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.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let o=this.backend.numDataIds(),s=0;n.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=o-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],o=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=qb(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(qb(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=zc(d,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let w=this.backend.numDataIds();l=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let b=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,w,b);let k=b.map(_=>{if(_.rank!=null)return _;let{dataId:A,shape:N,dtype:$}=_;return this.makeTensorFromDataId(A,N,$)});if(o){let _=this.getTensorsForGradient(d,h,k);n=this.saveTensorsForBackwardMode(_)}return k}}else{let{forwardFunc:d}=e,h=g=>{!o||(n=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=qb(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)}),o&&this.addTapeNode(u,c,t,m,n,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(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let o=Vh(e);if(o!=null){let s=o.inputsToSave||[],a=o.outputsToSave||[],i;o.saveAllInputs?(E(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 l=n.filter((u,c)=>a[c]);return i.concat(l)}return[]}makeTensor(e,t,n,o){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=e;n==="string"&&is(e[0])&&(s=e.map(l=>tl(l)));let a=o.write(s,t,n),i=new Ve(t,n,a,this.nextTensorId());if(this.trackTensor(i,o),n==="string"){let l=this.state.tensorInfo.get(a),u=Db(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,n,o){n=n||"float32";let s=new Ve(t,n,e,this.nextTensorId());return this.trackTensor(s,o),s}makeVariable(e,t=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==e.dtype&&(e=e.cast(o));let s=new rl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*zh(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof rl||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*zh(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,o,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},l=Vh(e);l!=null&&(o=l.gradFunc),o!=null&&(i.gradient=u=>(u=u.map((c,p)=>{if(c==null){let m=n[p],f=Fc(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(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=wm(e),n=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&&!n.has(a.id)&&a.dispose()}let o=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===o.id&&this.track(s)})}gradients(e,t,n,o=!1){if(E(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));E(s instanceof Ve,()=>"The result y returned by f() must be a tensor.");let a=DI(this.state.activeTape,t,s);if(!o&&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]=n==null?dV(s.shape):n,$I(i,a,u=>this.tidy(u),hV);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 E(Us(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{E(t.every(i=>i instanceof Ve),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,o={};t.forEach((i,l)=>{o[l]=i});let s=(i,l)=>(n=e(...t,l),E(n.value instanceof Ve,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),E(Us(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(i,l)=>{let u=n.gradFunc(i,l),c=Array.isArray(u)?u:[u];E(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(...)."),E(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:o})}}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=mu(),n=await this.backend.time(e);return n.wallMs=mu()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Kb;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}};du.nextTensorId=0;du.nextVariableId=0;function dV(r){let e=pm(ct(r),"float32");return D.makeTensor(e,r,"float32")}function Xb(){let r=Fb();if(r._tfengine==null){let e=new Bh(r);r._tfengine=new du(e)}return AI(r._tfengine.ENV),PI(()=>r._tfengine),r._tfengine}var D=Xb();function hV(r,e){let t={a:r,b:e};return D.runKernel(wn,t)}var hu={};Ke(hu,{isBrowser:()=>Yb,isMobile:()=>xV});function gV(){return typeof navigator!="undefined"&&navigator!=null}function xV(){if(gV()){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 Yb(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var vs=j();vs.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. 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Got an object with ${e.length} keys.`);let t=e[0],n=r[t];t.endsWith("_")&&(t=t.substring(0,t.length-1)),t=t+GI;let o=(...s)=>{D.startScope(t);try{let a=n(...s);return fm(a)&&console.error("Cannot return a Promise inside of tidy."),D.endScope(a),a}catch(a){throw D.endScope(null),a}};return Object.defineProperty(o,"name",{value:t,configurable:!0}),o}function yV(r,e){let t=v(r,"real","complex"),n=v(e,"imag","complex");Ct(t.shape,n.shape,`real and imag shapes, ${t.shape} and ${n.shape}, must match in call to tf.complex().`);let o={real:t,imag:n};return D.runKernel(Gl,o)}var _n=T({complex_:yV});function Ur(r,e,t,n){if(n==null&&(n=$c(r)),n==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!or(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(e!=null){mm(e);let o=ct(e),s=ct(t);E(o===s,()=>`Based on the provided shape, [${e}], the tensor should have ${o} values but has ${s}`);for(let a=0;a<t.length;++a){let i=t[a],l=a===t.length-1?i!==ct(e.slice(a)):!0;E(t[a]===e[a]||!l,()=>`Error creating a new Tensor. 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Actual: ${o}.
