?void 0:h.pitch)||0)+(((m=(f=e.face[C].rotation)==null?void 0:f.angle)==null?void 0:m.pitch)||0))/n},E.gaze={bearing:((n-1)*(((A=(g=$e.face[C].rotation)==null?void 0:g.gaze)==null?void 0:A.bearing)||0)+(((x=(y=e.face[C].rotation)==null?void 0:y.gaze)==null?void 0:x.bearing)||0))/n,strength:((n-1)*(((v=(b=$e.face[C].rotation)==null?void 0:b.gaze)==null?void 0:v.strength)||0)+(((S=(k=e.face[C].rotation)==null?void 0:k.gaze)==null?void 0:S.strength)||0))/n},$e.face[C]={...e.face[C],rotation:E,box:D,boxRaw:O}}if(!$e.object||e.object.length!==$e.object.length)$e.object=JSON.parse(JSON.stringify(e.object));else for(let C=0;C<e.object.length;C++){let D=e.object[C].box.map((E,R)=>((n-1)*$e.object[C].box[R]+E)/n),O=e.object[C].boxRaw.map((E,R)=>((n-1)*$e.object[C].boxRaw[R]+E)/n);$e.object[C]={...e.object[C],box:D,boxRaw:O}}if(e.persons){let C=e.persons;if(!$e.persons||C.length!==$e.persons.length)$e.persons=JSON.parse(JSON.stringify(C));else for(let D=0;D<C.length;D++)$e.persons[D].box=C[D].box.map((O,E)=>((n-1)*$e.persons[D].box[E]+O)/n)}return e.gesture&&($e.gesture=e.gesture),e.performance&&($e.performance=e.performance),$e}var Bx="2.2.3";var L0=`
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2Q==`;async function sue(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(L0);break;case"body":case"full":n=await t(B0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function rue(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+L0;break;case"full":case"body":n="data:image/jpeg;base64,"+B0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:ie.Image&&(s=new ie.Image),s.onload=async()=>{let r=ps(s.naturalWidth,s.naturalHeight);if(!r)re("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function aue(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(L0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(B0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&re("Warmup tfjs-node not loaded");return s}async function MI(e,t){let n=et();if(e.state="warmup",t&&(e.config=Vt(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await sue(e):typeof Image!="undefined"||ie.Canvas!==void 0?s=await rue(e):s=await aue(e);let a=et();e.config.debug&&re("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Bu,qd,Xd,W0,LI=class{constructor(t){xe(this,"version");xe(this,"config");xe(this,"result");xe(this,"state");xe(this,"process");xe(this,"tf");xe(this,"env");xe(this,"draw");xe(this,"models");xe(this,"events");xe(this,"faceTriangulation");xe(this,"faceUVMap");xe(this,"performance");Qu(this,Bu,void 0);Qu(this,qd,void 0);Qu(this,Xd,void 0);xe(this,"gl");xe(this,"analyze",(...t)=>{if(!Ju(this,qd))return;let n=this.tf.engine().state.numTensors,s=Ju(this,Bu);ec(this,Bu,n);let r=n-s;r!==0&&re(...t,r)});Qu(this,W0,t=>{if(!Ju(this,Xd))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Ge))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});xe(this,"emit",t=>{var n;return(n=this.events)==null?void 0:n.dispatchEvent(new Event(t))});w0(),this.env=ie,Yr.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${gh}/dist/`,Yr.modelBasePath=this.env.browser?"../models/":"file://models/",Yr.backend=this.env.browser?"humangl":"tensorflow",this.version=Bx,Object.defineProperty(this,"version",{value:Bx}),this.config=JSON.parse(JSON.stringify(Yr)),Object.seal(this.config),t&&(this.config=Vt(this.config,t)),this.tf=xi,this.state="idle",ec(this,Bu,0),ec(this,qd,!1),ec(this,Xd,!1),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=new EventTarget,this.models=new Hd,this.draw={options:Hr,canvas:(n,s)=>FI(n,s),face:(n,s,r)=>Px(n,s,r),body:(n,s,r)=>Mx(n,s,r),hand:(n,s,r)=>zx(n,s,r),gesture:(n,s,r)=>Ox(n,s,r),object:(n,s,r)=>Lx(n,s,r),person:(n,s,r)=>_I(n,s,r),all:(n,s,r)=>$I(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=R8,this.faceUVMap=D8,this.gl=$t,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Yr)),this.config.backend=t}validate(t){return mg(Yr,t||this.config)}image(t){return _u(t,this.config)}similarity(t,n){return X2(t,n)}async segmentation(t,n){return II(t,n,this.config)}enhance(t){return K2(t)}match(t,n,s=0){return $8(t,n,s)}async init(){await F0(this,!0),await this.tf.ready(),T8(this.env)}async load(t){this.state="load";let n=et(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=Vt(this.config,t)),ie.initial&&(this.config.debug&&re(`version: ${this.version}`),this.config.debug&&re(`tfjs version: ${this.tf.version_core}`),await F0(this)||re("error: backend check failed"),await yh(),this.env.browser&&(this.config.debug&&re("configuration:",this.config),this.config.debug&&re("tf flags:",this.tf.ENV.flags))),await CI(this),ie.initial&&this.config.debug&&re("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ie.