mirror of https://github.com/vladmandic/human
4342 lines
1.0 MiB
4342 lines
1.0 MiB
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/*
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Human library
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let o=e[t],{success:n,asyncInit:s}=this.initializeBackend(o);if(s||n)return{name:o,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let o=this.state.tensorInfo.get(t),n=o.backend,s=this.readSync(t),a=n.refCount(t);n.disposeData(t,!0),o.backend=e,e.move(t,s,o.shape,o.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let o=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function 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this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=ew(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ew(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Sm(d,this.backendName);A(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();l=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let _=w.map(k=>{if(k.rank!=null)return k;let{dataId:D,shape:T,dtype:R}=k;return this.makeTensorFromDataId(D,T,R)});if(n){let k=this.getTensorsForGradient(d,h,_);o=this.saveTensorsForBackwardMode(k)}return _}}else{let{forwardFunc:d}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let x=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:p}=e,m=ew(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),n&&this.addTapeNode(u,c,t,m,o,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Ub(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Uh(e.dtype)),this.state.numBytes+=o,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:o})),e instanceof nl||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 o=e.size*Uh(e.dtype);this.state.numBytes-=o}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,o=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},l=Ub(e);l!=null&&(n=l.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,p)=>{if(c==null){let m=o[p],f=pc(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Dm(e),o=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!o.has(a.id)&&a.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),o!=null&&o.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(s instanceof Ve,()=>"The result y returned by f() must be a tensor.");let a=uS(this.state.activeTape,t,s);if(!n&&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 i={};i[s.id]=o==null?wG(s.shape):o,cS(i,a,u=>this.tidy(u),_G);let l=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:l}})}customGrad(e){return A(Rs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(i=>i instanceof Ve),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let o,n={};t.forEach((i,l)=>{n[l]=i});let s=(i,l)=>(o=e(...t,l),A(o.value instanceof Ve,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(Rs(o.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),o.value),a=(i,l)=>{let u=o.gradFunc(i,l),c=Array.isArray(u)?u:[u];A(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),A(c.every(m=>m instanceof Ve),()=>"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 p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}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=Vl(),o=await this.backend.time(e);return o.wallMs=Vl()-t,o}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 tw;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}};Wl.nextTensorId=0;Wl.nextVariableId=0;function wG(r){let e=bm(ct(r),"float32");return E.makeTensor(e,r,"float32")}function rw(){let r=Wb();if(r._tfengine==null){let e=new Hh(r);r._tfengine=new Wl(e)}return nS(r._tfengine.ENV),dS(()=>r._tfengine),r._tfengine}var E=rw();function _G(r,e){let t={a:r,b:e};return E.runKernel(Fo,t)}var jl={};Ge(jl,{isBrowser:()=>ow,isMobile:()=>vG});function kG(){return typeof navigator!="undefined"&&navigator!=null}function vG(){if(kG()){let r=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(r)||/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(r.substr(0,4))}return!1}function ow(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ms=W();ms.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. 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Actual: ${n}.
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Actual: ${n}.
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Expected: ${s}.`)}}function dW(r,e){r().then(()=>e.fail(),()=>e())}function hW(r,e){let t=typeof e=="string"||typeof e=="number"||typeof e=="boolean"?[e]:e;return _n(r)||_n(r[0])||_n(e)||_n(e[0])?ww(r,t,(o,n)=>o==n):ww(r,e,(o,n)=>_w(o,n,0))}function gW(r,e,t){if(t==null&&(t=bw()),!_w(r,e,t))throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`)}function _w(r,e,t){return!isFinite(r)&&!isFinite(e)?!0:!(isNaN(r)||isNaN(e)||Math.abs(r-e)>t)}function xW(r,e,t){for(let o=0;o<r.length;o++)if(r[o]<e||r[o]>t)throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t}`)}function yW(r,e){expect(new Float32Array(r)).toEqual(new Float32Array(e))}function dT(r){for(let e=0;e<r.length;e++){let t=r[e];Array.isArray(t)?dT(t):r[e]=ol(t)}return r}var bW="3.3.0";function wie(){W().set("PROD",!0)}function _ie(){W().set("DEBUG",!0)}function kie(){W().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function 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Got strides ${t} and dilations '${a}'`);let i=s,l=!1;s.rank===3&&(l=!0,i=L(s,[1,s.shape[0],s.shape[1],s.shape[2]])),A(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),n!=null&&A(ot(o),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${n} but got pad ${o}.`);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n},p=E.runKernel(Cn,u,c);return p=ne(p,s.dtype),l?L(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ua=S({avgPool_:UW});function HW(r,e,t,o,n,s="NDHWC"){let a=v(r,"x","avgPool3d","float32"),i=a,l=!1;a.rank===4&&(l=!0,i=L(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),A(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),A(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),n!=null&&A(ot(o),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${n} but got pad ${o}.`);let u={x:i},c={filterSize:e,strides:t,pad:o,dimRoundingMode:n,dataFormat:s},p=E.runKernel(Wa,u,c);return p=ne(p,i.dtype),l?L(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Gm=S({avgPool3d_:HW});function qW(r,e=0){A(r.length>=1,()=>"Pass at least one tensor to concat");let t=na(r,"tensors","concat","string_or_numeric");if(t[0].dtype==="complex64"&&t.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
|
|
with dtype ${s.dtype}. `)}),t.length===1)return Po(t[0]);let o=t,n={axis:e};return E.runKernel(Ps,o,n)}var Ze=S({concat_:qW});function KW(r){let t={x:v(r,"x","sigmoid")};return E.runKernel(ns,t)}var Zr=S({sigmoid_:KW});function XW(r,e,t){let o=v(r,"x","slice","string_or_numeric");if(o.rank===0)throw new Error("Slicing scalar is not possible");let n={x:o},s={begin:e,size:t};return E.runKernel(js,n,s)}var Re=S({slice_:XW});function YW(r){let t={x:v(r,"x","tanh")};return E.runKernel(cs,t)}var Qs=S({tanh_:YW});function ZW(r,e,t,o,n,s){let a=v(r,"forgetBias","basicLSTMCell"),i=v(e,"lstmKernel","basicLSTMCell"),l=v(t,"lstmBias","basicLSTMCell"),u=v(o,"data","basicLSTMCell"),c=v(n,"c","basicLSTMCell"),p=v(s,"h","basicLSTMCell"),m=Ze([u,p],1),f=je(m,i),d=ee(f,l),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Re(d,[0,0],x),w=Re(d,[0,g],x),_=Re(d,[0,g*2],x),k=Re(d,[0,g*3],x),D=ee(P(Zr(b),Qs(w)),P(c,Zr(ee(a,_)))),T=P(Qs(D),Zr(k));return[D,T]}var JW=S({basicLSTMCell_:ZW});function QW(r,e,t){let o=v(r,"x","batchToSpaceND"),n=e.reduce((i,l)=>i*l);A(o.rank>=1+e.length,()=>`input rank is ${o.rank} but should be > than blockShape.length ${e.length}`),A(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),A(o.shape[0]%n==0,()=>`input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`);let s={x:o},a={blockShape:e,crops:t};return E.runKernel(ja,s,a)}var ca=S({batchToSpaceND_:QW});function yT(r){let e;return r.rank===0||r.rank===1?e=L(r,[1,1,1,r.size]):r.rank===2?e=L(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=L(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function ej(r,e,t,o,n,s){s==null&&(s=.001);let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;o!=null&&(c=v(o,"offset","batchNorm")),A(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),A(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),A(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:yT(a),scale:u,offset:c,mean:i,variance:l},f={varianceEpsilon:s},d=E.runKernel(Pn,m,f);return L(d,a.shape)}var Jo=S({batchNorm_:ej});function tj(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),A(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),A(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),A(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Jo(a,i,l,c,u,s)}var Sw=S({batchNorm2d_:tj});function rj(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),A(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),A(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Jo(a,i,l,c,u,s)}var Tw=S({batchNorm3d_:rj});function oj(r,e,t,o,n,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;n!=null&&(u=v(n,"scale","batchNorm"));let c;return o!=null&&(c=v(o,"offset","batchNorm")),A(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),A(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Jo(a,i,l,c,u,s)}var Aw=S({batchNorm4d_:oj});function nj(r,e,t){let o=v(r,"x","bincount"),n=v(e,"weights","bincount");A(o.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),A(t>=0,()=>`size must be non-negative, but got ${t}.`),A(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return E.runKernel(gc,s,a)}var Ew=S({bincount_:nj});function sj(r,e){let t=v(r,"broadcastTo","x"),o=t.shape;if(e.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${e}].`);if(e.length<t.rank)throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t.rank}.`);if(e.length>t.rank){let u=t.shape.slice();for(;u.length<e.length;)u.unshift(1);t=L(t,u)}let n=t.shape,s=Array.from(e);for(let u=e.length-1;u>=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Po(t);let i={x:t},l={reps:s};return E.runKernel(Oo,i,l)}var ul=S({broadcastTo_:sj});function ij(r){let t={x:v(r,"x","ceil")};return E.runKernel(Nn,t)}var Wm=S({ceil_:ij});function aj(r,e,t){let o=v(r,"x","clipByValue");A(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let n={x:o},s={clipValueMin:e,clipValueMax:t};return E.runKernel(qo,n,s)}var ar=S({clipByValue_:aj});function lj(r){return Ze(r,0)}var Dw=S({concat1d_:lj});function uj(r,e){return Ze(r,e)}var $w=S({concat2d_:uj});function cj(r,e){return Ze(r,e)}var Rw=S({concat3d_:cj});function pj(r,e){return Ze(r,e)}var Fw=S({concat4d_:pj});function mj(r,e,t,o,n="NHWC",s=[1,1],a){let i=v(r,"x","conv2d"),l=v(e,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1],i.shape[2]])),A(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&A(ot(o),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let p=n==="NHWC"?u.shape[3]:u.shape[1];A(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),A(kr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`);let m={x:u,filter:l},f={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},d=E.runKernel(Sn,m,f);return c?L(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Jr=S({conv2d_:mj});function fj(r,e,t,o,n="NWC",s=1,a){let i=v(r,"x","conv1d"),l=v(e,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1]])),A(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),a!=null&&A(ot(o),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`),A(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A(kr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),A(n==="NWC",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let p=L(l,[1,l.shape[0],l.shape[1],l.shape[2]]),m=L(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Jr(m,p,[1,t],o,"NHWC",[1,s],a);return c?L(g,[g.shape[2],g.shape[3]]):L(g,[g.shape[0],g.shape[2],g.shape[3]])}var Yl=S({conv1d_:fj});function dj(r,e,t,o,n,s="NHWC",a){A(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,l=e,u=!1;e.rank===3&&(u=!0,l=L(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),A(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),A(p===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${t.shape[3]}.`),a!=null&&A(ot(n),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let m={dy:l,filter:t},f={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},d=E.runKernel(Tn,m,f);return u?L(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Zc=S({conv2DBackpropInput_:dj});function hj(r,e,t,o,n,s){let a=v(r,"x","conv2dTranspose"),i=v(e,"filter","conv2dTranspose");return Zc(t,a,i,o,n,"NHWC",s)}var Zl=S({conv2dTranspose_:hj});function gj(r,e,t,o,n="NDHWC",s=[1,1,1]){let a=v(r,"x","conv3d"),i=v(e,"filter","conv3d"),l=a,u=!1;a.rank===4&&(u=!0,l=L(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),A(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),A(kr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),A(n==="NDHWC",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`);let c={x:l,filter:i},p={strides:t,pad:o,dataFormat:n,dilations:s},m=E.runKernel(Ha,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var jm=S({conv3d_:gj});function xj(r,e,t,o,n){A(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=L(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let l=s[4],u=a.shape[4];A(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),A(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),A(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),A(l===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${t.shape[3]}.`),A(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},p={pad:n,strides:o,inputShape:s},m=E.runKernel(wc,c,p);return i?L(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var ug=S({conv3DBackpropInput_:xj});function yj(r,e,t,o,n){let s=v(r,"x","conv3dTranspose"),a=v(e,"filter","conv3dTranspose");return ug(t,s,a,o,n)}var bj=S({conv3dTranspose_:yj});function wj(r){let t={x:v(r,"x","cos")};return E.runKernel(An,t)}var pa=S({cos_:wj});function _j(r){let t={x:v(r,"x","cosh")};return E.runKernel(Ii,t)}var Jl=S({cosh_:_j});function kj(r,e=0,t=!1,o=!1){let s={x:v(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:o};return E.runKernel(En,s,a)}var Ql=S({cumsum_:kj});function vj(r,e,t,o=!1){let n=v(r,"x","denseBincount"),s=v(e,"weights","denseBincount");A(n.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),A(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),A(t>=0,()=>`size must be non-negative, but got ${t}.`),A(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return E.runKernel(_c,a,i)}var Ow=S({denseBincount_:vj});function Cj(r,e,t="NHWC"){let o=v(r,"x","depthToSpace"),n=t==="NHWC"?o.shape[1]:o.shape[2],s=t==="NHWC"?o.shape[2]:o.shape[3],a=t==="NHWC"?o.shape[3]:o.shape[1];A(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${n} and ${e} for depthToSpace with input shape
|
|
${o.shape}`),A(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying
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|
${s} and ${e} for depthToSpace with input shape
|
|
${o.shape}`),A(a%(e*e)==0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},l={blockSize:e,dataFormat:t};return E.runKernel(Si,i,l)}var Um=S({depthToSpace_:Cj});function Ij(r,e,t,o,n="NHWC",s=[1,1],a){let i=v(r,"x","depthwiseConv2d"),l=v(e,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1],i.shape[2]])),A(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),A(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),a!=null&&A(ot(o),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let p={x:u,filter:l},m={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=E.runKernel(Dn,p,m);return c?L(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ds=S({depthwiseConv2d_:Ij});function Nj(r){let t={x:v(r,"x","diag")};return E.runKernel(Cc,t)}var Sj=S({diag_:Nj});function Tj(r,e,t,o,n=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");A(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),A(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=L(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:o,dilations:n},m=E.runKernel(qa,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Hm=S({dilation2d_:Tj});function Aj(r,e){let t=r.length,o=[];for(let n=0;n<t;n++){let s=t-1-n,a=r[s]||1;(e[e.length-1-n]||1)>1&&a===1&&o.unshift(s)}return o}function _t(r,e){let t=[];for(let o=0;o<e.length;o++){let 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rank ${s.rank}.`),A(ot(e),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);let a=s,i=!1;s.rank===3&&(i=!0,a=L(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:a},u={depthRadius:e,bias:t,alpha:o,beta:n},c=E.runKernel(Xa,l,u);return i?L(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Ym=S({localResponseNormalization_:Qj});function e4(r){let t={x:v(r,"x","log")};return E.runKernel(zn,t)}var ur=S({log_:e4});function t4(r){let t={x:v(r,"x","log1p")};return E.runKernel(Bi,t)}var ru=S({log1p_:t4});function r4(r){return A(Rs(r),()=>"The f passed in grad(f) must be a function"),(e,t)=>{let o=v(e,"x","tf.grad","string_or_numeric"),n=t!=null?v(t,"dy","tf.grad"):null;return E.tidy(()=>{let{value:s,grads:a}=E.gradients(()=>r(o),[o],n);return n!=null&&vt(s.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),cg(a),a[0]})}}function o4(r){return A(Rs(r),()=>"The f passed in grads(f) must be a function"),(e,t)=>{A(Array.isArray(e),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let o=na(e,"args","tf.grads","string_or_numeric"),n=t!=null?v(t,"dy","tf.grads"):null;return E.tidy(()=>{let{value:s,grads:a}=E.gradients(()=>r(...o),o,n);return n!=null&&vt(s.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cg(a),a})}}function n4(r){return A(Rs(r),()=>"The f passed in valueAndGrad(f) must be a function"),(e,t)=>{A(e instanceof Ve,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),A(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:o,value:n}=E.gradients(()=>r(e),[e],t);return cg(o),{grad:o[0],value:n}}}function s4(r){return A(Rs(r),()=>"The f passed in valueAndGrads(f) must be a function"),(e,t)=>{A(Array.isArray(e)&&e.every(n=>n instanceof Ve),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),A(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let o=E.gradients(()=>r(...e),e,t);return t!=null&&vt(o.value.shape,t.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),cg(o.grads),o}}function pg(r,e){A(Rs(r),()=>"The f passed in variableGrads(f) must be a function"),A(e==null||Array.isArray(e)&&e.every(u=>u instanceof nl),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let t=e!=null;if(!t){e=[];for(let u in E.registeredVariables)e.push(E.registeredVariables[u])}let o=t?e.filter(u=>!u.trainable):null,n=e.length;e=e.filter(u=>u.trainable),A(e.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`);let s=!0,{value:a,grads:i}=E.gradients(r,e,null,s);A(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),A(a.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);let l={};return e.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),o!=null&&o.forEach(u=>l[u.name]=null),{value:a,grads:l}}function Qr(r){return E.customGrad(r)}function cg(r){if(r.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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the f you passed encloses all operations that lead from x to y.`)}function i4(r){let t={x:v(r,"x","neg")};return E.runKernel(zs,t)}var He=S({neg_:i4});function a4(r){let t={x:v(r,"x","softplus")};return E.runKernel(Qi,t)}var xs=S({softplus_:a4});function l4(r){let e=v(r,"x","logSigmoid");return Qr(o=>({value:He(xs(He(o))),gradFunc:a=>P(a,Zr(He(o)))}))(e)}var Vw=S({logSigmoid_:l4});function u4(r,e=null,t=!1){let n={x:v(r,"x","max")},s={reductionIndices:e,keepDims:t};return E.runKernel(Bn,n,s)}var cr=S({max_:u4});function c4(r,e){let t=v(r,"a","sub"),o=v(e,"b","sub");[t,o]=We(t,o);let n={a:t,b:o};return E.runKernel(us,n)}var ce=S({sub_:c4});function p4(r,e=null,t=!1){let o=v(r,"x","sum");o.dtype==="bool"&&(o=ne(o,"int32"));let n={x:o},s={axis:e,keepDims:t};return E.runKernel(is,n,s)}var ge=S({sum_:p4});function m4(r,e=-1){let t=v(r,"logits","logSoftmax");if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. 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Labels / logits was rank ${e.rank} and dim was ${t}`);return Qr((n,s,a)=>{let l=Jm(s,[t],!0),u=ce(ne(s,"float32"),l);a([n,u]);let c=He(P(u,n));return{value:ge(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=en(f.shape,[t]);return[P(L(f,x),ce(ne(h,"float32"),Zt(g))),P(L(f,x),ce(Zt(g),ne(h,"float32")))]}}})(r,e)}function QH(r,e,t,o=0,n=Gt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),vt(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=le(o),c=le(1),p=le(s.shape[1]);s=ee(P(s,ce(c,u)),me(u,p))}let l=JH(s,a);return Dr(l,i,n)}var h1=S({softmaxCrossEntropy_:QH});var vRe={fft:ba,ifft:ti,rfft:wa,irfft:mu},CRe={hammingWindow:jT,hannWindow:_g,frame:kg,stft:UT},oi={flipLeftRight:qT,resizeNearestNeighbor:Sg,resizeBilinear:Ng,rotateWithOffset:KT,cropAndResize:HT,nonMaxSuppression:XT,nonMaxSuppressionAsync:JT,nonMaxSuppressionWithScore:QT,nonMaxSuppressionWithScoreAsync:e1,nonMaxSuppressionPadded:t1,nonMaxSuppressionPaddedAsync:r1,transform:o1},g1={bandPart:n1,gramSchmidt:s1,qr:a1},IRe={absoluteDifference:l1,computeWeightedLoss:Dr,cosineDistance:u1,hingeLoss:c1,huberLoss:p1,logLoss:m1,meanSquaredError:f1,sigmoidCrossEntropy:d1,softmaxCrossEntropy:h1};var zr=class extends ag{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ae(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return pg(e,t)}dispose(){this.iterations_!=null&&Ae(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(zr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var ip=class extends zr{constructor(e,t,o=null){super();this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:V(()=>Ce(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:V(()=>Ce(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;V(()=>{let c=ee(P(l,this.rho),P(Oe(i),1-this.rho)),p=P(me(gt(ee(u,this.epsilon)),gt(ee(l,this.epsilon))),i),m=ee(P(u,this.rho),P(Oe(p),1-this.rho));l.assign(c),u.assign(m);let f=ee(P(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ip.className="Adadelta";co(ip);var ap=class extends zr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:V(()=>ma(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;V(()=>{let l=ee(i,Oe(a));i.assign(l);let u=ee(P(me(a,gt(ee(l,E.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ae(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};ap.className="Adagrad";co(ap);var lp=class extends zr{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(o).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ce(1,this.accBeta1),n=ce(1,this.accBeta2);t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Ce(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Ce(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=ee(P(c,this.beta1),P(u,1-this.beta1)),f=ee(P(p,this.beta2),P(Oe(u),1-this.beta2)),d=me(m,o),h=me(f,n);c.assign(m),p.assign(f);let g=ee(P(me(d,ee(gt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(Lr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Lr(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};lp.className="Adam";co(lp);var up=class extends zr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ce(1,this.accBeta1),n=me(-this.learningRate,ee(P(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ce(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ce(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=ee(P(c,this.beta1),P(u,1-this.beta1)),f=P(p,this.beta2),d=It(u),h=eo(f,d);c.assign(m),p.assign(h);let g=ee(P(me(n,o),me(m,ee(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(ee(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};up.className="Adamax";co(up);var pl=class extends zr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=E.registeredVariables[o];V(()=>{let i=ee(P(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Et(le(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};pl.className="SGD";co(pl);var cp=class extends pl{constructor(e,t,o=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=le(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulations[n]==null){let l=!1;this.accumulations[n]={originalName:`${o}/momentum`,variable:V(()=>Ce(s).variable(l))}}let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&V(()=>{let l,u=ee(P(this.m,a),i);this.useNesterov?l=ee(P(this.c,ee(i,P(u,this.m))),s):l=ee(P(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ae(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await 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${s}).`);if(t<o)throw new Error(`batchDims (${o}) must be less than or equal to axis (${t}).`);for(let p=0;p<o;++p)if(r.shape[p]!==e.shape[p])throw new Error(`x.shape[${p}]: ${r.shape[p]} should be equal to indices.shape[${p}]: ${e.shape[p]}.`);let a=r.shape[t],i=[],l=1,u=1,c=1;for(let p=0;p<o;++p)i.push(r.shape[p]),l*=r.shape[p];for(let p=o;p<t;p++)i.push(r.shape[p]),u*=r.shape[p];for(let p=o;p<n;p++)i.push(e.shape[p]);for(let p=t+1;p<s;p++)i.push(r.shape[p]),c*=r.shape[p];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:a,outputShape:i}}function Eq(r){try{return r.map(e=>Wc(e))}catch(e){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`)}}function Dq(r){return r.map(e=>ol(e))}var $r={};Ge($r,{nonMaxSuppressionV3Impl:()=>vg,nonMaxSuppressionV4Impl:()=>Cg,nonMaxSuppressionV5Impl:()=>Ig,whereImpl:()=>gg});function te(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var $q=$r.whereImpl,wu=class extends $s{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Va(this,Zo())}nextDataId(){return wu.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,W().get("IS_NODE")&&N.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 n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:o,refCount:1}),n}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return{dataId:n,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,o,n,s){this.data.set(e,{values:t,dtype:n,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:o}=this.data.get(e);if(t==="complex64"){let n=this.readSync(o.real.dataId),s=this.readSync(o.imag.dataId);return N.mergeRealAndImagArrays(n,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,o)}makeOutput(e,t,o){let n=this.write(e,t,o);return Zo().makeTensorFromDataId(n,t,o,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:o}=this.data.get(e);o!=null&&(this.disposeData(o.real.dataId,!0),this.disposeData(o.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=y.now();return e(),{kernelMs:y.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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c=N.computePool3DInfo(s.shape,a,i,1,l,u),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,_=c.effectiveFilterDepth,k=c.effectiveFilterHeight,D=c.effectiveFilterWidth,T=_-1-c.padInfo.front,R=D-1-c.padInfo.left,O=k-1-c.padInfo.top,M=ve(s.shape,"float32"),G=1/(d*h*g),j=t.bufferSync(n);for(let U=0;U<c.batchSize;++U)for(let H=0;H<c.inChannels;++H)for(let q=0;q<c.inDepth;++q)for(let X=0;X<c.inHeight;++X)for(let oe=0;oe<c.inWidth;++oe){let Y=q-T,re=X-O,J=oe-R,ie=0;for(let ue=0;ue<_;ue+=x){let ae=(Y+ue)/p;if(!(ae<0||ae>=c.outDepth||Math.floor(ae)!==ae))for(let fe=0;fe<k;fe+=b){let de=(re+fe)/m;if(!(de<0||de>=c.outHeight||Math.floor(de)!==de))for(let xe=0;xe<D;xe+=w){let we=(J+xe)/f;if(we<0||we>=c.outWidth||Math.floor(we)!==we)continue;ie+=j.get(U,ae,de,we,H)}}}M.set(ie*G,U,q,X,oe,H)}return t.makeTensorInfo(M.shape,M.dtype,M.values)}var cA={kernelName:hc,backendName:"cpu",kernelFunc:fK};function dK(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;te([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=x-1-c.padInfo.top,k=ve(a.shape,"float32"),D=1/(f*d),T=t.data.get(n.dataId).values,R=ve(n.shape,"float32",T);for(let O=0;O<c.batchSize;++O)for(let M=0;M<c.inChannels;++M)for(let G=0;G<c.inHeight;++G)for(let j=0;j<c.inWidth;++j){let U=G-_,H=j-w,q=0;for(let X=0;X<x;X+=h){let oe=(U+X)/p;if(!(oe<0||oe>=c.outHeight||Math.floor(oe)!==oe))for(let Y=0;Y<b;Y+=g){let re=(H+Y)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;q+=R.get(O,oe,re,M)}}k.set(q*D,O,G,j,M)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var pA={kernelName:dc,backendName:"cpu",kernelFunc:dK};function 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t.makeTensorInfo(n.shape,n.dtype,h)}var mA={kernelName:Pn,backendName:"cpu",kernelFunc:hK};function gK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;te([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=Qe({inputs:{x:n},backend:t,attrs:{shape:l}}),d=or({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Qe({inputs:{x:d},backend:t,attrs:{shape:c}}),g=un({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var fA={kernelName:ja,backendName:"cpu",kernelFunc:gK};function xK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,l=t.data.get(s.dataId).values,u=dp(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var dA={kernelName:gc,backendName:"cpu",kernelFunc:xK};var yK=$e(qo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),hA={kernelName:qo,backendName:"cpu",kernelFunc:yK};var bK=r=>{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];o[u]=Math.hypot(c,p)}return t.makeOutput(o,e.shape,"float32")},gA={kernelName:Ua,backendName:"cpu",kernelFunc:bK};function ni(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.imag,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}var xA={kernelName:Tc,backendName:"cpu",kernelFunc:ni};function ml(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(h=>y.sizeFromShape(h.shape)>0);if(i.length===1)return Rr({inputs:{x:i[0]},backend:t});let l=i.map(h=>h.shape);if(N.assertParamsConsistent(l,s),i[0].dtype==="complex64"){let h=i.map(_=>nn({inputs:{input:_},backend:t})),g=i.map(_=>ni({inputs:{input:_},backend:t})),x=ml({inputs:h,backend:t,attrs:{axis:s}}),b=ml({inputs:g,backend:t,attrs:{axis:s}}),w=fr({inputs:{real:x,imag:b},backend:t});return h.forEach(_=>t.disposeIntermediateTensorInfo(_)),g.forEach(_=>t.disposeIntermediateTensorInfo(_)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),w}let u=i.map(h=>{let g=y.sizeFromShape(h.shape.slice(s));return Qe({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=N.