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Expected: ${s}.`);for(let a=0;a<s.length;++a){let i=o[a],l=s[a];if(!t(i,l))throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${l}.
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Actual: ${o}.
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Expected: ${s}.`)}}function uG(r,e){r().then(()=>e.fail(),()=>e())}function cG(r,e){let t=typeof e=="string"||typeof e=="number"||typeof e=="boolean"?[e]:e;return is(r)||is(r[0])||is(e)||is(e[0])?fw(r,t,(n,o)=>n==o):fw(r,e,(n,o)=>dw(n,o,0))}function pG(r,e,t){if(t==null&&(t=mw()),!dw(r,e,t))throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`)}function dw(r,e,t){return!isFinite(r)&&!isFinite(e)?!0:!(isNaN(r)||isNaN(e)||Math.abs(r-e)>t)}function mG(r,e,t){for(let n=0;n<r.length;n++)if(r[n]<e||r[n]>t)throw new Error(`Value out of range:${r[n]} low: ${e}, high: ${t}`)}function fG(r,e){expect(new Float32Array(r)).toEqual(new Float32Array(e))}function ON(r){for(let e=0;e<r.length;e++){let t=r[e];Array.isArray(t)?ON(t):r[e]=tl(t)}return r}var dG="3.3.0";function hG(){j().set("PROD",!0)}function gG(){j().set("DEBUG",!0)}function xG(){j().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function 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g=XG([e,t,n,1],i,1,o,r,c);m=g[0],f=g[1],d=g[2]}else if(r==="same"){m=Math.ceil(e/o),f=Math.ceil(t/s),d=Math.ceil(n/a);let h=(m-1)*o+i-e,g=(f-1)*s+l-t,x=(d-1)*a+u-n,w=Math.floor(h/2),b=h-w,k=Math.floor(g/2),_=g-k,A=Math.floor(x/2),N=x-A;p={top:k,bottom:_,left:A,right:N,front:w,back:b,type:"SAME"}}else if(r==="valid")p={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},m=Math.ceil((e-i+1)/o),f=Math.ceil((t-l+1)/s),d=Math.ceil((n-u+1)/a);else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:p,outDepth:m,outHeight:f,outWidth:d}}function ku(r,e){if(!e)return Math.trunc(r);switch(e){case"round":return Math.round(r);case"ceil":return Math.ceil(r);case"floor":return Math.floor(r);default:throw new Error(`Unknown roundingMode ${e}`)}}function Ln(r){let[e,t,n]=ng(r);return e===1&&t===1&&n===1}function kr(r,e){return Ln(r)||Ln(e)}function MN(r){if(r==="NHWC")return"channelsLast";if(r==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${r}`)}function YG(r,e){let n={x:v(r,"x","reshape","string_or_numeric")},o={shape:e};return D.runKernel(ds,n,o)}var L=T({reshape_:YG});function ZG(r,e,t,n,o){let s=v(r,"x","avgPool","float32"),a=1;E(kr(t,a),()=>`Error in avgPool: Either strides or dilations must be 1. 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]])),E(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),o!=null&&E(nt(n),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${o} but got pad ${n}.`);let u={x:i},c={filterSize:e,strides:t,pad:n,dimRoundingMode:o},p=D.runKernel(Kn,u,c);return p=oe(p,s.dtype),l?L(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var wa=T({avgPool_:ZG});function JG(r,e,t,n,o,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]])),E(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),E(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),o!=null&&E(nt(n),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${n}.`);let u={x:i},c={filterSize:e,strides:t,pad:n,dimRoundingMode:o,dataFormat:s},p=D.runKernel(ta,u,c);return p=oe(p,i.dtype),l?L(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Em=T({avgPool3d_:JG});function QG(r,e=0){E(r.length>=1,()=>"Pass at least one tensor to concat");let t=ha(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 Pn(t[0]);let n=t,o={axis:e};return D.runKernel(ls,n,o)}var Ze=T({concat_:QG});function eW(r){let t={x:v(r,"x","sigmoid")};return D.runKernel(Ao,t)}var qr=T({sigmoid_:eW});function tW(r,e,t){let n=v(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:e,size:t};return