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await TI(this),this.emit("load"));let a=Math.trunc(et()-n);a>(this.performance.load||0)&&(this.performance.load=a)}next(t=this.result){return PI(t)}async warmup(t){return MI(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var A,y,x,b,v,k,S,C,D,O,E,R,T,P,U,j,q,X,te,ne,se,ae;this.state="config";let r,a;this.config=Vt(this.config,n),this.state="check";let o=Ju(this,W0).call(this,t);o&&(re(o,t),s({error:o}));let i=et();await F0(this),await this.load(),r=et(),this.state="image";let l=_u(t,this.config);if(this.process=l,this.performance.image=Math.trunc(et()-r),this.analyze("Get Image:"),!l.tensor){this.config.debug&&re("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=et(),this.config.skipFrame=await C8(this.config,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(et()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],p=[];this.state="detect:face",this.config.async?(u=this.config.face.enabled?_x(this,l.tensor):[],this.performance.face&&delete this.performance.face):(r=et(),u=this.config.face.enabled?await _x(this,l.tensor):[],a=Math.trunc(et()-r),a>0&&(this.performance.face=a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(u=await u),this.analyze("Start Body:"),this.state="detect:body";let h=this.config.body.maxDetected===-1?Vt(this.config,{body:{maxDetected:this.config.face.enabled?1*u.length:1}}):this.config;this.config.async?(((A=this.config.body.modelPath)==null?void 0:A.includes("posenet"))?c=this.config.body.enabled?ax(l.tensor,h):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?c=this.config.body.enabled?yx(l.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?kx(l.tensor,h):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("movenet"))&&(c=this.config.body.enabled?Sx(l.tensor,h):[]),this.performance.body&&delete this.performance.body):(r=et(),((v=this.config.body.modelPath)==null?void 0:v.includes("posenet"))?c=this.config.body.enabled?await ax(l.tensor,h):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?c=this.config.body.enabled?await yx(l.tensor,h):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("efficientpose"))?c=this.config.body.enabled?await kx(l.tensor,h):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(c=this.config.body.enabled?await Sx(l.tensor,h):[]),a=Math.trunc(et()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?Vt(this.config,{hand:{maxDetected:this.config.face.enabled?2*u.length:1}}):this.config;this.config.async?(((O=(D=this.config.hand.detector)==null?void 0:D.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?dx(l.tensor,f):[]:((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handtrack"))&&(d=this.config.hand.enabled?gx(l.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=et(),((P=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:P.includes("handdetect"))?d=this.config.hand.enabled?await dx(l.tensor,f):[]:((j=(U=this.config.hand.detector)==null?void 0:U.modelPath)==null?void 0:j.includes("handtrack"))&&(d=this.config.hand.enabled?await gx(l.tensor,f):[]),a=Math.trunc(et()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((q=this.config.object.modelPath)==null?void 0:q.includes("nanodet"))?p=this.config.object.enabled?Tx(l.tensor,this.config):[]:((X=this.config.object.modelPath)==null?void 0:X.includes("centernet"))&&(p=this.config.object.enabled?Ex(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=et(),((te=this.config.object.modelPath)==null?void 0:te.includes("nanodet"))?p=this.config.object.enabled?await Tx(l.tensor,this.config):[]:((ne=this.config.object.modelPath)==null?void 0:ne.includes("centernet"))&&(p=this.config.object.enabled?await Ex(l.tensor,this.config):[]),a=Math.trunc(et()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([u,c,d,p]=await Promise.all([u,c,d,p])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=et(),m=[...EI(u),...NI(c),...DI(d),...RI(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(et()-r)),this.performance.total=Math.trunc(et()-i);let g=((ae=(se=this.process)==null?void 0:se.tensor)==null?void 0:ae.shape)||[];this.result={face:u,body:c,hand:d,gesture:m,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return OI(u,c,d,m,g)}},Z(l.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Bu=new WeakMap,qd=new WeakMap,Xd=new WeakMap,W0=new WeakMap;return oue;})();
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/**
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* @license
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* Copyright 2017 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* https://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|