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=_u(c,a,e[0].dtype,p),f=N.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var yA={kernelName:Ps,backendName:"cpu",kernelFunc:ml};function P_(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o;te([n,s],"conv2d");let p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",_=new lt(m.outShape,n.dtype),k=y.computeStrides(n.shape),D=y.computeStrides(s.shape),T=k[0],R=w?k[1]:k[2],O=w?k[2]:1,M=w?1:k[1],G=_.strides[0],j=w?_.strides[1]:_.strides[2],U=w?_.strides[2]:1,H=w?1:_.strides[1],q=t.data.get(n.dataId).values,X=t.data.get(s.dataId).values,oe=_.values;for(let Y=0;Y<m.batchSize;++Y){let re=Y*T,J=Y*G;for(let ie=0;ie<m.outHeight;++ie){let ue=J+ie*j,ae=ie*m.strideHeight-b;for(let fe=0;fe<f;++fe){let de=ae+fe*h;if(de<0||de>=m.inHeight)continue;let xe=fe*D[0],we=re+de*R;for(let De=0;De<m.outWidth;++De){let Ne=ue+De*U,ze=De*m.strideWidth-x;for(let qe=0;qe<d;++qe){let it=ze+qe*g;if(it<0||it>=m.inWidth)continue;let St=xe+qe*D[1],Tt=we+it*O,Ue=St;for(let ut=0;ut<m.inChannels;++ut){let mt=q[Tt+ut*M];for(let Pt=0;Pt<m.outChannels;++Pt)oe[Ne+Pt*H]+=mt*X[Ue+Pt];Ue+=m.outChannels}}}}}}return t.makeTensorInfo(_.shape,_.dtype,oe)}var bA={kernelName:Sn,backendName:"cpu",kernelFunc:P_};function wK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o;te([n,s],"conv2dBackpropFilter");let p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,x=m.dataFormat==="channelsLast",b=new lt(m.filterShape,"float32"),w=m.padInfo.left,_=m.padInfo.top,k=t.data.get(n.dataId).values,D=t.data.get(s.dataId).values,T=new lt(n.shape,n.dtype,k),R=new lt(s.shape,s.dtype,D);for(let O=0;O<h;++O){let M=Math.max(0,Math.ceil((_-O)/f)),G=Math.min(m.outHeight,(m.inHeight+_-O)/f);for(let j=0;j<g;++j){let U=Math.max(0,Math.ceil((w-j)/d)),H=Math.min(m.outWidth,(m.inWidth+w-j)/d);for(let q=0;q<m.inChannels;++q)for(let X=0;X<m.outChannels;++X){let oe=0;for(let Y=0;Y<m.batchSize;++Y)for(let re=M;re<G;++re){let J=O+re*f-_;for(let ie=U;ie<H;++ie){let ue=j+ie*d-w;x?oe+=T.get(Y,J,ue,q)*R.get(Y,re,ie,X):oe+=T.get(Y,q,J,ue)*R.get(Y,X,re,ie)}}b.set(oe,O,j,q,X)}}}return t.makeTensorInfo(b.shape,b.dtype,b.values)}var wA={kernelName:yc,backendName:"cpu",kernelFunc:wK};function _K(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o;te([n,s],"conv2dBackpropInput");let p=y.computeStrides(s.shape),m=y.computeStrides(n.shape),f=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,f),h=new 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_A={kernelName:Tn,backendName:"cpu",kernelFunc:_K};function kK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o;te([n,s],"conv3d");let u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,x=g.front,b=g.left,w=g.top,_=new lt(u.outShape,n.dtype),k=t.data.get(n.dataId).values,D=t.data.get(s.dataId).values,T=_.values,R=y.computeStrides(n.shape),O=y.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let G=M*R[0],j=M*_.strides[0];for(let U=0;U<u.outDepth;++U){let H=j+U*_.strides[1],q=U*u.strideDepth-x;for(let X=0;X<c;++X){let oe=q+X*f;if(oe<0||oe>=u.inDepth)continue;let Y=X*O[0],re=G+oe*R[1];for(let J=0;J<u.outHeight;++J){let ie=H+J*_.strides[2],ue=J*u.strideHeight-w;for(let ae=0;ae<p;++ae){let fe=ue+ae*d;if(fe<0||fe>=u.inHeight)continue;let de=Y+ae*O[1],xe=re+fe*R[2];for(let we=0;we<u.outWidth;++we){let 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ue=Math.max(0,Math.ceil((Y-ie)/m)),ae=Math.min(p.outDepth,(p.inDepth+Y-ie)/m),fe=ie*_;for(let de=0;de<g;++de){let xe=Math.max(0,Math.ceil((J-de)/f)),we=Math.min(p.outHeight,(p.inHeight+J-de)/f),De=de*k+fe;for(let Ne=0;Ne<x;++Ne){let ze=Math.max(0,Math.ceil((re-Ne)/d)),qe=Math.min(p.outWidth,(p.inWidth+re-Ne)/d),it=Ne*D+De;for(let St=0;St<p.inChannels;++St){let Tt=St*T+it;for(let Ue=0;Ue<p.outChannels;++Ue){let ut=0;for(let mt=0;mt<p.batchSize;++mt){let Pt=mt*H,_o=mt*O;for(let Xt=ue;Xt<ae;++Xt){let Or=(ie+Xt*m-Y)*q+Pt,jo=Xt*M+_o;for(let nr=xe;nr<we;++nr){let jr=(de+nr*f-J)*X+Or,wr=nr*G+jo;for(let ao=ze;ao<qe;++ao){let $l=(Ne+ao*d-re)*oe+jr,Oa=ao*j+wr;ut+=U[$l+St]*R[Oa+Ue]}}}}w[Tt+Ue]=ut}}}}}return t.makeTensorInfo(b.shape,b.dtype,b.values)}var vA={kernelName:bc,backendName:"cpu",kernelFunc:vK};function CK(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o;te([n],"conv3dBackpropInputV2");let u=y.computeStrides(n.shape),c=y.computeStrides(s.shape),p=N.computeConv3DInfo(l,s.shape,i,1,a),m=new lt(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,b=t.data.get(n.dataId).values,[w,_,k,D]=u,T=t.data.get(s.dataId).values,[R,O,M,G]=c,{batchSize:j,filterDepth:U,filterHeight:H,filterWidth:q,inChannels:X,inDepth:oe,inHeight:Y,inWidth:re,outChannels:J,outDepth:ie,outHeight:ue,outWidth:ae,strideDepth:fe,strideHeight:de,strideWidth:xe}=p,we=U-1-p.padInfo.front,De=H-1-p.padInfo.top,Ne=q-1-p.padInfo.left;for(let ze=0;ze<j;++ze)for(let qe=0;qe<X;++qe)for(let it=0;it<oe;++it){let St=it-we,Tt=Math.max(0,Math.ceil(St/fe)),Ue=Math.min(ie,(U+St)/fe);for(let ut=0;ut<Y;++ut){let mt=ut-De,Pt=Math.max(0,Math.ceil(mt/de)),_o=Math.min(ue,(H+mt)/de);for(let Xt=0;Xt<re;++Xt){let io=Xt-Ne,Or=Math.max(0,Math.ceil(io/xe)),jo=Math.min(ae,(q+io)/xe),nr=0;for(let ko=Tt;ko<Ue;++ko){let jr=ko*fe-St;for(let wr=Pt;wr<_o;++wr){let ao=wr*de-mt;for(let $o=Or;$o<jo;++$o){let 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SA={kernelName:Ni,backendName:"cpu",kernelFunc:SK};function TK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;te(n,"cumsum");let l=N.getAxesPermutation([s],n.shape.length),u=n;l!=null&&(u=or({inputs:{x:n},backend:t,attrs:{perm:l}}));let c=N.getInnerMostAxes(1,n.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let p=ir(u.dtype,"int32"),m=y.makeZerosTypedArray(y.sizeFromShape(u.shape),p),f=t.data.get(u.dataId).values,d=u.shape[u.shape.length-1],h=i?(x,b)=>x+d-b-1:(x,b)=>x+b;for(let x=0;x<f.length;x+=d)for(let b=0;b<d;b++){let w=h(x,b);if(b===0)m[w]=a?0:f[w];else{let _=h(x,b-1);m[w]=a?f[_]+m[_]:f[w]+m[_]}}let g=t.makeTensorInfo(u.shape,p,m);if(l!=null){let x=N.getUndoAxesPermutation(l),b=or({inputs:{x:g},backend:t,attrs:{perm:x}});return t.disposeIntermediateTensorInfo(g),t.disposeIntermediateTensorInfo(u),b}return g}var TA={kernelName:En,backendName:"cpu",kernelFunc:TK};function AK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.data.get(n.dataId).values,u=t.data.get(s.dataId).values,c=dp(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=Ag(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var AA={kernelName:_c,backendName:"cpu",kernelFunc:AK};function EK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(a==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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DK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o;te([n,s],"depthwiseConv2dNativeBackpropFilter");let p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),{strideHeight:m,strideWidth:f,filterHeight:d,filterWidth:h}=p,g=new lt(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=p.outChannels/p.inChannels,_=t.data.get(n.dataId).values,k=new lt(n.shape,n.dtype,_),D=t.data.get(s.dataId).values,T=new lt(s.shape,s.dtype,D);for(let R=0;R<d;++R){let O=Math.max(0,Math.ceil((b-R)/m)),M=Math.min(p.outHeight,(p.inHeight+b-R)/m);for(let G=0;G<h;++G){let j=Math.max(0,Math.ceil((x-G)/f)),U=Math.min(p.outWidth,(p.inWidth+x-G)/f);for(let H=0;H<p.outChannels;++H){let q=Math.trunc(H/w),X=H%w,oe=0;for(let Y=0;Y<p.batchSize;++Y)for(let re=O;re<M;++re){let J=R+re*m-b;for(let ie=j;ie<U;++ie){let ue=G+ie*f-x;oe+=k.get(Y,J,ue,q)*T.get(Y,re,ie,H)}}g.set(oe,R,G,q,X)}}}return t.makeTensorInfo(g.shape,g.dtype,g.values)}var 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r!==2?!1:Bo(r).fenceSync!=null}function _s(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGL backend.`)})}var Pe=W();Pe.registerFlag("HAS_WEBGL",()=>Pe.getNumber("WEBGL_VERSION")>0);Pe.registerFlag("WEBGL_VERSION",()=>Kg(2)?2:Kg(1)?1:0);Pe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Pe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Pe.get("WEBGL_VERSION")===2);Pe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Pe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Pe.registerFlag("WEBGL_PACK",()=>Pe.getBool("HAS_WEBGL"));Pe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_CLIP",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Pe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_REDUCE",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_CONV_IM2COL",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>sk(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>ik(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Pe.getNumber("WEBGL_VERSION");return r===0?0:ak(r)});Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Pe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!jl.isMobile());Pe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>uk(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Pe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Pe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Pe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>ck(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>pk(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Pe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Pe.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Pe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>jl.isMobile()&&Pe.getBool("IS_CHROME")?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});function Rt(){let r,e,t,o,n,s,a,i,l,u;return W().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",o="in",n="texture",s="outputColor",a="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)));
|
|
}
|
|
`):(r="",e="attribute",t="varying",o="varying",n="texture2D",s="gl_FragColor",a="",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:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function ks(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function _p(r){let e=y.computeStrides(r).map(t=>t.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
|
|
}
|
|
`}var Xg=`
|
|
const float FLOAT_MAX = 1.70141184e38;
|
|
const float FLOAT_MIN = 1.17549435e-38;
|
|
|
|
lowp vec4 encode_float(highp float v) {
|
|
if (isnan(v)) {
|
|
return vec4(255, 255, 255, 255);
|
|
}
|
|
|
|
highp float av = abs(v);
|
|
|
|
if(av < FLOAT_MIN) {
|
|
return vec4(0.0, 0.0, 0.0, 0.0);
|
|
} else if(v > FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
|
|
} else if(v < -FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
|
|
}
|
|
|
|
highp vec4 c = vec4(0,0,0,0);
|
|
|
|
highp float e = floor(log2(av));
|
|
highp float m = exp2(fract(log2(av))) - 1.0;
|
|
|
|
c[2] = floor(128.0 * m);
|
|
m -= c[2] / 128.0;
|
|
c[1] = floor(32768.0 * m);
|
|
m -= c[1] / 32768.0;
|
|
c[0] = floor(8388608.0 * m);
|
|
|
|
highp float ebias = e + 127.0;
|
|
c[3] = floor(ebias / 2.0);
|
|
ebias -= c[3] * 2.0;
|
|
c[2] += floor(ebias) * 128.0;
|
|
|
|
c[3] += 128.0 * step(0.0, -v);
|
|
|
|
return c / 255.0;
|
|
}
|
|
`;var mk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fl.DENSE;let t=dl(e),o=Rt();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${ks(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${o.output} = result;
|
|
}
|
|
`}};var fk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fl.DENSE;let t=dl(e),o=Rt();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${ks(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${o.output} = result;
|
|
}
|
|
`}};var dk=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Fr.DOWNLOAD;let t=Rt();this.outputShape=e,this.userCode=`
|
|
${Xg}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}};var hk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Fr.DOWNLOAD;let t=Rt();this.outputShape=e,this.userCode=`
|
|
${Xg}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}};var gk=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Rt(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${_p(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / ${a};
|
|
int c = imod(flatIndex, ${a});
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.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];
|
|
}
|
|
|
|
${n.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}};var xk=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Rt(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${c} < ${e[2]}) {
|
|
localCoords[2] += ${c};
|
|
if(localCoords[1] + ${u} < ${e[1]}) {
|
|
localCoords[1] += ${u};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${a};
|
|
c = imod(flatIndex, ${a});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${p}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${p}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${p}] = values[2];
|
|
} else {
|
|
result[${p}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${_p(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${n.output} = ${l};
|
|
}
|
|
`}};var w2={};Ge(w2,{bindVertexProgramAttributeStreams:()=>Nk,createBufferFromOutputTexture:()=>Ak,createFloat16MatrixTexture:()=>kk,createFloat16PackedMatrixTexture:()=>Ik,createFloat32MatrixTexture:()=>_k,createIndexBuffer:()=>wk,createPackedMatrixTexture:()=>Ck,createUnsignedBytesMatrixTexture:()=>vk,createVertexBuffer:()=>bk,createVertexShader:()=>yk,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Dk,downloadFloat32MatrixFromBuffer:()=>Ek,downloadMatrixFromPackedOutputTexture:()=>Rk,downloadPackedMatrixFromBuffer:()=>$k,getInternalFormatForFloat16MatrixTexture:()=>Zg,getInternalFormatForFloat16PackedMatrixTexture:()=>ex,getInternalFormatForFloat32MatrixTexture:()=>Yg,getInternalFormatForPackedMatrixTexture:()=>Qg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Jg,uploadDenseMatrixToTexture:()=>Sk,uploadPixelDataToTexture:()=>Tk});function yk(r){let e=Rt(),t=`${e.version}
|
|
precision highp float;
|
|
${e.attribute} vec3 clipSpacePos;
|
|
${e.attribute} vec2 uv;
|
|
${e.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return H_(r,t)}function bk(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Y_(r,e)}function wk(r){let e=new Uint16Array([0,1,2,2,1,3]);return Z_(r,e)}function Sf(r,e,t,o,n,s){Q_(e,t);let a=J_(r),i=r.TEXTURE_2D;return Ie(r,()=>r.bindTexture(i,a)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ie(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function Yg(r){return r.internalFormatFloat}function _k(r,e,t,o){let[n,s]=Nu(e,t);return Sf(r,n,s,Yg(o),o.textureFormatFloat,r.FLOAT)}function Zg(r){return r.internalFormatHalfFloat}function kk(r,e,t,o){let[n,s]=Nu(e,t);return Sf(r,n,s,Zg(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Jg(r){return r.downloadTextureFormat}function vk(r,e,t,o){let[n,s]=Nu(e,t);return Sf(r,n,s,Jg(o),r.RGBA,r.UNSIGNED_BYTE)}function Qg(r){return r.internalFormatPackedFloat}function Ck(r,e,t,o){let[n,s]=si(e,t);return Sf(r,n,s,Qg(o),r.RGBA,r.FLOAT)}function ex(r){return r.internalFormatPackedHalfFloat}function Ik(r,e,t,o){let[n,s]=si(e,t);return Sf(r,n,s,ex(o),r.RGBA,o.textureTypeHalfFloat)}function Nk(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ie(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Wg(r,e,"clipSpacePos",t,3,s,o)&&Wg(r,e,"uv",t,2,s,n)}function Sk(r,e,t,o,n,s){Ie(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Tk(r,e,t){Ie(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Ak(r,e,t,o){let n=r.createBuffer();Ie(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ie(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ie(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ie(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function Ek(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function Dk(r,e,t,o){let[n,s]=Nu(e,t),a=4,i=new Uint8Array(f2(e*t,a));return Ie(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function $k(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(d2(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Rk(r,e,t){let o=new Float32Array(e*t*4);return Ie(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var tx=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=W().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,j_(t,e)):this.gl=Bo(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(W().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=bp(this.gl,s),So(this.gl,a))this.textureHalfFloatExtension=bp(this.gl,a);else if(W().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(o),So(this.gl,n))this.colorBufferHalfFloatExtension=bp(this.gl,n);else if(W().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(o="EXT_color_buffer_float",So(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(So(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=bk(this.gl),this.indexBuffer=wk(this.gl),this.framebuffer=ek(this.gl),this.textureConfig=vf(this.gl,this.textureHalfFloatExtension)}get debug(){return W().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(),_k(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),kk(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),vk(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Tk(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),Sk(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Ik(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Ck(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(jg(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>Dk(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return $k(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Ek(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=Ak(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(W().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>Rk(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl,o=q_(t,e),n=yk(t),s=K_(t);return Ie(t,()=>t.attachShader(s,n)),Ie(t,()=>t.attachShader(s,o)),X_(t,s),this.debug&&Cf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=Nk(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&&Cf(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?tk(this.gl,e,t):rk(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,o){this.throwIfDisposed(),this.throwIfNoProgram(),ok(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=si(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Cf(this.gl,this.program),wp(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=bp(this.gl,W().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(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=M5(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),If(this.gl,e,this.framebuffer),this.debug&&wp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(If(this.gl,this.outputTexture,this.framebuffer),this.debug&&wp(this.gl)):jg(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;If(n,e,this.framebuffer),this.debug&&wp(n),this.outputTexture=e,Ie(n,()=>n.viewport(0,0,t,o)),Ie(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,o,n))}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 M5(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:_2}=N;function k2(r,e,t,o){let n=[];r.forEach(d=>{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`))});let s=n.join(`
|
|
`),a=r.map(d=>L5(d,e,o)).join(`
|
|
`),i=e.texShape,l=Rt(),u=V5(l),c,p,m=j5(l);return e.isPacked?(c=z5(e.logicalShape,i),p=W5(l)):(c=B5(e.logicalShape,i),p=G5(l)),o&&(m+=U5),[m,u,p,s,c,a,t].join(`
|
|
`)}function kp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return H5(r);case 1:return q5(r);case 2:return K5(r);case 3:return X5(r);case 4:return Y5(r);case 5:return Z5(r);case 6:return J5(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function v2(r){switch(r.shapeInfo.logicalShape.length){case 0:return Q5(r);case 1:return eX(r);case 2:return tX(r);case 3:return rX(r);default:return oX(r)}}function L5(r,e,t=!1){let o="";t?o+=v2(r):o+=kp(r);let n=r.shapeInfo.logicalShape,s=e.logicalShape;return n.length<=s.length&&(t?o+=nX(r,e):o+=sX(r,e)),o}function z5(r,e){switch(r.length){case 0:return C2();case 1:return iX(r,e);case 2:return uX(r,e);case 3:return aX(r,e);default:return lX(r,e)}}function B5(r,e){switch(r.length){case 0:return C2();case 1:return cX(r,e);case 2:return hX(r,e);case 3:return pX(r,e);case 4:return mX(r,e);case 5:return fX(r,e);case 6:return dX(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function V5(r){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${r.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function G5(r){return`
|
|
void setOutput(float val) {
|
|
${r.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function W5(r){return`
|
|
void setOutput(vec4 val) {
|
|
${r.output} = val;
|
|
}
|
|
`}function j5(r){return`${r.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${r.varyingFs} vec2 resultUV;
|
|
${r.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${r.defineSpecialNaN}
|
|
${r.defineSpecialInf}
|
|
${r.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${gX}
|
|
${xX}
|
|
${yX}
|
|
`}var gX=`
|
|
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);
|
|
}
|
|
`,xX=`
|
|
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);
|
|
}
|
|
`,yX=`
|
|
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);
|
|
}
|
|
`,U5=`
|
|
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 C2(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function iX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
}
|
|
`}function cX(r,e){return e[0]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${e[1]}.0);
|
|
}
|
|
`:e[1]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${e[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
return resTexRC.x * ${e[1]} + resTexRC.y;
|
|
}
|
|
`}function aX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
int b = index / ${n};
|
|
index -= b * ${n};
|
|
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function pX(r,e){let t=ks(["r","c","d"],r);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
${t}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function lX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
|
|
int b${l} = index / ${s};
|
|
index -= b${l} * ${s};
|
|
`+a,i=`b${l}, `+i;return`
|
|
ivec${r.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${a}
|
|
|
|
int b = index / ${n};
|
|
index -= b * ${n};
|
|
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec${r.length}(${i});
|
|
}
|
|
`}function mX(r,e){let t=ks(["r","c","d","d2"],r);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
${t}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function fX(r,e){let t=ks(["r","c","d","d2","d3"],r);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
|
|
${e[1]}));
|
|
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
|
|
${t}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function dX(r,e){let t=ks(["r","c","d","d2","d3","d4"],r);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
|
|
${t}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function uX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`;let o=Math.ceil(r[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function hX(r,e){return y.arraysEqual(r,e)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
|
|
}
|
|
`:r[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:r[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
int r = index / ${r[1]};
|
|
int c = index - r * ${r[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Su(r){return`offset${r}`}function Q5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Rt();return`
|
|
vec4 ${t}() {
|
|
return ${o.texture2D}(${e}, halfCR);
|
|
}
|
|
`}function H5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
|
|
float ${t}() {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let[s,a]=r.shapeInfo.texShape,i=Su(e);return`
|
|
float ${t}() {
|
|
vec2 uv = uvFromFlat(${s}, ${a}, ${i});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function eX(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Rt();return`
|
|
vec4 ${t}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${n[0]}, ${n[1]}, index);
|
|
return ${s.texture2D}(${e}, uv);
|
|
}
|
|
`}function q5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
|
|
float ${t}(int index) {
|
|
${vp(r)}
|
|
}
|
|
`;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
|
|
float ${t}(int index) {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let a=Su(e);return s===1?`
|
|
float ${t}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:n===1?`
|
|
float ${t}(int index) {
|
|
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:`
|
|
float ${t}(int index) {
|
|
vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function tX(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Rt();if(n!=null&&y.arraysEqual(e,n))return`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
|
|
|
|
return ${i.texture2D}(${t}, uv);
|
|
}
|
|
`;let l=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],u=Math.ceil(e[1]/2);return`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${i.texture2D}(${t}, uv);
|
|
}
|
|
`}function K5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&y.arraysEqual(e,n)){let p=n[0],m=n[1];return`
|
|
float ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=Cp(r,i),m=["row","col"];return`
|
|
${kp(p)}
|
|
float ${o}(int row, int col) {
|
|
return ${o}(${Ip(m,a)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
|
|
${vp(r)}
|
|
}
|
|
`;let l=n[0],u=n[1],c=Su(t);return u===1?`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:l===1?`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${e[1]} + col + ${c};
|
|
vec2 uv = uvFromFlat(${l}, ${u}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function rX(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Cp(r,p),d=["b","row","col"];return`
|
|
${v2(f)}
|
|
vec4 ${o}(int b, int row, int col) {
|
|
return ${o}(${Ip(d,m)});
|
|
}
|
|
`}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Rt();return`
|
|
vec4 ${o}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${a}, ${i}, ${u}, ${l}, b, row, col);
|
|
return ${c.texture2D}(${t}, uv);
|
|
}
|
|
`}function X5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=Cp(r,l),h=["row","col","depth"];return`
|
|
${kp(d)}
|
|
float ${o}(int row, int col, int depth) {
|
|
return ${o}(${Ip(h,i)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${n}, ${s}, 1)));
|
|
${vp(r)}
|
|
}
|
|
`;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${c}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(p===s&&m==null)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let f=Su(t);return`
|
|
float ${o}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n} + col * ${s} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${c}, ${p}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function oX(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Rt();return`
|
|
vec4 ${n}(${p}) {
|
|
int index = ${m};
|
|
int texR = index / ${l};
|
|
int texC = index - texR * ${l};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
|
|
return ${f.texture2D}(${o}, uv);
|
|
}
|
|
`}function Y5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=Cp(r,i),h=["row","col","depth","depth2"];return`
|
|
${kp(d)}
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
return ${o}(${Ip(h,l)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${a}, ${s}, ${n}, 1)));
|
|
${vp(r)}
|
|
}
|
|
`;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${n}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(m===n&&u==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${e[1]*e[2]}, ${e[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let f=Su(t);return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${s} +
|
|
depth * ${n} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function Z5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=Cp(r,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${kp(h)}
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${o}(${Ip(g,u)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${a}, ${s}, ${n})) +
|
|
depth3;
|
|
${vp(r)}
|
|
}
|
|
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${a}, ${s}, ${n}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(f===n&&c==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${e[1]*e[2]*e[3]},
|
|
${e[2]*e[3]}, ${e[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let d=Su(t);return`
|
|
float ${o}(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 * ${a} + depth * ${s} +
|
|
depth2 * ${n} + depth3 + ${d};
|
|
vec2 uv = uvFromFlat(${m}, ${f}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function J5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=Cp(r,n),x=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${kp(g)}
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${o}(${Ip(x,s)});
|
|
}
|
|
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${a}, 1)));
|
|
${vp(r)}
|
|
}
|
|
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${i}, ${a})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${f}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(d===a&&p==null)return`
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${e[1]*e[2]*e[3]*e[4]},
|
|
${e[2]*e[3]*e[4]},
|
|
${e[3]*e[4]},
|
|
${e[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${f}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let h=Su(t);return`
|
|
float ${o}(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 * ${a} + depth4 + ${h};
|
|
vec2 uv = uvFromFlat(${f}, ${d}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function vp(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
|
|
for (int i = 0; i < ${t}; i++) {
|
|
if (i == index) {
|
|
return ${e}[i];
|
|
}
|
|
}
|
|
`}function nX(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=_2(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
|
|
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(h&&!x)a===1?f=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:f=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let b=s-2,w=s-1;i.indexOf(b)>-1&&i.indexOf(w)>-1?f="return vec4(outputValue.x);":i.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${n}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${o}(${m});
|
|
${f}
|
|
}
|
|
`}function sX(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
|
|
float ${n}() {
|
|
return sampleTexture(${t}, resultUV);
|
|
}
|
|
`;let u=Le(l),c=_2(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
|
|
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
|
|
float ${n}() {
|
|
${u} coords = getOutputCoords();
|
|
${m}
|
|
return get${o}(${d});
|
|
}
|
|
`}function Le(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Cp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Ip(r,e){return e.map(t=>r[t]).join(", ")}function I2(r,e,t,o){let n=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},l=k2(s,i,n,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);W().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function N2(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function S2(r,e,t,o,n){N2(e.inShapeInfos,t),N2([e.outShapeInfo],[o]);let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),W().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(y.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),n!=null&&n(r,e.webGLProgram),r.executeProgram()}function T2(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${l}_${i}`});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n,s}var{addImpl:A2,bincountImpl:rx,bincountReduceImpl:E2,ceilImpl:D2,concatImpl:$2,expImpl:R2,expm1Impl:F2,floorImpl:O2,gatherV2Impl:P2,greaterImpl:M2,lessImpl:L2,linSpaceImpl:z2,logImpl:B2,maxImpl:V2,maximumImpl:G2,minimumImpl:W2,multiplyImpl:j2,negImpl:U2,prodImpl:H2,rangeImpl:q2,rsqrtImpl:K2,simpleAbsImpl:ox,sliceImpl:X2,stridedSliceImpl:Y2,subImpl:Z2,tileImpl:J2,topKImpl:Q2,transposeImpl:Tu,uniqueImpl:eD}=Mg;function Fk(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Wt(r,e){return e===1?[r]:Fk(r,e)}function tD(r,e){if(r===1)return"rc";let t="";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=",");return t}var Ok=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 o=Wt("rc",t),n=Le(t),s=bX(t,e,o),a=wX(t,e[e.length-1],e[e.length-2],o),i=_X(e,o);this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
|
|
if(${s}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function kX(r,e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function bX(r,e,t){if(r===1)return`rc > ${e[0]}`;let o="";for(let n=r-2;n<r;n++)o+=`${t[n]} >= ${e[n]}`,n<r-1&&(o+="||");return o}function wX(r,e,t,o){if(r===1)return"";let n=o.slice(-2);return`
|
|
int r = ${n[0]};
|
|
int c = ${n[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${e};
|
|
bool rEdge = rp1 >= ${t};
|
|
`}function _X(r,e){let t=r.length,o=kX(t,e);return t===1?`getA(rc),
|
|
rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${o[0]}),
|
|
cEdge ? 0. : getA(${o[1]}),
|
|
rEdge ? 0. : getA(${o[2]}),
|
|
rEdge || cEdge ? 0. : getA(${o[3]})`}var Tf=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=`
|
|
${s}
|
|
${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${n}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${n>0?"}":""}
|
|
`}this.userCode=`
|
|
${vX(t)}
|
|
${_p(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${o}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function vX(r){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${ks(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var Pk=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=oD(t,o),s=nD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=rD(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===vr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===vr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===vr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===vr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===vr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=oD(o,n),a=nD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=rD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function CX(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function rD(r,e,t,o,n){let s=IX(e,o),a;if(n){let[l,u]=si(r[0],r[1]);a=l*u}else{let[l,u]=Nu(r[0],r[1]);a=l*u}let i=CX(t,s);return a*i}function IX(r,e){switch(r){case vr.PACKED_2X2_FLOAT32:return Qg(e);case vr.PACKED_2X2_FLOAT16:return ex(e);case vr.UNPACKED_FLOAT32:return Yg(e);case vr.UNPACKED_FLOAT16:return Zg(e);case vr.PACKED_4X1_UNSIGNED_BYTE:return Jg(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function NX(r){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?vr.PACKED_2X2_FLOAT32:vr.UNPACKED_FLOAT32:r?vr.PACKED_2X2_FLOAT16:vr.UNPACKED_FLOAT16}function oD(r,e){if(r===Fr.UPLOAD)return vr.PACKED_2X2_FLOAT32;if(r===Fr.RENDER||r==null)return NX(e);if(r===Fr.DOWNLOAD||r===Fr.PIXELS)return vr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function nD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var mo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},xr="if (isnan(x)) return x;",sD="return x;",Mk="return abs(x);";var iD="return (x >= 0.0) ? x : (exp(x) - 1.0);",aD=xr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,lD=xr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Af="return x;";var uD="return x;",cD=`
|
|
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;
|
|
`,pD=`
|
|
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;
|
|
`,mD=`
|
|
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;
|
|
`,vs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}};var Lk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Wt("rc",t),n=Le(t),s=tD(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${s});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}};var SX=$r.whereImpl,TX=1e-7,AX=1e-4,nx={};function EX(r){return r in nx||(nx[r]={}),nx[r]}var DX=128,$X=600;function RX(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*$X/1024/1024}var Au=class extends $s{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.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Bo(W().getNumber("WEBGL_VERSION"));this.binaryCache=EX(W().getNumber("WEBGL_VERSION")),this.gpgpu=new tx(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 Pk(this.gpgpu),this.numMBBeforeWarning=RX(),this.texData=new Va(this,Zo())}nextDataId(){return Au.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Fr.UPLOAD,refCount:1}),n}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,o,n,s){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Fr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new vs(i,Af):m=new mo(i,Af);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new vs(n,Af):d=new mo(n,Af);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...dl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Zo().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!U_(o))throw W().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...dl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=W().getBool("WEBGL_PACK")&&n===!0,i=a?Nf(t):t,l=a?new hk(i):new dk(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(W().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:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return W().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Zo().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=DX){let o=this.getCPUBackend();return!W().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return SX(e.shape,t)}packedUnaryOp(e,t,o){let n=new vs(e.shape,t),s=this.compileAndRun(n,[e],o);return Zo().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=ox(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Mk,e.dtype);let t=new mo(e.shape,Mk),o=this.compileAndRun(t,[e]);return Zo().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return Zo().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Lk(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Ok(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Ca(e.shape),...Ia(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Ca(t),...Ia(t)],a=new Tf(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Nf(n),i;o?i=new fk(a):i=new mk(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===fl.DENSE){let g=dl(e.outputShape);i.texShape=g.map(x=>x*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(g.dataId);if(x.texture==null){if(!e.packedInputs&&y.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=g.shape)}else if(!!x.isPacked!=!!e.packedInputs)g=x.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),x=this.texData.get(g.dataId);else if(x.isPacked&&!hl(x.shape,g.shape)){let b=g,w=g.shape;g.shape=x.shape,g=this.packedReshape(g,w),l.push(g),x=this.texData.get(g.dataId),b.shape=w}return this.uploadToGPU(g.dataId),{shape:g.shape,texData:x,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=T2(e,u,c),m=this.getAndSaveBinary(p,()=>I2(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),S2(this.gpgpu,m,u,c,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=W().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=y.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().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=V(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?TX:AX}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=nk(o,l),t.texShape=p),s!=null){let m=Nf(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=si(p[0],p[1]),f=new xk(m,[h,d],g)):f=new gk(m,[h,d],g);let x=this.makeTensorInfo([h,d],n);g?this.texData.get(x.dataId).usage=Fr.PIXELS:this.texData.get(x.dataId).usage=Fr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,w=this.runWebGLProgram(f,[x],n,null,b),_=this.texData.get(w.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(w.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=FX(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};Au.nextDataId=0;function FX(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var zk="3.3.0";function fD(){W().set("WEBGL_FORCE_F16_TEXTURES",!0)}jl.isBrowser()&&Xc("webgl",()=>new Au,2);var kXe={forceHalfFloat:fD};var sx=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`;var cn=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}};var gl=`
|
|
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;
|
|
`;var Cs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${Le(s)} coords = getOutputCoords();
|
|
`,s===1)a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let l=Wt("coords",s);a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${l[s-2]} + 1) >= ${this.outputShape[s-2]};
|
|
bool nextColOutOfBounds =
|
|
(${l[s-1]} + 1) >= ${this.outputShape[s-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var dD={kernelName:Ko,backendName:"webgl",kernelFunc:jt};function fo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=jt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var hD={kernelName:xc,backendName:"webgl",kernelFunc:fo};var Bk="return (a < 0.) ? b * a : a;",Vk=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function OX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cs(Vk,n.shape,a.shape):new cn(Bk,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var gD={kernelName:Ln,backendName:"webgl",kernelFunc:OX};var Gk="return (a < 0.) ? b * a : a;",Wk=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function PX(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cs(Wk,o.shape,n.shape):new cn(Gk,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var xD={kernelName:Yn,backendName:"webgl",kernelFunc:PX};var ix="if (isnan(x)) return x;",yD=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,bD=`
|
|
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 _e({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new vs(a.shape,e):c=new mo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,k]=w,D={dataId:_.dataId,dtype:_.dtype,shape:l.shape},T={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new cn(r,l.shape,u.shape);return c.runWebGLProgram(R,[D,T],ir(_.dtype,k.dtype))}),b=fo({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ir(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Cs(e,l.shape,u.shape,t):f=new cn(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function xl(r,e=!1){if(r==="linear")return e?uD:sD;if(r==="relu")return e?pD:aD;if(r==="elu")return e?cD:iD;if(r==="relu6")return e?mD:lD;if(r==="prelu")return e?Wk:Gk;if(r==="leakyrelu")return e?Vk:Bk;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Ef=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:u?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:g=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${g}
|
|
|
|
const float sharedDimension = ${p}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${p}; i++) {
|
|
int batchA = ${w};
|
|
int batchB = ${_};
|
|
vec4 a = getMatrixA(batchA, ${m});
|
|
vec4 b = getMatrixB(batchB, ${f});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${d[0]} * ${h[0]});
|
|
result += (${d[1]} * ${h[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${b}
|
|
|
|
${x}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};var jk={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},ax=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}};var wD="return a * b;";function Uk(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=N.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new ax(jk.REAL,o.shape,n.shape),c=new ax(jk.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=fo({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=j2(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Cs(wD,o.shape,n.shape):a=new cn(wD,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var _D={kernelName:Hn,backendName:"webgl",kernelFunc:Uk};function kD(r,e,t){let o=[Ca(r.shape),...Ia(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Ca(e),...Ia(e)],a=new Tf(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!hl(n.shape,l)&&!(c.texture!==null&&hl(c.shape,l))?kD(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var vD={kernelName:Gs,backendName:"webgl",kernelFunc:pe};var lx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${o};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${u}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${l===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${l===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${l===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${u}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}};var Hk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
|
|
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 = ${l}(values, minMaxValue);
|
|
}
|
|
`,f="vec4";t==="all"?(i="1.0",m=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,f="bvec4"):t==="any"&&(i="0.0",m=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,f="bvec4");let d="";s%o>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${o};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${m}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${p===1}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===2}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===3}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function MX(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function To(r,e,t,o){let n=MX(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new lx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new lx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Hk({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var qk=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=LX(t);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function LX(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var Kk=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=Fk("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${u};
|
|
if(${l}) {
|
|
result[1] = ${u};
|
|
}
|
|
--${s[this.rank-1]};
|
|
if(++${s[this.rank-2]} < ${o[this.rank-2]}) {
|
|
result[2] = ${u};
|
|
if(${l}) {
|
|
result[3] = ${u};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function yl(r,e,t){let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kk(r.shape,e):new qk(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function CD(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=yl(r,l,o),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=pe({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=Gl(r.dtype),w=To(x,b,"sum",o),_=pe({inputs:{x:w},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(w),u&&o.disposeIntermediateTensorInfo(c),_}function Df(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return CD(n,s,a,t)}var ID={kernelName:is,backendName:"webgl",kernelFunc:Df};function Mt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=Tu(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=yl(n,s,a);return u}var ND={kernelName:ps,backendName:"webgl",kernelFunc:Mt};var Xk=1e3;function Eu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=x===b||x===1||b===1;y.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let k=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let D=t?[x,p,f]:[x,f,p],T=o?[b,d,m]:[b,m,d],R=pe({inputs:{x:r},backend:n,attrs:{shape:D}}),O=pe({inputs:{x:e},backend:n,attrs:{shape:T}}),M=[R,O],G=Math.max(x,b),j=t?R.shape[1]:R.shape[2],U=s!=null,H=a!=null,q=l==="leakyrelu",X=l!=null?xl(l,!0):null,oe=U||H||q||X!=null,Y;if((f===1||d===1)&&j>Xk&&oe===!1){let J=R,ie=O;t&&(J=Mt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),M.push(J)),o&&(ie=Mt({inputs:{x:O},backend:n,attrs:{perm:[0,2,1]}}),M.push(ie));let ue=d!==1,ae=d===1,fe=J;ue&&(fe=pe({inputs:{x:J},backend:n,attrs:{shape:[G,j,1]}}),M.push(fe));let de=d===1?2:1,xe=ie;ae&&(xe=pe({inputs:{x:ie},backend:n,attrs:{shape:[G,1,j]}}),M.push(xe));let we=Uk({inputs:{a:fe,b:xe},backend:n});Y=Df({inputs:{x:we},backend:n,attrs:{axis:de,keepDims:!0}}),M.push(we)}else{let J=ir(r.dtype,e.dtype),ie=new Ef(D,T,[G,f,d],t,o,U,X,H,q),ue=[R,O];if(s!=null&&ue.push(s),H&&ue.push(a),q){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));ue.push(ae),M.push(ae)}Y=n.runWebGLProgram(ie,ue,J)}let re=pe({inputs:{x:Y},backend:n,attrs:{shape:k}});M.push(Y);for(let J of M)n.disposeIntermediateTensorInfo(J);return re}function zX(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return Eu({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var SD={kernelName:Ks,backendName:"webgl",kernelFunc:zX};var TD="return abs(x);";function BX(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=ox(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new vs(o.shape,TD):n=new mo(o.shape,TD),t.runWebGLProgram(n,[o],o.dtype)}var AD={kernelName:Os,backendName:"webgl",kernelFunc:BX};var VX=xr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,GX=_e({opSnippet:VX}),ED={kernelName:yi,backendName:"webgl",kernelFunc:GX};var WX=xr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,backendName:"webgl",kernelFunc:jX};var $D="return a + b;",UX=nt({opSnippet:$D,packedOpSnippet:$D,supportsComplex:!0,cpuKernelImpl:A2}),RD={kernelName:Fo,backendName:"webgl",kernelFunc:UX};var Yk=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${o.join(`
|
|
`)}
|
|
|
|
float result = ${n};
|
|
setOutput(result);
|
|
}
|
|
`}};var Zk=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${o.join(`
|
|
`)}
|
|
|
|
vec4 result = ${n};
|
|
setOutput(result);
|
|
}
|
|
`}};function ux(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=ux({inputs:o.slice(0,l),backend:t}),c=ux({inputs:o.slice(l),backend:t});return ux({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>ir(l,u)),s=o.map(l=>l.shape),i=W().getBool("WEBGL_PACK")?new Zk(o[0].shape,s):new Yk(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var FD={kernelName:kn,backendName:"webgl",kernelFunc:ux};function HX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var OD={kernelName:mc,backendName:"webgl",kernelFunc:HX};function qX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var PD={kernelName:fc,backendName:"webgl",kernelFunc:qX};var Jk=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"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 * ${n};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${n}; i++) {
|
|
int inIdx = ${l};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}};var Qk=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Wt("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=`
|
|
${R} sourceLocR = ${R}(${c.join()}, 0);
|
|
++${c[l-1]};
|
|
${R} sourceLocG = ${R}(${c.join()}, 0);
|
|
++${c[l-2]};
|
|
${R} sourceLocA = ${R}(${c.join()}, 0);
|
|
--${c[l-1]};
|
|
${R} sourceLocB = ${R}(${c.join()}, 0);
|
|
--${c[l-2]};`}else m=l,p=`
|
|
${u} sourceLocR = coords;
|
|
++${c[l-1]};
|
|
${u} sourceLocG = coords;
|
|
++${c[l-2]};
|
|
${u} sourceLocA = coords;
|
|
--${c[l-1]};
|
|
${u} sourceLocB = coords;
|
|
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Wt("sourceLocR",m-1).concat("inIdx.r"),x=Wt("sourceLocG",m-1).concat("inIdx.g"),b=Wt("sourceLocB",m-1).concat("inIdx.b"),w=Wt("sourceLocA",m-1).concat("inIdx.a"),_=o==="max"?"greaterThan":"lessThan",k=n?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${x.join()}),
|
|
getBestIndicesAChannel(${b.join()}),
|
|
getBestIndicesAChannel(${w.join()})));`,D=`vec4(
|
|
getAChannel(${g.join()}),
|
|
hasNextCol ? getAChannel(${x.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${b.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,T=n?"":`
|
|
float getBestIndicesAChannel(${h.join()}) {
|
|
return getChannel(getBestIndicesA(${f.join()}),
|
|
vec2(${f.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${h.join()}) {
|
|
return getChannel(getA(${f.join()}),
|
|
vec2(${f.slice(-2).join()}));
|
|
}
|
|
${T}
|
|
void main() {
|
|
${u} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
|
|
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
|
|
${p}
|
|
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
|
|
sourceLocB${d}, sourceLocA${d}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${D};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${k}
|
|
vec4 candidate = ${D};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${_}(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 MD(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Jk(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=MD(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function LD(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Qk(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=LD(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function cx(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=pe({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=MD(r,u,o);s.push(c);let p=pe({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return LD(r,e,o)}function KX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Mt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=cx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var zD={kernelName:vn,backendName:"webgl",kernelFunc:KX};function XX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Mt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=cx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var BD={kernelName:Ga,backendName:"webgl",kernelFunc:XX};var YX=xr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,ZX=_e({opSnippet:YX}),VD={kernelName:wi,backendName:"webgl",kernelFunc:ZX};var JX=xr+"return log(x + sqrt(x * x + 1.0));",QX=_e({opSnippet:JX}),GD={kernelName:_i,backendName:"webgl",kernelFunc:QX};var e8=xr+`
|
|
return atan(x);
|
|
`,t8=_e({opSnippet:e8}),WD={kernelName:ki,backendName:"webgl",kernelFunc:t8};var r8=yD+`
|
|
return atan(a, b);
|
|
`,o8=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+bD+`
|
|
return result;
|
|
`,n8=nt({opSnippet:r8,packedOpSnippet:o8}),jD={kernelName:Ci,backendName:"webgl",kernelFunc:n8};var s8=xr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelName:vi,backendName:"webgl",kernelFunc:i8};var ii=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${f}, ${d});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m};
|
|
wC += ${c}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${R} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let k=Math.floor(a/4)*4,D=a%4,T=`
|
|
if (${h}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${w}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${f}, ${d});
|
|
const float initializationValue = ${b};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${b});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${k}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${T}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${D===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${D===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${D===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
}
|
|
}
|
|
setOutput(${_});
|
|
}
|
|
`}},Du=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),o){let M=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${l}, ${u});
|
|
const ivec3 pads = ivec3(${g}, ${x}, ${b});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${f};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h};
|
|
wC += ${m}) {
|
|
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 ${M} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
|
|
wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let k="max",D=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(D="avgValue / count");let T=Math.floor(a/4)*4,R=a%4,O=`
|
|
if (${w}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${k}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${l}, ${u});
|
|
const ivec3 pads = ivec3(${g}, ${x}, ${b});
|
|
const float initializationValue = ${_};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${_});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${f};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${T}; wC += 4) {
|
|
int xC = xCCorner + wC * ${m};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
|
|
);
|
|
|
|
${O}
|
|
}
|
|
|
|
int xC = xCCorner + ${T};
|
|
if (${R===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${O}
|
|
} else if (${R===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${O}
|
|
} else if (${R===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${O}
|
|
}
|
|
}
|
|
setOutput(${D});
|
|
}
|
|
}
|
|
`}};function a8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;_s(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new ii(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var HD={kernelName:Cn,backendName:"webgl",kernelFunc:a8};function l8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new Du(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var qD={kernelName:Wa,backendName:"webgl",kernelFunc:l8};var ev=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${p});
|
|
const float avgMultiplier = float(${m});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${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+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},tv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`
|
|
const ivec3 pads = ivec3(${d}, ${h}, ${g});
|
|
const float avgMultiplier = float(${x});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${l}) {
|
|
float dyD = float(dyDCorner + wD) / ${s}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${m};
|
|
wR += ${u}) {
|
|
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 < ${f};
|
|
wC += ${c}) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function u8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new tv(m);return t.runWebGLProgram(f,[n],a.dtype)}var KD={kernelName:hc,backendName:"webgl",kernelFunc:u8};function c8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;_s([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new ev(c);return t.runWebGLProgram(p,[n],a.dtype)}var XD={kernelName:dc,backendName:"webgl",kernelFunc:c8};function p8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Eu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var YD={kernelName:In,backendName:"webgl",kernelFunc:p8};var rv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${l};
|
|
float inv = scale * inversesqrt(variance + float(${a}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}};var ov=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${l};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}};var m8=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=W().getBool("WEBGL_PACK_NORMALIZATION")?new ov(o.shape,n.shape,s.shape,c,p,l):new rv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},ZD={kernelName:Pn,backendName:"webgl",kernelFunc:m8};var nv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=f8(this.rank),s,a=e.map((i,l)=>`sourceLoc.${sv[l]} = start[${l}] + coords.${sv[l]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${a.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${o}
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},sv=["x","y","z","w","u","v"];function f8(r){if(r===1)return"sourceLoc";if(r<=6)return sv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var iv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Wt("coords",this.rank),n=Wt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
|
|
result.x = ${a};
|
|
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${n[this.rank-1]};
|
|
result.y = ${a};
|
|
--${n[this.rank-1]};
|
|
}
|
|
`,l=this.rank===1?"":`
|
|
--${o[this.rank-1]};
|
|
if (++${o[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${n[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${n[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,u=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${p}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${u}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${l}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function d8(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=er.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Na(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=er.parseSliceParams(n,s,a);if(er.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=X2(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=er.isSliceContinous(n.shape,i,l);if(u||!c){let p=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iv(l):new nv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),d8(n,i,l,t)}var JD={kernelName:js,backendName:"webgl",kernelFunc:Na};var h8=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=pe({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Mt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Na({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},QD={kernelName:ja,backendName:"webgl",kernelFunc:h8};function g8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=rx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var e$={kernelName:gc,backendName:"webgl",kernelFunc:g8};var x8="return float(a != b);",av=nt({opSnippet:x8,dtype:"bool"}),t$={kernelName:Wi,backendName:"webgl",kernelFunc:av};function Sa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var r$={kernelName:Oc,backendName:"webgl",kernelFunc:Sa};var y8="return float(int(x));";function o$(r,e){let t=new mo(r.shape,y8),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=ht(n.shape),i=lv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=fo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=Sa({inputs:{input:n},backend:t}),i=lv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return o$(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=av({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var n$={kernelName:Ho,backendName:"webgl",kernelFunc:lv};var s$="return ceil(x);",b8=_e({opSnippet:s$,packedOpSnippet:s$,cpuKernelImpl:D2}),i$={kernelName:Nn,backendName:"webgl",kernelFunc:b8};var uv=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var cv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function w8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;W().getBool("WEBGL_PACK_CLIP")?i=new cv(n.shape):i=new uv(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var a$={kernelName:qo,backendName:"webgl",kernelFunc:w8};var pv=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float re = abs(getRealAtOutCoords());
|
|
float im = abs(getImagAtOutCoords());
|
|
float mx = max(re, im);
|
|
|
|
// sadly the length function in glsl is not underflow-safe
|
|
// (at least not on Intel GPUs). So the safe solution is
|
|
// to ensure underflow-safety in all cases.
|
|
setOutput(
|
|
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
|
|
);
|
|
}
|
|
`}};function l$(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function _8(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new pv(o.shape),a=[l$(o,n.complexTensorInfos.real),l$(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var u$={kernelName:Ua,backendName:"webgl",kernelFunc:_8};var mv=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);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 o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${o.join(`
|
|
`)}
|
|
}
|
|
`}};var fv=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=Wt("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
|
|
return getChannel(
|
|
getT0(${p}), vec2(${c.join()}));
|
|
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
|
|
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
|
|
return getChannel(
|
|
getT${h}(${px(i,u,g)}),
|
|
vec2(${px(c,u,g)}));
|
|
}`}let f=l.length,d=l[l.length-1];m+=`
|
|
return getChannel(
|
|
getT${f}(${px(i,u,d)}),
|
|
vec2(${px(c,u,d)}));`,this.userCode=`
|
|
float getValue(${i.map(h=>"int "+h)}) {
|
|
${m}
|
|
}
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[n-1]} = ${a[n-1]} + 1;
|
|
if (${a[n-1]} < ${o[n-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[n-2]} = ${a[n-2]} + 1;
|
|
if (${a[n-2]} < ${o[n-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[n-1]} = ${a[n-1]} - 1;
|
|
if (${a[n-2]} < ${o[n-2]} &&
|
|
${a[n-1]} < ${o[n-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function px(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function $u(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var c$={kernelName:Tc,backendName:"webgl",kernelFunc:$u};function Ru(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>Sa({inputs:{input:d},backend:t})),c=r.map(d=>$u({inputs:{input:d},backend:t})),p=Ru(u,e,t),m=Ru(c,e,t),f=fo({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=p$(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=$2(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=Ru(r.slice(0,u),e,t),p=Ru(r.slice(u),e,t),m=Ru([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new fv(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=p$(r,e,t),a=new mv(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=pe({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function p$(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function dv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),Ru(i,s,t)}var m$={kernelName:Ps,backendName:"webgl",kernelFunc:dv};var $f=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,_="",k="";o&&(n?_=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:s?_=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:_=`
|
|
float activation(float x) {
|
|
${o}
|
|
}
|
|
`,k="result = activation(result);");let D=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${_}
|
|
|
|
const ivec2 strides = ivec2(${l}, ${u});
|
|
const ivec2 pads = ivec2(${a}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${w}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${x}], coords[${b}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${m}; wR++) {
|
|
int xR = xRCorner + wR * ${c};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${d}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${h===1}) {
|
|
|
|
if (${g}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${d}) *
|
|
getW(wR, wC, ${d}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${d}, xR, xC) *
|
|
getW(wR, wC, ${d}, d2);
|
|
}
|
|
|
|
} else if (${h===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${d}, d2),
|
|
getW(wR, wC, ${d} + 1, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${d}),
|
|
getX(batch, xR, xC, ${d} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${d}, xR, xC),
|
|
getX(batch, ${d} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${h===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${d}, d2),
|
|
getW(wR, wC, ${d} + 1, d2),
|
|
getW(wR, wC, ${d} + 2, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${d}),
|
|
getX(batch, xR, xC, ${d} + 1),
|
|
getX(batch, xR, xC, ${d} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${d}, xR, xC),
|
|
getX(batch, ${d} + 1, xR, xC),
|
|
getX(batch, ${d} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${D}
|
|
${k}
|
|
setOutput(result);
|
|
}
|
|
`}},hv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${s}, ${a}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${o}, ${n});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d2 = coords.u;
|
|
|
|
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xFCorner = xFRCCorner.x;
|
|
int xRCorner = xFRCCorner.y;
|
|
int xCCorner = xFRCCorner.z;
|
|
|
|
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
|
|
// y(yF, yR, yC, d2). ? = to be determined. : = across all
|
|
// values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${p}; wF++) {
|
|
int xF = xFCorner + wF * ${l};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${m}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${d}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${h===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${d}) *
|
|
getW(wF, wR, wC, ${d}, d2);
|
|
} else if (${h===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${d}),
|
|
getX(batch, xF, xR, xC, ${d} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${d}, d2),
|
|
getW(wF, wR, wC, ${d} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${h===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${d}),
|
|
getX(batch, xF, xR, xC, ${d} + 1),
|
|
getX(batch, xF, xR, xC, ${d} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${d}, d2),
|
|
getW(wF, wR, wC, ${d} + 1, d2),
|
|
getW(wF, wR, wC, ${d} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};var gv=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Rt(),x=m==="channelsLast",b=x?0:1,w=x?1:2,_="";for(let k=0;k<=1;k++)for(let D=0;D<=1;D++)_+=`
|
|
blockIndex = rc.y + ${D};
|
|
pos = rc.x + ${k};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${u})) * ${i} - ${d};
|
|
d0 = offsetY + ${p} * (pos / ${h});
|
|
|
|
if(d0 < ${t[b]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
|
|
d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));
|
|
|
|
if(d1 < ${t[w]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${s}.));
|
|
|
|
if (${x}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${k*2+D}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${k*2+D}] = 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;
|
|
|
|
${_}
|
|
|
|
${g.output} = result;
|
|
}
|
|
`}};function mx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Xk,w=l[2]%2!=0&&!!u.isPacked;if(b||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=pe({inputs:{x:r},backend:o,attrs:{shape:[1,_,t.inChannels]}}),D=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),T=Eu({a:k,b:D,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=pe({inputs:{x:T},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(D),x.push(T)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},D=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(hl(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let T=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(T);let R=Eu({a:k,b:T,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),O=o.texData.get(R.dataId);y.assert(O.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=D,O.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}for(let _ of x)o.disposeIntermediateTensorInfo(_);return g}function fx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,w=!1,_=[],k=pe({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),D=pe({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});_.push(k),_.push(D);let T=new gv(x,k.shape,t),R=o.runWebGLProgram(T,[k],"float32"),O=pe({inputs:{x:R},backend:o,attrs:{shape:[1,x[0],x[1]]}});_.push(R),_.push(O);let M=n!=null,G=s!=null,j=i==="leakyrelu",U=i?xl(i,!0):null,H=new Ef(O.shape,D.shape,[1,g,t.outChannels],b,w,M,U,G,j),q=[O,D];if(n&&q.push(n),G&&q.push(s),j){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));q.push(re),_.push(re)}let X=o.runWebGLProgram(H,q,"float32"),oe=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=pe({inputs:{x:X},backend:o,attrs:{shape:oe}});_.push(X);for(let re of _)o.disposeIntermediateTensorInfo(re);return Y}function k8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=mx({x:n,filter:s,convInfo:m,backend:t});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=fx({x:n,filter:s,convInfo:m,backend:t});else{let h=new $f(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=pe({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var f$={kernelName:Sn,backendName:"webgl",kernelFunc:k8};var xv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${o} - ${s};
|
|
|
|
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);
|
|
}
|
|
`}},yv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${p}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.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 < ${o}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${o} - 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);
|
|
}
|
|
`}},bv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${s};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${o} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},wv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${l}, ${u}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${s}.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 < ${o}; 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 = ${o} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function v8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new xv(m);return t.runWebGLProgram(f,[n,s],"float32")}var d$={kernelName:yc,backendName:"webgl",kernelFunc:v8};function C8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new yv(m);return t.runWebGLProgram(f,[n,s],"float32")}var h$={kernelName:Tn,backendName:"webgl",kernelFunc:C8};function I8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new hv(u);return t.runWebGLProgram(c,[n,s],"float32")}var g$={kernelName:Ha,backendName:"webgl",kernelFunc:I8};function N8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new bv(u);return t.runWebGLProgram(c,[n,s],"float32")}var x$={kernelName:bc,backendName:"webgl",kernelFunc:N8};function S8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new wv(u);return t.runWebGLProgram(c,[n,s],"float32")}var y$={kernelName:wc,backendName:"webgl",kernelFunc:S8};var T8=ix+`
|
|
return cos(x);
|
|
`,A8=_e({opSnippet:T8}),b$={kernelName:An,backendName:"webgl",kernelFunc:A8};var E8=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,D8=_e({opSnippet:E8}),w$={kernelName:Ii,backendName:"webgl",kernelFunc:D8};var _v=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,k]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
|
|
const float height_ratio = float(${g});
|
|
const float width_ratio = float(${w});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${a}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${x};
|
|
float width_scale = ${_};
|
|
|
|
float in_y = ${b};
|
|
if( in_y < 0.0 || in_y > ${d} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
float in_x = ${k};
|
|
if( in_x < 0.0 || in_x > ${h} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${f} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}};var $8=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new _v(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},_$={kernelName:Ni,backendName:"webgl",kernelFunc:$8};var dx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${k$(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${Le(n)} coords = getOutputCoords();
|
|
int end = ${v$(n,"coords")};
|
|
float val = ${s};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${l};
|
|
${v$(n,"coords")} = idx;
|
|
val += getX(${k$(n,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function k$(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function v$(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function R8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Mt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new dx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new dx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Mt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var C$={kernelName:En,backendName:"webgl",kernelFunc:R8};function F8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=rx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=E2(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var I$={kernelName:_c,backendName:"webgl",kernelFunc:F8};var kv=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 O8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new kv(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var N$={kernelName:Si,backendName:"webgl",kernelFunc:O8};var Rf=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";o&&(n?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:s?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${o}
|
|
}
|
|
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${p});
|
|
const ivec2 pads = ivec2(${l}, ${u});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${g};
|
|
int q = d2 - d1 * ${g};
|
|
|
|
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 < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${m};
|
|
|
|
if (xR < 0 || xR >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h}; wC++) {
|
|
int xC = xCCorner + wC * ${f};
|
|
|
|
if (xC < 0 || xC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${w}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}};var Ff=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let k=0;k<d;k++)for(let D=0;D<h;D++)x+=`
|
|
vec4 xTexelR${k}C${D*2} = vec4(0.);
|
|
vec4 wR${k}C${D} = vec4(0.);
|
|
vec4 xR${k}C${D} = vec4(0.);`;for(let k=0;k<d;k++)for(let D=0;D<g;D++){let T=D*2;if(x+=`
|
|
xR = xRCorner + ${k*m};
|
|
xC = xCCorner + ${T*f};
|
|
`,p===1){if(T<h&&(u%2==1?x+=`
|
|
xCOffset = xC + 1;
|
|
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
xTexelR${k}C${T}.zw = vec2(0.);
|
|
}
|
|
} else {
|
|
xTexelR${k}C${T} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + 1 - 2;
|
|
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
vec4 previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
previous.zw = vec2(0.);
|
|
}
|
|
|
|
xR${k}C${T} = vec4(previous.zw, xTexelR${k}C${T}.xy);
|
|
} else {
|
|
xR${k}C${T} = vec4(0, 0, xTexelR${k}C${T}.xy);
|
|
}
|
|
`:x+=`
|
|
if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
|
|
xTexelR${k}C${T} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${k}C${T} = vec4(0.);
|
|
}
|
|
|
|
xR${k}C${T} = xTexelR${k}C${T};
|
|
`,T+1<h)){let R=u%2==0?y.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(x+=`
|
|
xCOffset = xC + ${u%2} + ${R};
|
|
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`,f>1&&(x+=`
|
|
xCOffset -= 2;
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${k}C${T} = vec4(0.);
|
|
}
|
|
`),x+=`
|
|
xR${k}C${T+1} = vec4(
|
|
xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.xy);
|
|
`):x+=`
|
|
xCOffset = xC + ${R};
|
|
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
|
|
xR${k}C${T+1} = xTexelR${k}C${T+2};
|
|
`}}else T<h&&(x+=`
|
|
if(xR >= 0 && xR < ${a}) {
|
|
`,u%2==1?(x+=`
|
|
xCOffset = xC + 1 - ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${k}C${T} = vec4(0.);
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${i}) {
|
|
xTexelR${k}C${T+2} = getX(batch, xR, xC + 1, d1);
|
|
} else {
|
|
xTexelR${k}C${T+2} = vec4(0.);
|
|
}
|
|
|
|
xR${k}C${T} = vec4(
|
|
xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
|
|
`,T+1<h&&(x+=`
|
|
vec4 final = vec4(0.);
|
|
xCOffset = xC + 1 + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xR${k}C${T+1} = vec4(xTexelR${k}C${T+2}.xy, final.xy);
|
|
`)):(x+=`
|
|
if(xC >= 0 && xC < ${i}) {
|
|
xTexelR${k}C${T} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${k}C${T} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${k}C${T+2} = vec4(0.);
|
|
}
|
|
|
|
xR${k}C${T} = vec4(
|
|
xTexelR${k}C${T}.xy, xTexelR${k}C${T+2}.xy);
|
|
`,T+1<h&&(x+=`
|
|
xR${k}C${T+1} = vec4(
|
|
xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
|
|
`)),x+="}");T<h&&(x+=`
|
|
vec4 wTexelR${k}C${T} = getW(${k}, ${T}, d1, q);
|
|
wR${k}C${T} = vec4(wTexelR${k}C${T}.xz, wTexelR${k}C${T}.xz);
|
|
`,T+1<h&&(x+=`
|
|
vec4 wTexelR${k}C${T+1} = getW(${k}, ${T+1}, d1, q);
|
|
wR${k}C${T+1} =
|
|
vec4(wTexelR${k}C${T+1}.xz, wTexelR${k}C${T+1}.xz);`))}for(let k=0;k<d;k++)for(let D=0;D<h;D++)x+=`dotProd += xR${k}C${D} * wR${k}C${D};`;let b="",w="";o&&(n?b=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:s?b=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:b=`vec4 activation(vec4 x) {
|
|
${o}
|
|
}`,w="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${b}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${p});
|
|
const ivec2 pads = ivec2(${l}, ${u});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2;
|
|
int q = 0;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
vec4 dotProd = vec4(0.);
|
|
|
|
${x}
|
|
|
|
vec4 result = dotProd;
|
|
${_}
|
|
${w}
|
|
setOutput(result);
|
|
}
|
|
`}};function P8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return W().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Ff(p):m=new Rf(p),t.runWebGLProgram(m,[n,s],"float32")}var S$={kernelName:Dn,backendName:"webgl",kernelFunc:P8};var vv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${o} - ${s};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Cv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
ivec2 dyCorner = coords.yz - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.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 < ${o}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${o} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${l}; dm++) {
|
|
int d2 = d1 * ${l} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function M8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new vv(p);return t.runWebGLProgram(m,[n,s],"float32")}var T$={kernelName:kc,backendName:"webgl",kernelFunc:M8};function L8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new Cv(p);return t.runWebGLProgram(m,[n,s],"float32")}var A$={kernelName:vc,backendName:"webgl",kernelFunc:L8};var Iv=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function z8(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Iv(s),l=t.runWebGLProgram(i,[a],a.dtype),u=pe({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var E$={kernelName:Cc,backendName:"webgl",kernelFunc:z8};var Nv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
|
|
const ivec2 strides = ivec2(${s}, ${a});
|
|
const ivec2 pads = ivec2(${p}, ${m});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${i}; h++) {
|
|
int hIn = hBeg + h * ${u};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${l}; w++) {
|
|
int wIn = wBeg + w * ${c};
|
|
|
|
if (wIn >= 0 && wIn < ${o}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new Nv(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=pe({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var D$={kernelName:qa,backendName:"webgl",kernelFunc:B8};var V8="return (x >= 0.0) ? x : (exp(x) - 1.0);",G8=`
|
|
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;
|
|
`,W8=_e({opSnippet:V8,packedOpSnippet:G8}),$$={kernelName:Ti,backendName:"webgl",kernelFunc:W8};var j8="return (b >= 1.0) ? a : a * (b + 1.0);",U8=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,H8=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cs(U8,o.shape,n.shape):new cn(j8,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},R$={kernelName:Ic,backendName:"webgl",kernelFunc:H8};var q8=`
|
|
return vec4(equal(a, b));
|
|
`,K8="return float(a == b);",X8=nt({opSnippet:K8,packedOpSnippet:q8,dtype:"bool"}),F$={kernelName:Ei,backendName:"webgl",kernelFunc:X8};var Y8=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${N.ERF_P};
|
|
float a1 = ${N.ERF_A1};
|
|
float a2 = ${N.ERF_A2};
|
|
float a3 = ${N.ERF_A3};
|
|
float a4 = ${N.ERF_A4};
|
|
float a5 = ${N.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));
|
|
`,Z8=_e({opSnippet:Y8}),O$={kernelName:Ai,backendName:"webgl",kernelFunc:Z8};var P$="return exp(x);",Sv=_e({opSnippet:P$,packedOpSnippet:P$,cpuKernelImpl:R2}),M$={kernelName:Rn,backendName:"webgl",kernelFunc:Sv};function hx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var L$={kernelName:Ms,backendName:"webgl",kernelFunc:hx};var z$="return exp(x) - 1.0;",J8=_e({opSnippet:z$,packedOpSnippet:z$,cpuKernelImpl:F2}),B$={kernelName:Di,backendName:"webgl",kernelFunc:J8};var gx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${s};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${n});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${n}; 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 xx(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new gx("real",l,e),c=new gx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=fo({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=pe({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function Q8(r){let{inputs:e,backend:t}=r,{input:o}=e;return xx(o,!1,t)}var V$={kernelName:Nc,backendName:"webgl",kernelFunc:Q8};var Tv=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
uniform float value;
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Of(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Tv(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var G$={kernelName:Ka,backendName:"webgl",kernelFunc:Of};var Av=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${t} - x;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var W$={kernelName:$i,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Av(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var j$="return floor(x);",eY=_e({opSnippet:j$,packedOpSnippet:j$,cpuKernelImpl:O2}),U$={kernelName:Fn,backendName:"webgl",kernelFunc:eY};var tY=`
|
|
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;
|
|
}
|
|
`,rY=`
|
|
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);
|
|
`,oY=nt({opSnippet:tY,packedOpSnippet:rY,dtype:"int32"}),H$={kernelName:On,backendName:"webgl",kernelFunc:oY};var Ev=class{constructor(e){this.variableNames=["A"];let t=Rt(),[o,n]=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(${n}.0, ${o}.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));
|
|
}
|
|
`}};var Dv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Rt(),[o,n]=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(${n}.0, ${o}.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;
|
|
}
|
|
`}};var q$={kernelName:Im,backendName:"webgl",kernelFunc:nY},Np;function nY(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,[l,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,l],p=[u,l,s];(i||a)&&(Np==null&&(Np=document.createElement("canvas").getContext("2d")),Np.canvas.width=l,Np.canvas.height=u,Np.drawImage(n,0,0,l,u),n=Np.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=Fr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let f=W().getBool("WEBGL_PACK")?new Dv(p):new Ev(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function sY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=mx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=fx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,k=i!=null,D=f==="leakyrelu",T=f?xl(f,!1):null,R=new $f(g,_,T,k,D),O=[n,s];if(a&&O.push(a),i&&O.push(i),D){let M=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));O.push(M),b.push(M)}x=t.runWebGLProgram(R,O,"float32")}let w=pe({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var K$={kernelName:Xs,backendName:"webgl",kernelFunc:sY};function iY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?xl(m,x):null,w=[n,s],_=a!=null,k=i!=null,D=m==="leakyrelu";if(_&&w.push(a),k&&w.push(i),D){let O=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(O),d.push(O)}let T;x?T=new Ff(g,_,b,k,D):T=new Rf(g,_,b,k,D);let R=t.runWebGLProgram(T,w,"float32");return d.forEach(O=>t.disposeIntermediateTensorInfo(O)),R}var X$={kernelName:Ys,backendName:"webgl",kernelFunc:iY};var $v=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${n} strides = ${n}(${this.strides});
|
|
void main() {
|
|
${s} 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 aY(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=pe({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new $v(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var Y$={kernelName:Ri,backendName:"webgl",kernelFunc:aY};var Rv=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=lY(e,2);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${n}));
|
|
}
|
|
`}};function lY(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function uY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=pe({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(f),w=t.bufferSync(m),_=P2(w,b,d);return p.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new Rv(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var Z$={kernelName:Ls,backendName:"webgl",kernelFunc:uY};var cY="return float(a > b);",pY=`
|
|
return vec4(greaterThan(a, b));
|
|
`,mY=nt({opSnippet:cY,packedOpSnippet:pY,cpuKernelImpl:M2,dtype:"bool"}),J$={kernelName:Fi,backendName:"webgl",kernelFunc:mY};var fY="return float(a >= b);",dY=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,hY=nt({opSnippet:fY,packedOpSnippet:dY,dtype:"bool"}),Q$={kernelName:Mn,backendName:"webgl",kernelFunc:hY};function gY(r){let{inputs:e,backend:t}=r,{input:o}=e;return xx(o,!0,t)}var eR={kernelName:Sc,backendName:"webgl",kernelFunc:gY};var xY="return float(!isnan(x) && !isinf(x));",yY=_e({opSnippet:xY,dtype:"bool"}),tR={kernelName:Oi,backendName:"webgl",kernelFunc:yY};var bY="return float(isinf(x));",wY=_e({opSnippet:bY,dtype:"bool"}),rR={kernelName:Pi,backendName:"webgl",kernelFunc:wY};var _Y="return float(isnan(x));",kY=_e({opSnippet:_Y,dtype:"bool"}),oR={kernelName:Mi,backendName:"webgl",kernelFunc:kY};var vY="return float(a < b);",CY=`
|
|
return vec4(lessThan(a, b));
|
|
`,IY=nt({opSnippet:vY,packedOpSnippet:CY,cpuKernelImpl:L2,dtype:"bool"}),nR={kernelName:Li,backendName:"webgl",kernelFunc:IY};var NY="return float(a <= b);",SY=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,TY=nt({opSnippet:NY,packedOpSnippet:SY,dtype:"bool"}),sR={kernelName:zi,backendName:"webgl",kernelFunc:TY};function AY(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=z2(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var iR={kernelName:Ac,backendName:"webgl",kernelFunc:AY};var EY=`if (x < 0.0) return NAN;
|
|
return log(x);`,DY=`
|
|
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;
|
|
`,$Y=_e({opSnippet:EY,packedOpSnippet:DY,cpuKernelImpl:B2}),aR={kernelName:zn,backendName:"webgl",kernelFunc:$Y};var RY="return log(1.0 + x);",FY=_e({opSnippet:RY}),lR={kernelName:Bi,backendName:"webgl",kernelFunc:FY};var OY="return float(a >= 1.0 && b >= 1.0);",PY=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,MY=nt({opSnippet:OY,packedOpSnippet:PY,dtype:"bool"}),uR={kernelName:Vi,backendName:"webgl",kernelFunc:MY};var LY="return float(!(x >= 1.0));",zY=_e({opSnippet:LY}),cR={kernelName:Ll,backendName:"webgl",kernelFunc:zY};var BY="return float(a >= 1.0 || b >= 1.0);",VY=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,GY=nt({opSnippet:BY,packedOpSnippet:VY,dtype:"bool"}),pR={kernelName:zl,backendName:"webgl",kernelFunc:GY};var Fv=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${a}; j <= ${a}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${l};
|
|
setOutput(val);
|
|
}
|
|
`}};var Ov=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${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(${i}));
|
|
|
|
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
|
|
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
|
|
|
|
if(depthInRange || depthPlusOneInRange){
|
|
vec4 z = vec4(0.);
|
|
vec4 xFragAtCurrentDepth;
|
|
z.xz = cache.xy;
|
|
if(depthPlusOneInRange && hasNextCol){
|
|
xFragAtCurrentDepth = idx.y != d ?
|
|
getX(b, r, c, idx.y) : xFragAtOutputCoords;
|
|
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
|
|
if(hasNextRow){
|
|
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
|
|
}
|
|
}
|
|
cache.xy = z.yw;
|
|
sum += z * z;
|
|
}
|
|
}
|
|
vec4 result = xAtOutputCoords * ${l};
|
|
setOutput(result);
|
|
}
|
|
`}};var WY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new Ov(n.shape,s,a,i,l):new Fv(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},mR={kernelName:Xa,backendName:"webgl",kernelFunc:WY};var Pv=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${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(${n}) * norm + float(${o});
|
|
|
|
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(${n})
|
|
* float(${s})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${s});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};var jY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new Pv(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},fR={kernelName:Ec,backendName:"webgl",kernelFunc:jY};function dR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,r.dtype,"max",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function Mv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let T=0;T<_.length;T++)_[T]=n.shape[c[T]];let k=Tu(w,n.shape,n.dtype,c,_);f=t.makeTensorInfo(_,n.dtype);let D=t.texData.get(f.dataId);D.values=k}else f=yl(n,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let w=t.texData.get(f.dataId).values,_=V2(w,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let k=t.texData.get(x.dataId);k.values=_}else x=dR(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var hR={kernelName:Bn,backendName:"webgl",kernelFunc:Mv};var UY=sx+`
|
|
return max(a, b);
|
|
`,HY=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+gl+`
|
|
return result;
|
|
`,qY=nt({opSnippet:UY,packedOpSnippet:HY,cpuKernelImpl:G2}),gR={kernelName:Vn,backendName:"webgl",kernelFunc:qY};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;_s(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new ii(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var xR={kernelName:Gn,backendName:"webgl",kernelFunc:KY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new Du(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var yR={kernelName:Ya,backendName:"webgl",kernelFunc:XY};var Lv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${s};
|
|
wR += ${n}) {
|
|
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) / ${o}.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 = ${u} - 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);
|
|
}
|
|
`}},zv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${m}, ${f});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${l};
|
|
wD += ${s}) {
|
|
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 < ${u};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${o}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.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 = ${d} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${u} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Du(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new zv(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var bR={kernelName:$c,backendName:"webgl",kernelFunc:YY};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;_s([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new ii(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new Lv(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var wR={kernelName:Dc,backendName:"webgl",kernelFunc:ZY};function _R(r,e,t,o){let n=new ii(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new ii(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var kR={kernelName:Rc,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=_R(o,i,c,l);return[p,m]}};function vR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,"float32","mean",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var CR={kernelName:Wn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let _=a.texData.get(d.dataId).values,k=new Array(i);for(let R=0;R<k.length;R++)k[R]=o.shape[c[R]];let D=Tu(_,o.shape,o.dtype,c,k);d=a.makeTensorInfo(k,o.dtype);let T=a.texData.get(d.dataId);T.values=D}else d=yl(o,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),x=h;n&&(x=N.expandShapeToKeepDim(h,l));let b=vR(d,g,x,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return b}};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"min",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var IR={kernelName:jn,backendName:"webgl",kernelFunc:JY};var QY=sx+`
|
|
return min(a, b);
|
|
`,e7=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+gl+`
|
|
return result;
|
|
`,t7=nt({opSnippet:QY,packedOpSnippet:e7,cpuKernelImpl:W2}),NR={kernelName:Un,backendName:"webgl",kernelFunc:t7};var Bv=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${u};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${u};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${s} start = ${s}(${a});
|
|
${s} end = ${s}(${i});
|
|
|
|
void main() {
|
|
${s} outC = getOutputCoords();
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${u};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
|
|
}
|
|
}
|
|
${s} coords = outC - start;
|
|
setOutput(getX(${l}));
|
|
}
|
|
`}};var Vv=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Wt("rc",n),u=Wt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
|
|
${s} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${m};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${m};
|
|
}
|
|
source -= start;
|
|
`;f=`
|
|
${s} rc = outputLoc;
|
|
${d}
|
|
result[0] = getChannel(getX(${u.join()}), ${p});
|
|
${l[n-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
`}else{let d=`
|
|
${s} source = rc;
|
|
${s} lt = ${s}(lessThan(source, start));
|
|
${s} gte = ${s}(greaterThanEqual(source, end));
|
|
${s} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${m}) +
|
|
gte * ((end - 1) * 2 - source + ${m});
|
|
source -= start;
|
|
`;f=`
|
|
${s} rc = outputLoc;
|
|
${d}
|
|
result[0] = getChannel(getX(${u.join()}), ${p});
|
|
${l[n-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
rc = outputLoc;
|
|
${l[n-2]} += 1;
|
|
if(${l[n-2]} < ${this.outputShape[n-2]}) {
|
|
${d}
|
|
result[2] = getChannel(getX(${u.join()}), ${p});
|
|
${l[n-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[3] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${s} start = ${s}(${a});
|
|
const ${s} end = ${s}(${i});
|
|
|
|
void main() {
|
|
${s} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};var r7=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vv(o.shape,n,s):new Bv(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},SR={kernelName:Za,backendName:"webgl",kernelFunc:r7};var o7=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,n7=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+gl+`
|
|
return result;
|
|
`,s7=nt({opSnippet:o7,packedOpSnippet:n7}),TR={kernelName:Gi,backendName:"webgl",kernelFunc:s7};var Gv=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],this.userCode=`
|
|
uniform float seed;
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var i7=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,a7=`
|
|
// 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;
|
|
`,Wv=nt({opSnippet:i7,packedOpSnippet:a7,checkOutOfBounds:!0}),AR={kernelName:$n,backendName:"webgl",kernelFunc:Wv};var ER="return a - b;",jv=nt({opSnippet:ER,packedOpSnippet:ER,supportsComplex:!0,cpuKernelImpl:Z2}),DR={kernelName:us,backendName:"webgl",kernelFunc:jv};function Uv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Mv({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=pe({inputs:{x:i},backend:t,attrs:{shape:l}}),c=jv({inputs:{a:n,b:u},backend:t}),p=Sv({inputs:{x:c},backend:t}),m=Df({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=pe({inputs:{x:m},backend:t,attrs:{shape:l}}),d=Wv({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var $R={kernelName:as,backendName:"webgl",kernelFunc:Uv};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:Uv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new Gv(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var RR={kernelName:Fc,backendName:"webgl",kernelFunc:l7};var FR="return -x;";function u7(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=U2(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new vs(o.shape,FR):n=new mo(o.shape,FR),t.runWebGLProgram(n,[o],o.dtype)}var OR={kernelName:zs,backendName:"webgl",kernelFunc:u7};var c7=$r.nonMaxSuppressionV3Impl;function p7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=c7(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var PR={kernelName:ji,backendName:"webgl",kernelFunc:p7};var m7=$r.nonMaxSuppressionV4Impl;function f7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=m7(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var MR={kernelName:Ui,backendName:"webgl",kernelFunc:f7};var d7=$r.nonMaxSuppressionV5Impl;function h7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=d7(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var LR={kernelName:Hi,backendName:"webgl",kernelFunc:h7};var Hv=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${n}), float(${o}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}};var g7=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new Hv(l,s,a,i),c=pe({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=pe({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},zR={kernelName:qn,backendName:"webgl",kernelFunc:g7};function Pf(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Sa({inputs:{input:o},backend:t}),s=Pf({inputs:{x:n},backend:t}),a=$u({inputs:{input:o},backend:t}),i=Pf({inputs:{x:a},backend:t}),l=fo({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Of({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var BR={kernelName:qs,backendName:"webgl",kernelFunc:Pf};function VR(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Sa({inputs:{input:o},backend:t}),s=VR({inputs:{x:n},backend:t}),a=$u({inputs:{input:o},backend:t}),i=Pf({inputs:{x:a},backend:t}),l=fo({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Of({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var GR={kernelName:Bs,backendName:"webgl",kernelFunc:VR};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return hx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=hx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=dv({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var WR={kernelName:Vs,backendName:"webgl",kernelFunc:x7};var qv=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${i};
|
|
uniform float value;
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${s} start = ${s}(${a});
|
|
${s} end = ${s}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${s} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${s} coords = outC - start;
|
|
setOutput(getX(${l}));
|
|
}
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var Kv=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Wt("rc",n),u=Wt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
|
|
if(${c}) {
|
|
`,n===1?"":`}
|
|
rc = outputLoc;
|
|
${l[n-2]} += 1;
|
|
if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${l[n-1]} += 1;
|
|
if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h<g;h++)d+=`
|
|
${m[h]}
|
|
if (${f}) {
|
|
result[${h}] = float(value);
|
|
} else {
|
|
${s} source = rc - start;
|
|
result[${h}] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
`;d+=n===1?"} ":"}}",this.userCode=`
|
|
const ${s} start = ${s}(${a});
|
|
const ${s} end = ${s}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${s} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var Xv=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kv(n.shape,s,a):new qv(n.shape,s,a),l=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[n],n.dtype,l)},jR={kernelName:Kn,backendName:"webgl",kernelFunc:Xv};var y7=`
|
|
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);
|
|
`,b7=`
|
|
// 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));
|
|
`+gl+`
|
|
return result;
|
|
`,w7=nt({opSnippet:y7,packedOpSnippet:b7}),UR={kernelName:Xn,backendName:"webgl",kernelFunc:w7};function _7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Mt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=H2(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=pe({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=Gl(n.dtype),w=To(x,b,"prod",t);f=pe({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(x),l.push(w)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=pe({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var HR={kernelName:qi,backendName:"webgl",kernelFunc:_7};var Yv=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=q2(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},qR={kernelName:Ja,backendName:"webgl",kernelFunc:Yv};var k7="return 1.0 / x;",v7=_e({opSnippet:k7}),KR={kernelName:Ki,backendName:"webgl",kernelFunc:v7};var C7=xr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,I7=`
|
|
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;
|
|
`,N7=_e({opSnippet:C7,packedOpSnippet:I7}),XR={kernelName:Zn,backendName:"webgl",kernelFunc:N7};var S7=xr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,T7=`
|
|
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;
|
|
`,A7=_e({opSnippet:S7,packedOpSnippet:T7}),YR={kernelName:Qn,backendName:"webgl",kernelFunc:A7};var Zv=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${m};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};var Jv=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]},
|
|
${c[1]/p[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
|
|
${l}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${m};
|
|
|
|
// Compute the four integer indices.
|
|
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
|
|
ivec3 sourceCeilRC = ivec3(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${u-1};
|
|
bool hasNextRow = coords.z < ${o-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 E7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Jv(n.shape,l,u,s,a):new Zv(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var ZR={kernelName:Jn,backendName:"webgl",kernelFunc:E7};var Qv=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${m});
|
|
const float invWidthScale = float(${f});
|
|
|
|
const int winHeight = int(${d});
|
|
const int winWidth = int(${h});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new Qv(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var JR={kernelName:Mc,backendName:"webgl",kernelFunc:D7};var eC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${f};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function $7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new eC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var QR={kernelName:Qa,backendName:"webgl",kernelFunc:$7};var tC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${m});
|
|
const float invWidthScale = float(${f});
|
|
|
|
const int winHeight = int(${d});
|
|
const int winWidth = int(${h});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${l[0]}) *
|
|
(float(dyR) / float(${u[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${l[1]}) *
|
|
(float(dyC) / float(${u[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${n}) - 1),
|
|
${o} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${s}) - 1),
|
|
${o} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function R7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new tC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var eF={kernelName:Pc,backendName:"webgl",kernelFunc:R7};var rC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Le(o);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${s}));
|
|
}
|
|
`}};var oC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Wt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===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(${s}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${l(n.slice())};
|
|
if(${s}){
|
|
result.g = ${u(n.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${c(n.slice())};
|
|
if(${s}) {
|
|
result.a = ${p(n.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oC(n.shape,i):new rC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var tF={kernelName:es,backendName:"webgl",kernelFunc:F7};var nC=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
|
|
vec3 fill = vec3(${t.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
uniform vec4 params;
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${s}
|
|
if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,o,n){return(s,a)=>{this.paramsLoc==null&&(this.paramsLoc=s.getUniformLocationNoThrow(a,"params")),s.gl.uniform4f(this.paramsLoc,e,t,o,n)}}};var rF={kernelName:oa,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new nC(o.shape,s),[u,c]=N.getImageCenter(a,o.shape[1],o.shape[2]),p=l.getCustomSetupFunc(u,c,Math.sin(n),Math.cos(n));return i.runWebGLProgram(l,[o],o.dtype,p)}};var O7=`
|
|
// 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;
|
|
}
|
|
}
|
|
`,P7=_e({opSnippet:O7}),oF={kernelName:ts,backendName:"webgl",kernelFunc:P7};var M7="return inversesqrt(x);",L7=_e({opSnippet:M7,cpuKernelImpl:K2}),nF={kernelName:rs,backendName:"webgl",kernelFunc:L7};var Mf=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
|
|
${l} strides = ${l}(${s});
|
|
|
|
void main() {
|
|
${u} 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(${p});
|
|
flattenedIndex += index * ${d};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${f};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function z7(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=pe({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=pe({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Mf(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=pe({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var sF={kernelName:Xi,backendName:"webgl",kernelFunc:z7};var sC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${n});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${s}));
|
|
} else {
|
|
setOutput(getB(${s}));
|
|
}
|
|
}
|
|
`}};function B7(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new sC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],ir(n.dtype,s.dtype))}var iF={kernelName:Ws,backendName:"webgl",kernelFunc:B7};var V7=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${N.SELU_SCALEALPHA};
|
|
float scale = ${N.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,G7=_e({opSnippet:V7}),aF={kernelName:Yi,backendName:"webgl",kernelFunc:G7};var W7="return 1.0 / (1.0 + exp(-1.0 * x));",j7=_e({opSnippet:W7}),lF={kernelName:ns,backendName:"webgl",kernelFunc:j7};var U7=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,H7=_e({opSnippet:U7}),uF={kernelName:Ji,backendName:"webgl",kernelFunc:H7};var q7=ix+`
|
|
return sin(x);
|
|
`,K7=_e({opSnippet:q7}),cF={kernelName:os,backendName:"webgl",kernelFunc:K7};var X7=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Y7=_e({opSnippet:X7}),pF={kernelName:Zi,backendName:"webgl",kernelFunc:Y7};var Z7=`
|
|
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;
|
|
`,J7=_e({opSnippet:Z7}),mF={kernelName:Qi,backendName:"webgl",kernelFunc:J7};var Q7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),l=[[0,0]];l.push(...a);for(let x=1+s.length;x<n.shape.length;++x)l.push([0,0]);let u=[],c=Xv({inputs:{x:n},backend:t,attrs:{paddings:l,constantValue:0}}),p=N.getReshaped(c.shape,s,i,!1),m=N.getPermuted(p.length,s.length,!1),f=N.getReshapedPermuted(c.shape,s,i,!1),d=pe({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Mt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},fF={kernelName:el,backendName:"webgl",kernelFunc:Q7};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=N.calculateShapes(s,n,i),m=!1,f=new Mf(u,l,n.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,n,a],s.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var dF={kernelName:Lc,backendName:"webgl",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],l=N.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),p=n.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Na({inputs:{x:n},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var hF={kernelName:Us,backendName:"webgl",kernelFunc:tZ};var rZ="return sqrt(x);",oZ=_e({opSnippet:rZ}),gF={kernelName:ss,backendName:"webgl",kernelFunc:oZ};var nZ="return x * x;",sZ=_e({opSnippet:nZ}),xF={kernelName:tl,backendName:"webgl",kernelFunc:sZ};var yF="return (a - b) * (a - b);",iZ=nt({opSnippet:yF,packedOpSnippet:yF}),bF={kernelName:ls,backendName:"webgl",kernelFunc:iZ};function aZ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=xr+`
|
|
return x > 0.0 ? 1.0 : float(${e.alpha});
|
|
`,s=new mo(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var wF={kernelName:Xo,backendName:"webgl",kernelFunc:aZ};var iC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Le(o.length),a=Le(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${s} begin = ${s}(${e});
|
|
${s} strides = ${s}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:b}=er.sliceInfo(n.shape,s,a,i,l,u,c,p,m),w=pe({inputs:{x:n},backend:t,attrs:{shape:x}}),_;if(f){let D=Na({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=pe({inputs:{x:D},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(D)}else if(b.some(D=>D===0))_=t.makeTensorInfo(b,n.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let R=t.texData.get(w.dataId).values,O=ve(w.shape,w.dtype,R),M=Y2(b,O,h,d);_=t.makeTensorInfo(b,w.dtype,M.values)}else{let T=new iC(d,h,b);_=t.runWebGLProgram(T,[w],w.dtype)}let k=pe({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),k}var _F={kernelName:ea,backendName:"webgl",kernelFunc:lZ};var uZ="return tan(x);",cZ=_e({opSnippet:uZ}),kF={kernelName:ta,backendName:"webgl",kernelFunc:cZ};var pZ=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,mZ=_e({opSnippet:pZ}),vF={kernelName:cs,backendName:"webgl",kernelFunc:mZ};var aC=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=fZ(e);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function fZ(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function lC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype==="string"){let u=t.readSync(n.dataId).map(m=>y.decodeString(m)),c=ve(n.shape,n.dtype,u),p=J2(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new aC(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var CF={kernelName:Oo,backendName:"webgl",kernelFunc:lC};function dZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=t.readSync(n.dataId),[l,u]=Q2(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var IF={kernelName:ra,backendName:"webgl",kernelFunc:dZ};var uC=class{constructor(e,t,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,l;switch(n){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${l} == 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 (${l} == 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 (${l} == 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(${s});
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
float outputValue;
|
|
int batch = coords[0];
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
int channel = coords[3];
|
|
float xf = float(x);
|
|
float yf = float(y);
|
|
float a1 = getTransforms(batch, 0);
|
|
float a2 = getTransforms(batch, 1);
|
|
float a3 = getTransforms(batch, 2);
|
|
float b1 = getTransforms(batch, 3);
|
|
float b2 = getTransforms(batch, 4);
|
|
float b3 = getTransforms(batch, 5);
|
|
float c1 = getTransforms(batch, 6);
|
|
float c2 = getTransforms(batch, 7);
|
|
float projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = float(${s});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${i} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function hZ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=o,[c,p,m,f]=n.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],x=new uC(p,m,a,i,l,g);return t.runWebGLProgram(x,[n,s],"float32")}var NF={kernelName:zc,backendName:"webgl",kernelFunc:hZ};function gZ(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;_s(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=eD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var SF={kernelName:Bc,backendName:"webgl",kernelFunc:gZ};function xZ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,l=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Na({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),x=pe({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=x,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var TF={kernelName:Hs,backendName:"webgl",kernelFunc:xZ};var cC=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let l="0.0",u="sumValue",c=Math.floor(o/4)*4,p=o%4,m=`
|
|
sumValue += dot(values, segFilter);
|
|
`,f="";s%o>0&&(f=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`);let d="";s%o>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${l};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${f}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${d}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${a})) * float(${o}));
|
|
int currentSeg = int(mod(float(outIdx), float(${a})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${m}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${p===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
0
|
|
);
|
|
|
|
${m}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function yZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=N.getAxesPermutation([u],i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=N.getInnerMostAxes(1,i)[0]);let m=N.segment_util.computeOutShape(p.shape,u,a),f=y.sizeFromShape([p.shape[u]]),d=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=Gl(n.dtype),g=(_,k,D,T,R)=>{let O=_.shape[0],M=_.shape[1],G=N.segment_util.segOpComputeOptimalWindowSize(M,R),j={windowSize:G,inSize:M,batchSize:O,numSegments:R},U=new cC(j,k),H=t.compileAndRun(U,[_,D],T);if(l.push(H),H.shape[1]===R)return H;let q=Yv({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),X=lC({inputs:{x:q},backend:t,attrs:{reps:[M/G]}});return l.push(q),l.push(X),g(H,k,X,T,R)},x=g(d,"unsortedSegmentSum",s,h,a),b=pe({inputs:{x},backend:t,attrs:{shape:m}}),w=b;if(c!=null){l.push(b);let _=N.getUndoAxesPermutation(c);w=Mt({inputs:{x:w},backend:t,attrs:{perm:_}})}return l.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var AF={kernelName:rl,backendName:"webgl",kernelFunc:yZ};var bZ=[mR,fR,SD,AD,ED,DD,RD,FD,OD,PD,zD,BD,VD,GD,jD,WD,UD,qD,HD,KD,XD,YD,ZD,QD,e$,n$,i$,a$,u$,hD,m$,d$,h$,f$,x$,y$,g$,b$,w$,_$,C$,I$,N$,T$,A$,S$,E$,D$,$$,R$,F$,O$,M$,L$,B$,V$,G$,W$,U$,H$,q$,K$,X$,Y$,Z$,J$,Q$,dD,eR,c$,tR,rR,oR,gD,nR,sR,iR,lR,aR,uR,cR,pR,hR,yR,xR,bR,wR,kR,gR,CR,IR,NR,SR,TR,RR,_D,OR,PR,MR,LR,t$,zR,GR,WR,jR,UR,xD,HR,qR,r$,AR,KR,YR,XR,vD,ZR,JR,QR,eF,tF,rF,oF,nF,sF,iF,aF,lF,uF,cF,pF,JD,$R,mF,fF,dF,hF,gF,xF,bF,wF,_F,DR,ID,kF,vF,CF,IF,NF,ND,SF,TF,AF,BR];for(let r of bZ)Bl(r);var Lt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(Lt||(Lt={}));var bl;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu"})(bl||(bl={}));var EF;function wZ(r){EF=r.wasm.cwrap(Ks,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function _Z(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o,m=t.dataIdMap.get(n.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);d=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=bl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=l?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],w=n.shape[0],_=t.makeOutput([w,x,b],n.dtype),k=t.dataIdMap.get(_.dataId).id,D=new Uint8Array(new Int32Array(n.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return EF(m,D,n.shape.length,f,T,s.shape.length,l,u,g,d,h,p||0,k),_}var DF={kernelName:Ks,backendName:"wasm",setupFunc:wZ,kernelFunc:_Z};function Nt(r){let e;function t(n){e=n.wasm.cwrap(r,null,["number","number"])}function o(n){let{backend:s,inputs:{x:a}}=n,i=s.dataIdMap.get(a.dataId).id,l=s.makeOutput(a.shape,a.dtype),u=s.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(i,u),l}return{kernelName:r,backendName:"wasm",setupFunc:t,kernelFunc:o}}var $F=Nt(Os);function xt(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:l}=a,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,f=t!=null?t:u.dtype,d=N.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id,w=()=>o(p,g,u.shape.length,m,x,c.shape.length,Lt[u.dtype],b);if(e&&u.dtype==="float32")return w(),h;let _=N.getBroadcastDims(u.shape,d),k=N.getBroadcastDims(c.shape,d),D=_.every((R,O)=>R===O),T=k.every((R,O)=>R===O);if(D&&T)return w(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var kZ=!0,RF=xt(Fo,kZ);var FF;function vZ(r){FF=r.wasm.cwrap(kn,null,["array","number","number","number"])}function CZ(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(y.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return FF(s,n.length,Lt[o.dtype],a),o}var OF={kernelName:kn,backendName:"wasm",setupFunc:vZ,kernelFunc:CZ};function Fu(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var PF={kernelName:Ko,backendName:"wasm",kernelFunc:Fu};var MF;function IZ(r){MF=r.wasm.cwrap(ps,null,["number","array","number","number","number","array","number"])}function Sp(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=SZ(e.x.shape,o.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=NZ(e.x.shape,o.perm),l={dataId:e.x.dataId,shape:n,dtype:e.x.dtype};if(a){let d=Fu({inputs:e,backend:t});return d.shape=i,d}let u=t.makeOutput(i,l.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(l.shape).buffer);return MF(c,f,l.shape.length,Lt[l.dtype],p,m,s.length),u}function NZ(r,e){let t=new Array(r.length);for(let o=0;o<t.length;o++)t[o]=r[e[o]];return t}function SZ(r,e){let t=[],o=[];for(let n=0;n<r.length;++n)r[n]!==1&&t.push(r[n]),r[e[n]]!==1&&o.push(e[n]);for(let n=0;n<o.length;++n){let s=-1;for(let a=0;a<o.length;++a)o[a]>=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var LF={kernelName:ps,backendName:"wasm",kernelFunc:Sp,setupFunc:IZ};function pn(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=N.getAxesPermutation(a,n),l=null,u=!1;if(i!=null){let c=new Array(n);for(let f=0;f<c.length;f++)c[f]=o[i[f]];a=N.getInnerMostAxes(a.length,n),l=Sp({inputs:{x:r},attrs:{perm:i},backend:t});let p=t.dataIdMap.get(r.dataId).id;t.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:a,inputWasTransposed:u}}var zF;function TZ(r){zF=r.wasm.cwrap(vn,null,["number","number","number","number","number"])}function AZ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n}=o,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=a,l=s,{transposed:u,axes:c,inputWasTransposed:p}=pn(s,n,e);if(p){let x=e.dataIdMap.get(u.dataId).id;x!==a&&(l=u,i=x)}let 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;this.outputLayers.push(w),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(k)}for(let b of this.inputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;Vo(_===0,"input layer has >1 nodes"),Vo(k===0,"input layer has >1 tensors"),this.inputLayers.push(w),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let w=this.inputLayers[b];if(!(w instanceof ui))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${w.getClassName()}.`);this.inputNames.push(w.name),this.feedInputShapes.push(w.batchInputShape),this.feedInputNames.push(w.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,w,_,k,D,T)=>{(k==null||D==null||T==null)&&(k=b.sourceLayer,D=b.nodeIndex,T=b.tensorIndex);let R=k.inboundNodes[D];if(_.indexOf(R)!==-1)throw new Vr(`The tensor ${b.name} at layer "${k.name}" is part of a cycle.`);if(w.indexOf(R)!==-1)return;this.containerNodes.add(Go.nodeKey(k,D)),k.id in a||(a[k.id]=Object.keys(a).length),_.indexOf(R)===-1&&_.push(R);let O=R.inboundLayers.length;for(let M=0;M<O;M++){let G=R.inputTensors[M],j=R.inboundLayers[M],U=R.nodeIndices[M],H=R.tensorIndices[M];l(G,w,_,j,U,H)}for(w.push(R);_.indexOf(R)>=0;)_.splice(_.indexOf(R),1);i.push(R)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){o[b.id]=b,b.id in t||(t[b.id]=0);let w=t[b.id],_=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];w=Math.max(w,_),n[b.outboundLayer.id]=w,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=w;for(let k=0;k<b.inboundLayers.length;k++){let D=b.inboundLayers[k],T=b.nodeIndices[k],R=D.inboundNodes[T],O=t[R.id]==null?0:t[R.id];t[R.id]=Math.max(w+1,O),o[R.id]=R}}let m={};for(let b in t){let w=t[b];w in m||(m[w]=[]),m[w].push(o[b])}let f={};for(let b in n){let w=n[b];w in f||(f[w]=[]),f[w].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(Gf);this.layers=[];for(let b of d){let w=f[b];w.sort((_,k)=>{let D=a[_.id],T=a[k.id];return D<T?-1:D>T?1:0});for(let _ of w)_ instanceof Go&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(Gf);let h=this.inputs.slice(),g=[];for(let b of d)for(let w of m[b]){let _=w.outboundLayer;if(_!=null){for(let k of w.inputTensors)if(h.indexOf(k)===-1)throw new Vr(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${_.name}". 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new z(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new z(`${a.length} of ${n} weights are not set: ${a}`)}Wp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${cd}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Ux(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return V(()=>{e=yt(e);let o=new Ss;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return Gu(this.outputs,o,t)})}computeMask(e,t){return V(()=>{e=yt(e);let o;return t==null?o=mn(null,e.length):o=yt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=Vp(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Gf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],x=u.nodeIndices[h],b=u.tensorIndices[h],w=`${g.name}_${x}_${b}`,_=o[w];p.push(_)}let m=c.computeOutputShape(yr(p)),f=Vp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Vo(l in o),s.push(o[l])}return yr(s)}runInternalGraph(e,t){t==null&&(t=mn(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(Gf);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,x,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[_,k]=d[0];h.mask==null&&(h.mask=k),b=yt(p.call(_,h)),w=yt(p.computeMask(_,k)),g=[_],x=[k]}else g=d.map(_=>_[0]),x=d.map(_=>_[1]),h.mask==null&&(h.mask=x),b=yt(p.call(g,h)),w=yt(p.computeMask(g,x));if(p.activityRegularizer)throw new Se("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<f.length;++_){let k=f[_],D=b[_],T=w[_];o[k.id]=[D,T]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Vo(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Go?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Go.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`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 z("Provide either a layer name or layer index");for(let o of this.layers)if(o.name===e)return o;throw new z(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Go.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Go.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. 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Provided ${t} not understood: ${JSON.stringify(r)}`)}function Hx(r,e){return WQ(r,e,"classWeight")}async function qx(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Ae(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(Wz(t.validationData))y.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=UQ(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Lx(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=zx(c,p,t.epochs,null,null,HQ(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(o||(h=await e.iterator());o?x<t.batchesPerEpoch:!0;){let w=await h.next();if(o&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. 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instead`);if(s==="channelsFirst"&&(r=Ke(r,[0,2,1])),n==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Yl(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ho(i,t)),i})}function r3(r,e,t,o=[1,1],n="valid",s,a,i=null){return V(()=>{if(s==null&&(s=to()),$t(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=bd(r,s);if(n==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=rn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ke(l,[0,3,1,2])),l})}function aee(r,e,t,o=[1,1,1],n="valid",s,a){return V(()=>{if(s==null&&(s=to()),$t(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=a0(r,s);if(n==="causal")throw new Se("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=jm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ho(i,t)),s==="channelsFirst"&&(i=Ke(i,[0,4,1,2,3])),i})}var Kp=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Kp.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Se(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Nl(t.kernelSize,e,"kernelSize"),this.strides=Nl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ro(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=As(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ot(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=Nl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Vo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ix(e.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ts(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ju=class extends Kp{constructor(e,t){super(e,t);this.kernel=null,ju.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ot(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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]:o}}],this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o,n=this.bias==null?null:this.bias.read(),s=Nx(this.activation.getClassName());if(s!=null&&this.rank===2)o=r3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=iee(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=r3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=aee(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Se("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Je(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=bo(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:st(this.kernelRegularizer),kernelConstraint:Ft(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 z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Sl=class extends ju{constructor(e){super(2,e);Sl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ix(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Sl.className="Conv2D";Q.registerClass(Sl);var Uu=class extends ju{constructor(e){super(3,e);Uu.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 z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Uu.className="Conv3D";Q.registerClass(Uu);var wd=class extends Sl{constructor(e){super(e);if(this.inputSpec=[new At({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new At({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{let o=Fe(e);if(o.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=yd(l,m,c,this.padding),h=yd(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=Ke(o,[0,2,3,1]));let x=Zl(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ke(x,[0,3,1,2])),this.bias!=null&&(x=ho(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Je(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=yd(t[n],l,a,this.padding),t[s]=yd(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};wd.className="Conv2DTranspose";Q.registerClass(wd);var l0=class extends ju{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 z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("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 z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Ot(t.depthwiseConstraint),this.pointwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Ot(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"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 At({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o;if(this.rank===1)throw new Se("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ke(e,[0,2,3,1])),o=nf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ho(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=Ke(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.pointwiseRegularizer=st(this.pointwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseConstraint),e.pointwiseConstraint=Ft(this.pointwiseConstraint),e}};l0.className="SeparableConv";var _d=class extends l0{constructor(e){super(2,e)}};_d.className="SeparableConv2D";Q.registerClass(_d);var Hu=class extends ju{constructor(e){super(1,e);Hu.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"&&!Ix(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Hu.className="Conv1D";Q.registerClass(Hu);var kd=class extends Me{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Fe(e),this.dataFormat==="channelsLast"){let o=Kf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Kf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=Kf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Kf(o,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};kd.className="Cropping2D";Q.registerClass(kd);var vd=class extends Me{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,mz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return V(()=>{let o=Fe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=Ke(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return Ke(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};vd.className="UpSampling2D";Q.registerClass(vd);function lee(r,e,t=[1,1],o="valid",n,s){return V(()=>{n==null&&(n=to()),$t(n);let a=bd(r,n);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=ds(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=Ke(a,[0,3,1,2])),a})}var Cd=class extends Kp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ot(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o=lee(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ho(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=bo(t,this.kernelSize[0],this.padding,this.strides[0]),a=bo(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseRegularizer),e}};Cd.className="DepthwiseConv2D";Q.registerClass(Cd);function u0(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function c0(r,e,t,o=!1,n,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Gr(2,l));if(e=Ke(e,u),s!=null)throw new Se("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=lr(n,-1)),n=Ke(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=mr(e),h;n!=null&&(h=mr(n));for(let x=0;x<f;++x){let b=d[x],w=V(()=>r(b,m));if(n==null)p=w[0],m=w[1];else{let _=V(()=>{let k=h[x],D=rr(k).sub(k),T=w[0].mul(k).add(m[0].mul(D)),R=m.map((O,M)=>w[1][M].mul(k).add(O.mul(D)));return{output:T,newStates:R}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var Eo=class extends Me{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Xp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("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 At({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 Gr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Fx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)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 Se("Constants support is not implemented in RNN yet.");Fx(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new At({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Se("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new At({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Ao("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ht([o,n])):this.states_=[ht([o,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ht([o,n])):this.states_[0]=ht([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Ae(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!y.arraysEqual(s.shape,i))throw new z(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>Et(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=u0(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new At({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof oo){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Fe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=c0((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=ht(e.shape);return t=ge(t,[1,2]),t=Ea(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?Ax(t,[1,o]):t):this.cell.stateSize>1?[Ax(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 o=this.cell.getConfig();return this.getClassName()===Eo.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=no(n,o);return new e(Object.assign(t,{cell:s}))}};Eo.className="RNN";Q.registerClass(Eo);var Tl=class extends Me{},Yp=class extends Tl{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,Ut(this.units,"units"),this.activation=As(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Mu([1,Ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Mu([1,Ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 V(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=xn(P(e,a),this.kernel.read()):s=xn(e,this.kernel.read()),this.bias!=null&&(s=ho(s,this.bias.read())),i!=null&&(o=P(o,i));let l=ee(s,xn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ts(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";Q.registerClass(Yp);var Id=class extends Eo{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};Id.className="SimpleRNN";Q.registerClass(Id);var Zp=class extends Tl{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 z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=As(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=As(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Mu([1,Ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Mu([1,Ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(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 V(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let c=xn(e,this.kernel.read());this.useBias&&(c=ho(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=P(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=pr(p,[2*this.units,this.units],p.rank-1),d=xn(n,m),[h,g,x]=pr(c,3,c.rank-1),[b,w]=pr(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,b)),l=this.recurrentActivation.apply(ee(g,w));let _=xn(P(l,n),f);u=this.activation.apply(ee(x,_));let k=ee(P(i,n),P(ee(1,He(i)),u));return[k,k]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ts(this.activation),recurrentActivation:Ts(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Zp.className="GRUCell";Q.registerClass(Zp);var Nd=class extends Eo{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 Zp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Nd.className="GRU";Q.registerClass(Nd);var Al=class extends Tl{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,Ut(this.units,"units"),this.activation=As(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=As(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Mu([1,Ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Mu([1,Ns([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=Je(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends go{apply(l,u){let c=s.apply([a]),p=new zu().apply([a]),m=s.apply([a*2]);return DC(DC(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let m=xn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=P(n,i[0])),m=ee(m,xn(n,this.recurrentKernel.read())),this.useBias&&(m=ho(m,this.bias.read()));let[f,d,h,g]=pr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=ee(P(u,s),P(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=P(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ts(this.activation),recurrentActivation:Ts(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Al.className="LSTMCell";Q.registerClass(Al);var Sd=class extends Eo{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 Al(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Sd.className="LSTM";Q.registerClass(Sd);var Xp=class extends Tl{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 V(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Fx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{Is(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(no(s,o));return new e({cells:n})}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 o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return rd(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}Wp(t)}};Xp.className="StackedRNNCells";Q.registerClass(Xp);function Ra(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Dx(e(),t),a=()=>wl(s,e,o);return!n||n<=1?Et(a().clone()):Array(n).fill(void 0).map(a).map(l=>Et(l.clone()))}var uee=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var p0=class extends Eo{constructor(e){if(e.unroll)throw new Se("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Se("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new At({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}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 V(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=ht(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Ao("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_=[ht(s)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_[0]=ht(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`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()):Ae(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new z(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Et(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=bo(u,n[0],s,a[0],i[0]),m=bo(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};p0.className="ConvRNN2D";var Jp=class extends Al{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=Nl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Ut(l,"kernelSize")),this.strides=Nl(n||1,2,"strides"),this.strides.forEach(l=>Ut(l,"strides")),this.padding=s||"valid",ro(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=Nl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Ut(l,"dilationRate"))}build(e){var t;e=Je(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends go{apply(m,f){let d=u.apply([c]),h=Ar([c]),g=u.apply([c*2]);return Rp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(J,ie,ue)=>!ie||!ie[ue]?J:P(ie[ue],J),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),w=3,[_,k,D,T]=pr(this.kernel.read(),i,w),[R,O,M,G]=this.useBias?pr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,R,this.padding),p=this.inputConv(p,k,O,this.padding),m=this.inputConv(m,D,M,this.padding),f=this.inputConv(f,T,G,this.padding);let[j,U,H,q]=pr(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,j),g=this.recurrentConv(g,U),x=this.recurrentConv(x,H),b=this.recurrentConv(b,q);let X=this.recurrentActivation.apply(ee(c,h)),oe=this.recurrentActivation.apply(ee(p,g)),Y=ee(P(oe,a),P(X,this.activation.apply(ee(m,x)))),re=P(this.recurrentActivation.apply(ee(f,b)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=uee(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=Jr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ho(s,o,this.dataFormat):s}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Jp.className="ConvLSTM2DCell";Q.registerClass(Jp);var Td=class extends p0{constructor(e){let t=new Jp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Td.className="ConvLSTM2D";Q.registerClass(Td);var Qp=class extends Me{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,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return wl(()=>Dx(o,this.rate,s,this.seed),()=>o,n)}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()}};Qp.className="Dropout";Q.registerClass(Qp);var Ad=class extends Qp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ad.className="SpatialDropout1D";Q.registerClass(Ad);var Ed=class extends Me{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,Ut(this.units,"units"),this.activation=As(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ot(e.kernelConstraint),this.biasConstraint=Ot(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(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=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=Nx(this.activation.getClassName()),s;return n!=null?s=xn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=xn(o,this.kernel.read()),this.bias!=null&&(s=ho(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ts(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ed.className="Dense";Q.registerClass(Ed);var Dd=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],gn(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return bz(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Dd.className="Flatten";Q.registerClass(Dd);var $d=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.activation=As(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Ts(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};$d.className="Activation";Q.registerClass($d);var Rd=class extends Me{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Fe(e),xz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Rd.className="RepeatVector";Q.registerClass(Rd);var Fd=class extends Me{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new z("Can only specifiy one unknown dimension.");else s*=u}let i=gn(e);if(a!==null){if(s===0||i%s!=0)throw new z(o);n[a]=i/s}else if(i!==s)throw new z(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Fd.className="Reshape";Q.registerClass(Fd);var Od=class extends Me{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=Gr(1,e.dims.length+1);if(!y.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 At({ndim:this.dims.length+1})]}computeOutputShape(e){e=Je(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return Ke(Fe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Od.className="Permute";Q.registerClass(Od);var Pd=class extends Me{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 o=Fe(e),n=-1;return al(tn(o,this.maskValue),n)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=-1,s=!0,a=al(tn(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};Pd.className="Masking";Q.registerClass(Pd);var Md=class extends Me{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(yt(e.inputLength))}this.inputDim=e.inputDim,Ut(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ut(this.outputDim,"outputDim"),this.embeddingsInitializer=pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Ot(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 V(()=>this.maskZero?(e=Fe(e),tn(e,Ce(e))):null)}computeOutputShape(e){if(e=Je(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return o.dtype!=="int32"&&(o=Aa(o,"int32")),Ex(this.embeddings.read(),o.as1D()).reshape(Je(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:st(this.embeddingsRegularizer),activityRegularizer:st(this.activityRegularizer),embeddingsConstraint:Ft(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Md.className="Embedding";Q.registerClass(Md);var El=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Se}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 o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Je(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=hn(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&hn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Ns(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Ea(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(gn(c.slice(1))));f=Ke(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Gr(1,u).concat([0]);o.push(Ke(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ke(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Gr(0,i-1));a=Ke(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=hn(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:lr(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=gr(o,t[n]);return o})}},Ld=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return t})}};Ld.className="Add";Q.registerClass(Ld);var zd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=P(t,e[o]);return t})}};zd.className="Multiply";Q.registerClass(zd);var Bd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return P(1/e.length,t)})}};Bd.className="Average";Q.registerClass(Bd);var Vd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=eo(t,e[o]);return t})}};Vd.className="Maximum";Q.registerClass(Vd);var Gd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=ys(t,e[o]);return t})}};Gd.className="Minimum";Q.registerClass(Gd);var Wd=class extends El{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(y.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Wd.className="Concatenate";Q.registerClass(Wd);function jd(r,e){for(;r<0;)r+=e;return r}function cee(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Se("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Se("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return V(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var Ud=class extends El{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.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],o=e[1];if(t.length>3||o.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new z(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>jd(s,e[a].shape.length)):n=[jd(this.axes,t.shape.length),jd(this.axes,o.shape.length)],this.normalize&&(t=od(t,n[0]),o=od(o,n[1])),cee(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[jd(this.axes,e.length),jd(this.axes,t.length)],o}computeOutputShape(e){y.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(),o=e[1].slice();if(t.length>3||o.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ud.className="Dot";Q.registerClass(Ud);var Hd=class extends Me{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 V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return wl(()=>Fp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};Hd.className="GaussianNoise";Q.registerClass(Hd);var qd=class extends Me{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 V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.rate>0&&this.rate<1?wl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(Fp(o.shape,1,s))},()=>o,t.training||!1):o})}};qd.className="GaussianDropout";Q.registerClass(qd);var Kd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Fe(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 V(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return wl(()=>{let s=Fe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=po(bs(o),this.rate);u=Aa(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Fe(e),t.training||!1)}return e})}};Kd.className="AlphaDropout";Q.registerClass(Kd);function Xd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=Sw(r,e,t,o,n,s);else if(r.rank===3)a=Tw(r,e,t,o,n,s);else if(r.rank===4)a=Aw(r,e,t,o,n,s);else throw new Se(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function pee(r,e,t,o,n=.001){return V(()=>{let s=Qc(r,o),a=s.mean,i=s.variance;return[Xd(r,a,i,t,e,n),a,i]})}function mee(r,e,t,o,n=.001){return V(()=>{let s=Qc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Gr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[Xd(r,u,c,m,p,n),a,i]})}function fee(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Gr(0,r.rank-1))?pee(r,e,t,o,n):mee(r,e,t,o,n)}var Yd=class extends Me{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=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ot(e.betaConstraint),this.gammaConstraint=Ot(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Je(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new At({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training,n=Fe(e),s=n.shape,a=s.length,i=Gr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=mn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Gr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return Xd(n,b,w,_,k,this.epsilon)}else return Xd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=fee(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let k=1-_,D=b.read(),T=D.sub(w).mul(k);b.write(D.sub(T))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer),betaConstraint:Ft(this.betaConstraint),gammaConstraint:Ft(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Yd.className="BatchNormalization";Q.registerClass(Yd);var Zd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Je(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==hn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Fe(e),n=o.shape,s=n.length;return V(()=>{let a=!0,{mean:i,variance:l}=Qc(o,this.axis,a),u=mn(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),Xd(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Zd.className="LayerNormalization";Q.registerClass(Zd);function dee(r,e,t){return V(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=to()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. 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s==="max"?a=ha(r,e,t,i):a=ua(r,e,t,i),n==="channelsFirst"&&(a=Ke(a,[0,3,1,2])),a})}function o3(r,e,t,o,n,s){return V(()=>{$t(n),AC(s),ro(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=to()),s==null&&(s="max"),r=a0(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Qm(r,e,t,i):a=Gm(r,e,t,i),n==="channelsFirst"&&(a=Ke(a,[0,4,1,2,3])),a})}var m0=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(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 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t=bo(t,this.poolSize[0],this.padding,this.strides[0]),o=bo(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(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}},th=class extends f0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),Zx(e,t,o,n,s,"max")}};th.className="MaxPooling2D";Q.registerClass(th);var rh=class extends f0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),Zx(e,t,o,n,s,"avg")}};rh.className="AveragePooling2D";Q.registerClass(rh);var d0=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),ro(this.padding),this.inputSpec=[new At({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=bo(t,this.poolSize[0],this.padding,this.strides[0]),o=bo(o,this.poolSize[1],this.padding,this.strides[1]),n=bo(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(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}},oh=class extends d0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),o3(e,t,o,n,s,"max")}};oh.className="MaxPooling3D";Q.registerClass(oh);var nh=class extends d0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),o3(e,t,o,n,s,"avg")}};nh.className="AveragePooling3D";Q.registerClass(nh);var h0=class extends Me{constructor(e){super(e);this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=no(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gee:e.mergeMode,hee(this.mergeMode),e.weights)throw new Se("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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oy(this.node.rawAttrs,e,t);if(o.shape!=null)return ay(this.node.rawAttrs,e,t);if(o.type!=null)return sy(this.node.rawAttrs,e,t);if(o.list!=null){if(o.list.i!=null||o.list.f!=null)return ly(this.node.rawAttrs,e,t);if(o.list.s!=null)return uy(this.node.rawAttrs,e,t);if(o.list.shape!=null)return cy(this.node.rawAttrs,e,t);if(o.list.b!=null)return py(this.node.rawAttrs,e,t);if(o.list.type!=null)return iy(this.node.rawAttrs,e,t)}return t}};var y3=(r,e,t)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[ee(C("a",r,e,t),C("b",r,e,t))];case"AddN":return[vw(C("tensors",r,e,t))];case"FloorMod":case"Mod":return[tf(C("a",r,e,t),C("b",r,e,t))];case"Mul":return[P(C("a",r,e,t),C("b",r,e,t))];case"RealDiv":case"Div":return[me(C("a",r,e,t),C("b",r,e,t))];case"DivNoNan":return[qm(C("a",r,e,t),C("b",r,e,t))];case"FloorDiv":return[Hl(C("a",r,e,t),C("b",r,e,t))];case"Sub":return[ce(C("a",r,e,t),C("b",r,e,t))];case"Minimum":return[ys(C("a",r,e,t),C("b",r,e,t))];case"Maximum":return[eo(C("a",r,e,t),C("b",r,e,t))];case"Pow":return[Lr(C("a",r,e,t),C("b",r,e,t))];case"SquaredDifference":return[fu(C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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TypeError(`Node type ${r.op} is not implemented`)}};function Do(r,e,t=""){if(!(typeof r=="number"||typeof e=="number")){y.assert(r.length===e.length,()=>t+` Shapes ${r} and ${e} must match`);for(let o=0;o<r.length;o++){let n=r[o],s=e[o];y.assert(n<0||s<0||n===s,()=>t+` Shapes ${r} and ${e} must match`)}}}function w3(r){return!(typeof r=="number"||r.some(e=>e<0))}function em(r,e,t){let o=my(r,t),n=!w3(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=my(s.shape,o)}),!w3(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function my(r,e){if(typeof r=="number")return e;if(typeof e=="number")return r;if(r.length!==e.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);let t=[];for(let o=0;o<r.length;++o){let n=r[o],s=e[o];if(n>=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);t[o]=n>=0?n:s}return t}var W0=class{constructor(e,t,o,n,s,a,i){this.name=e,this.dtype=t,this.maxSize=o,this.elementShape=n,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=le(0),Et(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 o=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
|
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),Do(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),o.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(o.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);o.tensor=t,Et(t),o.written=!0,this.tensors[e]=o}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((o,n)=>this.write(o,t[n]))}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 n=0;n<this.size();n++)e.push(n)}if(e.length===0)return Pr([],[0].concat(this.elementShape));let o=this.readMany(e);return Do(this.elementShape,o[0].shape,"TensorArray shape mismatch: "),Bt(o,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Pr([],[0].concat(this.elementShape));let t=[];for(let n=0;n<this.size();n++)t.push(n);let o=this.readMany(t);return Do(this.elementShape,o[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`),Ze(o,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. ${t.shape[0]}`);let o=Math.max(...e);if(!this.dynamicSize&&o>=this.maxSize)throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`);this.writeMany(e,mr(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 o=0,n=e.map(l=>(o+=l,o));if(o!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${o}, 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 s=o===0?0:t.size/o,a=[];V(()=>{t=L(t,[1,o,s]);for(let l=0;l<e.length;++l){let u=l===0?0:n[l-1],c=[0,u,0],p=[1,e[l],s];a[l]=L(Re(t,c,p),this.elementShape)}return a});let i=[];for(let l=0;l<e.length;l++)i[l]=l;this.writeMany(i,a)}};var qu=class{constructor(e,t,o,n=-1){this.tensors=e,this.elementShape=t,this.elementDtype=o,e!=null&&e.forEach(s=>{if(o!==s.dtype)throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`);Do(t,s.shape,"TensorList shape mismatch: "),Et(s)}),this.idTensor=le(0),this.maxNumElements=n,Et(this.idTensor)}get id(){return this.idTensor.id}copy(){return new qu([...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,o=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(o!==-1&&this.tensors.length!==o)throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`);Do(e,this.elementShape,"TensorList shape mismatch: ");let n=em(this.elementShape,this.tensors,e);return V(()=>{let s=this.tensors.map(a=>L(a,n));return Bt(s,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 o=em(this.elementShape,this.tensors,e),n=this.tensors.pop();return Do(n.shape,e,"TensorList shape mismatch: "),L(n,o)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Do(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Et(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,o){if(o!==this.elementDtype)throw new Error(`Invalid data types; op elements ${o}, 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.`);Do(this.tensors[e].shape,t,"TensorList shape mismatch: ");let n=em(this.elementShape,this.tensors,t);return L(this.tensors[e],n)}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.`);Do(this.elementShape,t.shape,"TensorList shape mismatch: "),Et(t),this.tensors[e]=t}gather(e,t,o){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);Do(this.elementShape,o,"TensorList shape mismatch: "),e=e.slice(0,this.size());let n=em(this.elementShape,this.tensors,o);return e.length===0?Pr([],[0].concat(n)):V(()=>{let s=e.map(a=>L(this.tensors[a],n));return Bt(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Do(this.elementShape,t,"TensorList shape mismatch: ");let o=em(this.elementShape,this.tensors,t);return this.size()===0?Pr([],[0].concat(o)):V(()=>{let n=this.tensors.map(s=>L(s,o));return Ze(n,0)})}};function _3(r,e,t){let o=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==t)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t}`);let n=r.shape.slice(1);Do(n,e,"TensorList shape mismatch: ");let s=mr(r);return new qu(s,e,o)}function k3(r,e,t){return new qu([],r,e,t)}function v3(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n} vs. ${o})`);let s=new qu([],t,r.dtype,o),a=mr(r,0);return e.forEach((i,l)=>{s.setItem(i,a[l])}),s}function C3(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
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${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=my(s,t),i=o===0?0:r.size/o,l=V(()=>{let c=[];r=L(r,[1,o,i]);for(let p=0;p<e.length;++p){let m=p===0?0:n[p-1],f=[0,m,0],d=[1,e[p],i];c[p]=L(Re(r,f,d),a)}return r.dispose(),c}),u=new qu([],t,r.dtype,e.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var I3=async(r,e,t)=>{switch(r.op){case"If":case"StatelessIf":{let o=C("thenBranch",r,e,t),n=C("elseBranch",r,e,t),s=C("cond",r,e,t),a=C("args",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case"While":case"StatelessWhile":{let o=C("body",r,e,t),n=C("cond",r,e,t),s=C("args",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),l=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;l[0];){let c=u;u=await 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o=C("tensorArrayId",r,e,t),n=C("tensor",r,e,t),s=C("lengths",r,e,t),a=t.getTensorArray(o.id);return a.split(s,n),[a.idTensor]}case"TensorArraySizeV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return[le(n.size(),"int32")]}case"TensorArrayCloseV3":{let o=C("tensorArrayId",r,e,t),n=t.getTensorArray(o.id);return n.clearAndClose(),[n.idTensor]}case"TensorListSetItem":{let o=C("tensorListId",r,e,t),n=C("index",r,e,t),s=C("tensor",r,e,t),a=t.getTensorList(o.id);return a.setItem(n,s),[a.idTensor]}case"TensorListGetItem":{let o=C("tensorListId",r,e,t),n=C("index",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).getItem(n,s,a)]}case"TensorListScatterV2":case"TensorListScatter":{let o=C("indices",r,e,t),n=C("tensor",r,e,t),s=C("elementShape",r,e,t),a=C("numElements",r,e,t),i=v3(n,o,s,a);return t.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let o=C("elementShape",r,e,t),n=C("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=C(s,r,e,t),i=k3(o,n,a);return t.addTensorList(i),[i.idTensor]}case"TensorListGather":{let o=C("tensorListId",r,e,t),n=C("indices",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(o.id).gather(n,a,s)]}case"TensorListStack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=C("numElements",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case"TensorListFromTensor":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=_3(o,n,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":{let o=C("tensorListId",r,e,t),n=t.getTensorList(o.id),s=C("dtype",r,e,t),a=C("elementShape",r,e,t);return[n.concat(s,a)]}case"TensorListPushBack":{let o=C("tensorListId",r,e,t),n=C("tensor",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case"TensorListPopBack":{let o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("lengths",r,e,t),a=C3(o,s,n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function N3(r,e,t){let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=o==="fusedbatchnorm",l=C("numArgs",r,e,t);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(i)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=C("strides",r,e,t),c=ph(r,e,t),p=C("dataFormat",r,e,t).toUpperCase(),m=C("dilations",r,e,t),[f,d]=C("args",r,e,t),h=C("leakyreluAlpha",r,e,t);return{stride:u,pad:c,dataFormat:p,dilations:m,biasArg:f,preluArg:d,activationFunc:n,leakyreluAlpha:h}}var S3=(r,e,t)=>{switch(r.op){case"Conv1D":{let o=C("stride",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilation",r,e,t);return[Yl(C("x",r,e,t),C("filter",r,e,t),o,n,s,a)]}case"Conv2D":{let o=C("strides",r,e,t),n=ph(r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[Jr(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=N3(r,e,t);return[rn.conv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=N3(r,e,t);return[rn.depthwiseConv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let o=C("outputShape",r,e,t),n=C("strides",r,e,t),s=ph(r,e,t);return[Zl(C("x",r,e,t),C("filter",r,e,t),o,[n[1],n[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let o=C("strides",r,e,t),n=ph(r,e,t),s=C("dilations",r,e,t),a=C("dataFormat",r,e,t).toUpperCase();return[ds(C("input",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,a,[s[1],s[2]])]}case"Conv3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[jm(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2],o[3]],n,s,[a[1],a[2],a[3]])]}case"AvgPool":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[ua(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n)]}case"MaxPool":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[ha(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n)]}case"MaxPoolWithArgmax":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t),a=C("includeBatchInIndex",r,e,t),{result:i,indexes:l}=Hw(C("x",r,e,t),[s[1],s[2]],[o[1],o[2]],n,a);return[i,l]}case"AvgPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Gm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"MaxPool3D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Qm(C("x",r,e,t),[s[1],s[2],s[3]],[o[1],o[2],o[3]],n)]}case"Dilation2D":{let o=C("strides",r,e,t),n=C("pad",r,e,t),s=C("dilations",r,e,t),a=o[1],i=o[2],l=s[1],u=s[2];return[Hm(C("x",r,e,t),C("filter",r,e,t),[a,i],n,[l,u],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var T3=(r,e,t)=>{switch(r.op){case"Fill":{let o=C("shape",r,e,t),n=C("dtype",r,e,t),s=C("value",r,e,t);return[ma(o,s,n)]}case"LinSpace":{let o=C("start",r,e,t),n=C("stop",r,e,t),s=C("num",r,e,t);return[Bw(o,n,s)]}case"Multinomial":{let o=C("logits",r,e,t),n=C("numSamples",r,e,t),s=C("seed",r,e,t);return[qw(o,n,s)]}case"OneHot":{let o=C("indices",r,e,t),n=C("depth",r,e,t),s=C("onValue",r,e,t),a=C("offValue",r,e,t);return[fs(o,n,s,a)]}case"Ones":return[Ar(C("shape",r,e,t),C("dtype",r,e,t))];case"OnesLike":return[rr(C("x",r,e,t))];case"RandomUniform":return[bs(C("shape",r,e,t),C("minval",r,e,t),C("maxval",r,e,t),C("dtype",r,e,t))];case"Range":{let o=C("start",r,e,t),n=C("stop",r,e,t),s=C("step",r,e,t);return[tp(o,n,s,C("dtype",r,e,t))]}case"TruncatedNormal":{let o=C("shape",r,e,t),n=C("mean",r,e,t),s=C("stdDev",r,e,t),a=C("seed",r,e,t);return[du(o,n,s,C("dtype",r,e,t),a)]}case"Zeros":return[ht(C("shape",r,e,t),C("dtype",r,e,t))];case"ZerosLike":return[Ce(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function j0(r,e,t){let o=C("boxes",r,e,t),n=C("scores",r,e,t),s=C("maxOutputSize",r,e,t),a=C("iouThreshold",r,e,t),i=C("scoreThreshold",r,e,t),l=C("softNmsSigma",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}}var A3=async(r,e,t)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:l}=j0(r,e,t),u=await oi.nonMaxSuppressionWithScoreAsync(o,n,s,a,i,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=j0(r,e,t),l=C("padToMaxOutputSize",r,e,t),u=await oi.nonMaxSuppressionPaddedAsync(o,n,s,a,i,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i}=j0(r,e,t);return[await oi.nonMaxSuppressionAsync(o,n,s,a,i)]}case"Where":{let o=ne(C("condition",r,e,t),"bool"),n=[await ff(o)];return o.dispose(),n}case"ListDiff":return s_(C("x",r,e,t),C("y",r,e,t));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var E3=(r,e,t)=>{switch(r.op){case"TopKV2":{let o=C("x",r,e,t),n=C("k",r,e,t),s=C("sorted",r,e,t),a=pf(o,n,s);return[a.values,a.indices]}case"Unique":{let o=C("x",r,e,t),n=op(o);return[n.values,n.indices]}case"UniqueV2":{let o=C("x",r,e,t),n=C("axis",r,e,t),s=op(o,n);return[s.values,s.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var D3=(r,e,t)=>{switch(r.op){case"Const":return e[r.name];case"PlaceholderWithDefault":let o=C("default",r,e,t);return[br(r.name,e,t)||o];case"Placeholder":return[br(r.name,e,t)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=C("x",r,e,t);return[Ds(u)]}case"IdentityN":return C("x",r,e,t).map(u=>Ds(u));case"Snapshot":let n=C("x",r,e,t);return[Ds(n)];case"Shape":return[Vt(C("x",r,e,t).shape,"int32")];case"ShapeN":return C("x",r,e,t).map(u=>Vt(u.shape));case"Size":return[le(C("x",r,e,t).size,"int32")];case"Rank":return[le(C("x",r,e,t).rank,"int32")];case"NoOp":return[le(1)];case"Print":let s=C("x",r,e,t),a=C("data",r,e,t),i=C("message",r,e,t),l=C("summarize",r,e,t);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let u=0;u<a.length;u++)console.log(Array.prototype.slice.call(a[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var U0=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=le(0),this.tensorMap=new Map,Et(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return le(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),V(()=>{let n=mr(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i<s;i++){let l=o[i],u=n[i];Et(u),this.tensorMap.set(l,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return V(()=>{let n=[];for(let s=0;s<o.length;s++){let a=o[s],i=this.findWithDefault(a,t);n.push(i)}return Bt(n)})}findWithDefault(e,t){let o=this.tensorMap.get(e);return o!=null?o:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}};var $3=async(r,e,t,o)=>{switch(r.op){case"HashTable":case"HashTableV2":{let n=C("keyDType",r,e,t),s=C("valueDType",r,e,t),a=new U0(n,s);return o.addHashTable(r.name,a),[a.handle]}case"LookupTableImport":case"LookupTableImportV2":{let n=C("tableHandle",r,e,t,o),s=C("keys",r,e,t),a=C("values",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case"LookupTableFind":case"LookupTableFindV2":{let n=C("tableHandle",r,e,t,o),s=C("keys",r,e,t),a=C("defaultValue",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case"LookupTableSize":case"LookupTableSizeV2":{let n=C("tableHandle",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var R3=(r,e,t)=>{switch(r.op){case"ResizeBilinear":{let o=C("images",r,e,t),n=C("size",r,e,t),s=C("alignCorners",r,e,t),a=C("halfPixelCenters",r,e,t);return[oi.resizeBilinear(o,[n[0],n[1]],s,a)]}case"ResizeNearestNeighbor":{let o=C("images",r,e,t),n=C("size",r,e,t),s=C("alignCorners",r,e,t),a=C("halfPixelCenters",r,e,t);return[oi.resizeNearestNeighbor(o,[n[0],n[1]],s,a)]}case"CropAndResize":{let o=C("image",r,e,t),n=C("boxes",r,e,t),s=C("boxInd",r,e,t),a=C("cropSize",r,e,t),i=C("method",r,e,t),l=C("extrapolationValue",r,e,t);return[oi.cropAndResize(o,n,s,a,i,l)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var F3=(r,e,t)=>{switch(r.op){case"Equal":return[Io(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[tn(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[tr(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[po(C("a",r,e,t),C("b",r,e,t))];case"Less":return[tu(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[zo(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[gr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[da(C("a",r,e,t))];case"LogicalOr":return[nu(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[Dt(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var O3=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[je(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[Ke(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[rn.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var P3=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Jo(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"FusedBatchNormV3":return[Jo(C("x",r,e,t),C("mean",r,e,t),C("variance",r,e,t),C("offset",r,e,t),C("scale",r,e,t),C("epsilon",r,e,t))];case"LRN":return[Ym(C("x",r,e,t),C("radius",r,e,t),C("bias",r,e,t),C("alpha",r,e,t),C("beta",r,e,t))];case"Softmax":return[ya(C("x",r,e,t))];case"LogSoftmax":return[ou(C("x",r,e,t))];case"SparseToDense":return[xg(C("sparseIndices",r,e,t),C("outputShape",r,e,t),C("sparseValues",r,e,t),C("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var M3=(r,e,t)=>{switch(r.op){case"Max":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[cr(C("x",r,e,t),a,i)]}case"Mean":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[dt(C("x",r,e,t),a,i)]}case"Min":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ei(C("x",r,e,t),a,i)]}case"Sum":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ge(C("x",r,e,t),a,i)]}case"All":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ql(C("x",r,e,t),a,i)]}case"Any":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[al(C("x",r,e,t),a,i)]}case"ArgMax":{let a=C("axis",r,e,t);return[ll(C("x",r,e,t),a)]}case"ArgMin":{let a=C("axis",r,e,t);return[Pm(C("x",r,e,t),a)]}case"Prod":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[su(C("x",r,e,t),a,i)]}case"Cumsum":{let a=C("axis",r,e,t),i=C("exclusive",r,e,t),l=C("reverse",r,e,t);return[Ql(C("x",r,e,t),a,i,l)]}case"Bincount":let o=C("x",r,e,t),n=C("weights",r,e,t),s=C("size",r,e,t);return[Ew(o,n,s)];case"DenseBincount":{let a=C("x",r,e,t),i=C("weights",r,e,t),l=C("size",r,e,t),u=C("binaryOutput",r,e,t);return[Ow(a,i,l,u)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var L3=(r,e,t)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=C("n",r,e,t),n=C("axis",r,e,t),s=C("tensors",r,e,t);return 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match");return u?l:L(l,s)});return[Bt(i,o)]});case"Unpack":{let o=C("axis",r,e,t),n=C("tensor",r,e,t);return mr(n,o)}case"Tile":{let o=C("reps",r,e,t);return[Lo(C("x",r,e,t),o)]}case"Split":case"SplitV":{let o=C("axis",r,e,t),n=C("numOrSizeSplits",r,e,t),s=C("x",r,e,t);return pr(s,n,o)}case"ScatterNd":{let o=C("indices",r,e,t),n=C("values",r,e,t),s=C("shape",r,e,t);return[OT(o,n,s)]}case"GatherNd":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[MT(o,n)]}case"SparseToDense":{let o=C("sparseIndices",r,e,t),n=C("outputShape",r,e,t),s=C("sparseValues",r,e,t),a=C("defaultValue",r,e,t);return[xg(o,s,n,s.dtype===a.dtype?a:ne(a,s.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var z3=(r,e,t)=>{switch(r.op){case"FFT":return[ba(C("x",r,e,t))];case"IFFT":return[ti(C("x",r,e,t))];case"RFFT":return[wa(C("x",r,e,t))];case"IRFFT":return[mu(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var B3=(r,e,t)=>{switch(r.op){case"Cast":return[ne(C("x",r,e,t),C("dtype",r,e,t))];case"ExpandDims":{let o=C("axis",r,e,t);return[lr(C("x",r,e,t),o)]}case"Squeeze":{let o=C("axis",r,e,t);return[No(C("x",r,e,t),o)]}case"Reshape":return[L(C("x",r,e,t),C("shape",r,e,t))];case"MirrorPad":return[ef(C("x",r,e,t),C("padding",r,e,t),C("mode",r,e,t))];case"PadV2":case"Pad":return[Mr(C("x",r,e,t),C("padding",r,e,t),C("constantValue",r,e,t))];case"SpaceToBatchND":{let o=C("blockShape",r,e,t),n=C("paddings",r,e,t);return[ga(C("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=C("blockShape",r,e,t),n=C("crops",r,e,t);return[ca(C("x",r,e,t),o,n)]}case"DepthToSpace":{let o=C("blockSize",r,e,t),n=C("dataFormat",r,e,t).toUpperCase();return[Um(C("x",r,e,t),o,n)]}case"BroadcastTo":return[ul(C("x",r,e,t),C("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function H0(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return V(()=>y3(s,a,i));case"basic_math":return V(()=>b3(s,a,i));case"control":return I3(s,a,i);case"convolution":return V(()=>S3(s,a,i));case"creation":return V(()=>T3(s,a,i));case"dynamic":return A3(s,a,i);case"evaluation":return V(()=>E3(s,a,i));case"image":return V(()=>R3(s,a,i));case"graph":return V(()=>D3(s,a,i));case"logical":return V(()=>F3(s,a,i));case"matrices":return V(()=>O3(s,a,i));case"normalization":return V(()=>P3(s,a,i));case"reduction":return V(()=>M3(s,a,i));case"slice_join":return V(()=>L3(s,a,i));case"spectral":return V(()=>z3(s,a,i));case"transformation":return V(()=>B3(s,a,i));case"hash_table":return $3(s,a,i,o);case"custom":let l=Qx(s.op);if(l&&l.customExecutor)return l.customExecutor(new G0(s,a,i));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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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 K0(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>so(m)[0]),c=[];o!=null&&(c=o.map(m=>so(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((q0(m)||mre(m)||fre(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function V3(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>so(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var dre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],hre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],gre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function q0(r){return dre.indexOf(r.op)>=0}function mre(r){return hre.indexOf(r.op)>=0}function fre(r){return gre.indexOf(r.op)>=0}var tm=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(o=>{this._functionExecutorMap[o]=new tm(e.functions[o],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(o=>e[o].map(n=>n.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 o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=K0(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${l}]. Missing the following inputs: [${n}]`)}return V3(this.graph,this.weightMap,o)}execute(e,t){e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(p=>this.graph.nodes[so(p)[0]]),s=t.map(p=>so(p)[0]),a=s.map(p=>this.graph.nodes[p]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),l=this.compiledMap.get(i);l==null&&(l=this.compile(e,a),this.compiledMap.set(i,l));let u={},c={};return V(()=>{let p=new fy(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(e).forEach(h=>{let[g,x]=so(h),b=[];b[x]=e[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<l.length;h++){let g=l[h];if(!m[g.name]){let x=H0(g,m,p,this._resourceManager);if(y.isPromise(x))throw new Error(`The execution of the op '${g.op}' returned a promise. 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You can use model.execute() instead.");let b=l.filter(w=>!q0(w)&&!br(w.name,d,t)).map(w=>w.name);if(b.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(e,t,o,n,s,a,i,l,u){let c=[];for(;t.length>0;){let p=t.pop();o.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,n,o)&&([m]=Es(p.node.name,o)),n[p.node.name]==null){let f=H0(p.node,n,o,this._resourceManager);m||([m]=Es(p.node.name,o));let d=o.currentContext;y.isPromise(f)?c.push(f.then(h=>(n[m]=h,o.currentContext=d,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u),h))):(n[m]=f,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u))}else this.processChildNodes(p.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=Es(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!br(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!br(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=so(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o 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Array(e),o=this.length();for(let n=0;n<o;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=o}};rm.INITIAL_CAPACITY=32;function sI(r){return new uB(r)}function fh(r){return new cB(r)}function pB(r,e){return new iI(r,e)}function fB(r,e=Fa.FAIL){return new mB(r,e)}var Kt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],o=await e.next();for(;!o.done;)t.push(o.value),o=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),o=e(t.value);for(;!t.done&&o;)t=await this.next(),o=e(t.value)}handleErrors(e){return new wB(this,e)}filter(e){return new yB(this,e)}map(e){return new bB(this,e)}mapAsync(e){return new aI(this,e)}serialMapAsync(e){return new aI(this,e).serial()}flatmap(e){return new _B(this,e)}async 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Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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 Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},gB=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},xB=class extends Kt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},yB=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Ae(e.value)}}},bB=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Yo.getTensorsInContainer(e.value),o=this.transform(e.value),n=Yo.getTensorsInContainer(o);for(let s of t)Yo.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},wB=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},aI=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Yo.getTensorsInContainer(e.value),o=await this.transform(e.value),n=Yo.getTensorsInContainer(o);for(let s of t)Yo.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},om=class extends Kt{constructor(){super();this.outputQueue=new rm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},_B=class extends om{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Yo.getTensorsInContainer(e.value),o=this.transform(e.value),n=Yo.getTensorsInContainer(o);this.outputQueue.pushAll(o);for(let s of t)Yo.isTensorInList(s,n)||s.dispose();return!0}},iI=class extends Kt{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 o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.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}},Fa;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Fa||(Fa={}));var mB=class extends Kt{constructor(e,t=Fa.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,o=0;function n(a){return a instanceof 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|
|
${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),wo(async()=>(await o.iterator()).columnMajorBatch(e,t,Are),n)}concatenate(e){let t=this,o;return this.size===Infinity||e.size===Infinity?o=Infinity:this.size!=null&&e.size!=null?o=this.size+e.size:o=null,wo(async()=>(await t.iterator()).concatenate(await e.iterator()),o)}filter(e){let t=this,o;return this.size===Infinity?o=Infinity:o=null,wo(async()=>(await t.iterator()).filter(n=>V(()=>e(n))),o)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return wo(async()=>(await t.iterator()).map(o=>V(()=>e(o))),this.size)}mapAsync(e){let t=this;return wo(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return wo(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,o;return this.size!=null&&e>0?o=this.size*e:e===0?o=0:this.size!=null&&(e===void 0||e<0)?o=Infinity:o=null,wo(async()=>{let n=fh(async()=>({value:await t.iterator(),done:!1}));return pB(n.take(e))},o)}skip(e){let t=this,o;return this.size!=null&&e>=0&&this.size>=e?o=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?o=0:o=null,wo(async()=>(await t.iterator()).skip(e),o)}shuffle(e,t,o=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,s=vB.alea(t||y.now().toString());return wo(async()=>{let a=s.int32();return o&&(a+=s.int32()),(await n.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,o;return this.size!=null&&this.size>e?o=e:this.size!=null&&this.size<=e?o=this.size:o=null,wo(async()=>(await t.iterator()).take(e),o)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};mi.MAX_BUFFER_SIZE=1e4;function wo(r,e=null){return new class extends mi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function CB(r){return wo(async()=>sI(r),r.length)}function IB(r){if(!Dl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return wo(async()=>{let t=await gy(r,o=>{if(o instanceof mi)return{value:o.iterator(),recurse:!1};if(Dl(o))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return fB(t,Fa.SHORTEST)},e)}function Are(r){if(r===null)return null;let e=r[0];return iB(e)?{value:Ere(r),recurse:!1}:{value:null,recurse:!0}}function Ere(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Bt(r):Pr(r)}var dh=class extends mi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n))}};var xy='"',hh=Symbol("out"),NB=Symbol("field"),yy=Symbol("quote"),uI=Symbol("quoteafterquote"),SB=Symbol("quoteinquote"),gh=class extends mi{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 dh(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.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&&y.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((n,s)=>(n[s]=n[s]+1||1,n),{}),o=Object.keys(t).filter(n=>t[n]>1);if(y.assert(o.length===0,()=>"Duplicate column names found: "+o.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" 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 o=t.value;return this.parseRow(o,!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),o={},n={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?n[a]=u:o[a]=u}}return Object.keys(n).length===0?o:{xs:o,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let o=[],n=0,s=e.length,a=hh;for(let i=0;i<s;i++)switch(a){case hh:switch(e.charAt(i)){case xy:n=i+1,a=yy;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;o.push(""),a=hh;break;default:a=NB,n=i;break}break;case NB:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i)),a=hh,n=i+1;break;default:}break;case yy:switch(e.charAt(i)){case xy:a=uI;break;default:}break;case uI:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i-1)),a=hh,n=i+1;break;case xy:a=yy;break;default:a=SB;break}break;case SB:switch(e.charAt(i)){case xy:a=yy;break;default:}break;default:}if(a===uI?o.push(e.substring(n,s-1)):o.push(e.substring(n)),t&&o.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var xh=class extends Kt{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(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new xh(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(o){throw new Error(`Error thrown while initializing video stream: ${o.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,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({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,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Pr(o,t)}};var yh=class extends Kt{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=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=ri([a,s,l,i],[1,4])}else this.cropBox=ri([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().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 o=new yh(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(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=rg.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=lr(ne(e,"float32"),0),o;o=oi.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return L(o,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var bh=class{};var by=class extends Kt{split(e){return new TB(this,e)}},TB=class extends by{constructor(e,t){super();this.upstream=e,this.impl=new AB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},AB=class extends om{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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 o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var cI=class extends Kt{decodeUTF8(){return new DB(this)}},DB=class extends by{constructor(e){super();this.upstream=e,this.impl=new $B(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$B=class extends om{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=EB();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 o;return W().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var wh=class extends cI{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(W().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,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return o(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>o(new Error("Aborted")),s.onerror=i=>o(new Error(i.type));let a=this.file.slice(this.offset,n);s.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function RB(r,e={}){let t,o;typeof r=="string"?t=r:(t=r.url,o=Dre(r));let n=await y.fetch(t,o);if(n.ok){let s=new Uint8Array(await n.arrayBuffer());return new wh(s,e)}else throw new Error(n.statusText)}var Dre=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function wy(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var _h=class extends bh{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(wy(this.input)&&W().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new wh(this.input,this.options)}};var kh=class extends bh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return wy(this.url)?new _h(this.url,this.fileOptions).iterator():RB(this.url,this.fileOptions)}};function FB(r,e={}){return new gh(new kh(r),e)}function OB(r){let e=fh(r);return wo(async()=>e)}function PB(r){return wo(async()=>{let e=await r();return fh(()=>e.next())})}async function MB(r,e){return yh.create(r,e)}async function LB(r){return xh.create(r)}var zB="3.3.0";var SGt={tfjs:(dm==null?void 0:dm.version)||void 0,"tfjs-core":(hm==null?void 0:hm.version)||void 0,"tfjs-data":(gm==null?void 0:gm.version)||void 0,"tfjs-layers":(xm==null?void 0:xm.version)||void 0,"tfjs-converter":(ym==null?void 0:ym.version)||void 0,"tfjs-backend-cpu":A_||void 0,"tfjs-backend-webgl":zk||void 0,"tfjs-backend-wasm":wC||void 0};export{Os as Abs,yi as Acos,bi as Acosh,ip as AdadeltaOptimizer,ap as AdagradOptimizer,lp as AdamOptimizer,up as AdamaxOptimizer,Fo as Add,kn as AddN,mc as All,fc as Any,vn as ArgMax,Ga as ArgMin,wi as Asin,_i as Asinh,ki as Atan,Ci as Atan2,vi as Atanh,Cn as AvgPool,Wa as AvgPool3D,hc as AvgPool3DGrad,dc as AvgPoolGrad,_x as BackendWasm,In as BatchMatMul,ja as BatchToSpaceND,gc as Bincount,sS as BroadcastTo,_0 as Callback,FC as CallbackList,Ho as Cast,Nn as Ceil,qo as ClipByValue,xc as Complex,Ua as ComplexAbs,Ps as Concat,Sn as Conv2D,yc as Conv2DBackpropFilter,Tn as Conv2DBackpropInput,Ha as Conv3D,bc as Conv3DBackpropFilterV2,wc as Conv3DBackpropInputV2,An as Cos,Ii as Cosh,Ni as CropAndResize,En as Cumsum,PC as CustomCallback,Va as DataStorage,_c as DenseBincount,Si as DepthToSpace,Dn as DepthwiseConv2dNative,kc as DepthwiseConv2dNativeBackpropFilter,vc as DepthwiseConv2dNativeBackpropInput,Cc as Diag,qa as Dilation2D,Cm as Dilation2DBackpropFilter,vm as Dilation2DBackpropInput,Vb as ENV,k0 as EarlyStopping,Ti as Elu,Ic as EluGrad,Hh as Environment,Ei as Equal,Ai as Erf,Rn as Exp,Ms as ExpandDims,Di as Expm1,Nc as FFT,Ka as Fill,$i as FlipLeftRight,Fn as Floor,On as FloorDiv,Im as FromPixels,Pn as FusedBatchNorm,Xs as FusedConv2D,Ys as FusedDepthwiseConv2D,tx as GPGPUContext,Ri as GatherNd,Ls as GatherV2,Y0 as GraphModel,Fi as Greater,Mn as GreaterEqual,OC as History,Sc as IFFT,Ko as Identity,Tc as Imag,At as InputSpec,Oi as IsFinite,Pi as IsInf,Mi as IsNan,$s as KernelBackend,Xa as LRN,Ec as LRNGrad,Ox as LayerVariable,Wo as LayersModel,Ln as LeakyRelu,Li as Less,zi as LessEqual,Ac as LinSpace,zn as Log,Bi as Log1p,iS as LogSoftmax,Vi as LogicalAnd,Ll as LogicalNot,zl as LogicalOr,wu as MathBackendCPU,Au as MathBackendWebGL,Bn as Max,Gn as MaxPool,Ya as MaxPool3D,$c as MaxPool3DGrad,Dc as MaxPoolGrad,Rc as MaxPoolWithArgmax,Vn as Maximum,Wn as Mean,jn as Min,Un as Minimum,Za as MirrorPad,Gi as Mod,cp as MomentumOptimizer,Fc as Multinomial,Hn as Multiply,zs as Neg,ji as NonMaxSuppressionV3,Ui as NonMaxSuppressionV4,Hi as NonMaxSuppressionV5,Wi as NotEqual,wS as OP_SCOPE_SUFFIX,qn as OneHot,Bs as OnesLike,zr as Optimizer,Vs as Pack,Kn as PadV2,zre as Pool,Xn as Pow,Yn as Prelu,qi as Prod,pp as RMSPropOptimizer,Eo as RNN,Ja as Range,Kb as Rank,Oc as Real,$n as RealDiv,Ki as Reciprocal,Gt as Reduction,Zn as Relu,Qn as Relu6,Gs as Reshape,Jn as ResizeBilinear,Mc as ResizeBilinearGrad,Qa as ResizeNearestNeighbor,Pc as ResizeNearestNeighborGrad,es as Reverse,oa as RotateWithOffset,ts as Round,rs as Rsqrt,pl as SGDOptimizer,Xi as ScatterNd,Ws as Select,Yi as Selu,$a as Sequential,ns as Sigmoid,Ji as Sign,os as Sin,Zi as Sinh,js as Slice,as as Softmax,Qi as Softplus,el as SpaceToBatchND,Lc as SparseToDense,Us as SplitV,ss as Sqrt,tl as Square,ls as SquaredDifference,Xo as Step,ea as StridedSlice,us as Sub,is as Sum,oo as SymbolicTensor,ta as Tan,cs as Tanh,Ve as Tensor,lt as TensorBuffer,Oo as Tile,ra as TopK,zc as Transform,ps as Transpose,Bc as Unique,Hs as Unpack,rl as UnsortedSegmentSum,nl as Variable,qs as ZerosLike,Ks as _FusedMatMul,It as abs,Fm as acos,Om as acosh,ee as add,vw as addN,ql as all,al as any,ll as argMax,Pm as argMin,Mm as asin,Lm as asinh,zm as atan,Bm as atan2,Vm as atanh,ua as avgPool,Gm as avgPool3d,hT as backend,N as backend_util,JW as basicLSTMCell,Jo as batchNorm,Sw as batchNorm2d,Tw as batchNorm3d,Aw as batchNorm4d,ca as batchToSpaceND,Ew as bincount,JCe as booleanMaskAsync,ul as broadcastTo,rg as browser,ve as buffer,Ute as callbacks,ne as cast,Wm as ceil,ar as clipByValue,Po as clone,Co as complex,Ze as concat,Dw as concat1d,$w as concat2d,Rw as concat3d,Fw as concat4d,iz as constraints,Yl as conv1d,Jr as conv2d,Zl as conv2dTranspose,jm as conv3d,bj as conv3dTranspose,Ure as copyRegisteredKernels,pa as cos,Jl as cosh,yg as cosineWindow,Ql as cumsum,Qr as customGrad,pI as data,Ow as denseBincount,kw as deprecationWarn,Um as depthToSpace,ds as depthwiseConv2d,qte as deregisterOp,jl as device_util,Sj as diag,Hm as dilation2d,kie as disableDeprecationWarnings,Ae as dispose,vie as disposeVariables,me as div,qm as divNoNan,Pw as dot,zT as dropout,hs as elu,_ie as enableDebugMode,wie as enableProdMode,BT as enclosingPowerOfTwo,Zo as engine,W as env,Io as equal,Km as erf,Zt as exp,lr as expandDims,Xm as expm1,Jc as eye,ba as fft,ma as fill,Aie as findBackend,Eie as findBackendFactory,gs as floor,Hl as floorDiv,fD as forceHalfFloat,rn as fused,Qo as gather,MT as gatherND,og as gather_util,Sie as getBackend,Ub as getGradient,Sm as getKernel,qh as getKernelsForBackend,w2 as gpgpu_util,r4 as grad,o4 as grads,tr as greater,po as greaterEqual,ti as ifft,eu as imag,oi as image,lIe as inTopKAsync,Sz as initializers,UC as input,Tr as io,mu as irfft,Mw as isFinite,Lw as isInf,zw as isNaN,Et as keep,$r as kernel_impls,n3 as layers,fa as leakyRelu,tu as less,zo as lessEqual,g1 as linalg,Bw as linspace,bre as loadGraphModel,nee as loadLayersModel,Ym as localResponseNormalization,ur as log,ru as log1p,Vw as logSigmoid,ou as logSoftmax,Jm as logSumExp,gr as logicalAnd,da as logicalNot,nu as logicalOr,Uw as logicalXor,IRe as losses,je as matMul,JS as math,cr as max,ha as maxPool,Qm as maxPool3d,Hw as maxPoolWithArgmax,eo as maximum,dt as mean,Rm as memory,u3 as metrics,ei as min,ys as minimum,ef as mirrorPad,tf as mod,ree as model,c3 as models,Qc as moments,C0e as movingAverage,P as mul,$4 as multiRNNCell,qw as multinomial,He as neg,df as nextFrame,np as norm,tn as notEqual,fs as oneHot,Ar as ones,rr as onesLike,S as op,M4 as outerProduct,Mr as pad,B4 as pad1d,G4 as pad2d,j4 as pad3d,H4 as pad4d,Kw as pool,Lr as pow,xa as prelu,mw as print,su as prod,Cie as profile,tU as rand,uU as randomGamma,dg as randomNormal,bs as randomUniform,tp as range,Nie as ready,cl as real,rf as reciprocal,Xc as registerBackend,see as registerCallbackConstructor,aS as registerGradient,Bl as registerKernel,Hte as registerOp,p3 as regularizers,Er as relu,au as relu6,Tie as removeBackend,L as reshape,qt as reverse,yU as reverse1d,wU as reverse2d,kU as reverse3d,CU as reverse4d,wa as rfft,of as round,lu as rsqrt,le as scalar,OT as scatterND,ng as scatter_util,uu as selu,nf as separableConv2d,oee as sequential,Q as serialization,wW as setBackend,Die as setPlatform,AJ as setWasmPath,EJ as setWasmPaths,j_ as setWebGLContext,s_ as setdiff1dAsync,Mg as shared,Zr as sigmoid,sf as sign,CRe as signal,cu as sin,pu as sinh,Re as slice,af as slice1d,hg as slice2d,lf as slice3d,rp as slice4d,er as slice_util,ya as softmax,xs as softplus,ga as spaceToBatchND,xg as sparseToDense,vRe as spectral,pr as split,gt as sqrt,Oe as square,fu as squaredDifference,No as squeeze,Bt as stack,ws as step,uf as stridedSlice,ce as sub,ge as sum,Gl as sumOutType,cf as tan,Qs as tanh,Pr as tensor,Vt as tensor1d,ri as tensor2d,gw as tensor3d,YU as tensor4d,ZU as tensor5d,JU as tensor6d,Yo as tensor_util,mT as test_util,V as tidy,Lo as tile,Iie as time,pf as topk,bu as train,Ke as transpose,du as truncatedNormal,op as unique,jre as unregisterGradient,Wre as unregisterKernel,mf as unsortedSegmentSum,mr as unstack,ir as upcastType,y as util,n4 as valueAndGrad,s4 as valueAndGrads,i_ as variable,pg as variableGrads,SGt as version,wre as version_converter,bW as version_core,A_ as version_cpu,cd as version_layers,wC as version_wasm,zk as version_webgl,kXe as webgl,h2 as webgl_util,Dt as where,ff as whereAsync,ht as zeros,Ce as zerosLike};
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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.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2019 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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|
* you may not use this file except in compliance with the License.
|
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
|
* Unless required by applicable law or agreed to in writing, software
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|
* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
|
* limitations under the License.
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|
* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the License);
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* you may not use this file except in compliance with the License.
|
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* You may obtain a copy of the License at
|
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*
|
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
|
* 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.
|
|
* =============================================================================
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*/
|
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/**
|
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* @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.
|
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* You may obtain a copy of the License at
|
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
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* limitations under the License.
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* =============================================================================
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*/
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/** @license See the LICENSE file. */
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//# sourceMappingURL=tfjs.esm.js.map
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