D.runKernel(gs,o,s)}var Re=T({slice_:tW});function rW(r){let t={x:v(r,"x","tanh")};return D.runKernel(Oo,t)}var Oi=T({tanh_:rW});function nW(r,e,t,n,o,s){let a=v(r,"forgetBias","basicLSTMCell"),i=v(e,"lstmKernel","basicLSTMCell"),l=v(t,"lstmBias","basicLSTMCell"),u=v(n,"data","basicLSTMCell"),c=v(o,"c","basicLSTMCell"),p=v(s,"h","basicLSTMCell"),m=Ze([u,p],1),f=We(m,i),d=ee(f,l),h=d.shape[0],g=d.shape[1]/4,x=[h,g],w=Re(d,[0,0],x),b=Re(d,[0,g],x),k=Re(d,[0,g*2],x),_=Re(d,[0,g*3],x),A=ee(P(qr(w),Oi(b)),P(c,qr(ee(a,k)))),N=P(Oi(A),qr(_));return[A,N]}var oW=T({basicLSTMCell_:nW});function sW(r,e,t){let n=v(r,"x","batchToSpaceND"),o=e.reduce((i,l)=>i*l);E(n.rank>=1+e.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${e.length}`),E(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.length}`),E(n.shape[0]%o==0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${o}`);let s={x:n},a={blockShape:e,crops:t};return D.runKernel(ra,s,a)}var ka=T({batchToSpaceND_:sW});function zN(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 iW(r,e,t,n,o,s){s==null&&(s=.001);let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;n!=null&&(c=v(n,"offset","batchNorm")),E(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),E(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),E(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:zN(a),scale:u,offset:c,mean:i,variance:l},f={varianceEpsilon:s},d=D.runKernel(io,m,f);return L(d,a.shape)}var Lo=T({batchNorm_:iW});function aW(r,e,t,n,o,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),E(a.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`),E(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),E(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Lo(a,i,l,c,u,s)}var ww=T({batchNorm2d_:aW});function lW(r,e,t,n,o,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Lo(a,i,l,c,u,s)}var kw=T({batchNorm3d_:lW});function uW(r,e,t,n,o,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Lo(a,i,l,c,u,s)}var _w=T({batchNorm4d_:uW});function cW(r,e,t){let n=v(r,"x","bincount"),o=v(e,"weights","bincount");E(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},a={size:t};return D.runKernel(Vl,s,a)}var vw=T({bincount_:cW});function pW(r,e){let t=v(r,"broadcastTo","x"),n=t.shape;if(e.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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 o=t.shape,s=Array.from(e);for(let u=e.length-1;u>=0;u--)if(o[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Pn(t);let i={x:t},l={reps:s};return D.runKernel(kn,i,l)}var al=T({broadcastTo_:pW});function mW(r){let t={x:v(r,"x","ceil")};return D.runKernel(Yn,t)}var Dm=T({ceil_:mW});function fW(r,e,t){let n=v(r,"x","clipByValue");E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let o={x:n},s={clipValueMin:e,clipValueMax:t};return D.runKernel(Rn,o,s)}var ir=T({clipByValue_:fW});function dW(r){return Ze(r,0)}var Cw=T({concat1d_:dW});function hW(r,e){return Ze(r,e)}var Iw=T({concat2d_:hW});function gW(r,e){return Ze(r,e)}var Nw=T({concat3d_:gW});function xW(r,e){return Ze(r,e)}var Sw=T({concat4d_:xW});function yW(r,e,t,n,o="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]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&E(nt(n),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p=o==="NHWC"?u.shape[3]:u.shape[1];E(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),E(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:n,dataFormat:o,dilations:s,dimRoundingMode:a},d=D.runKernel(Zn,m,f);return c?L(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Kr=T({conv2d_:yW});function bW(r,e,t,n,o="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]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),a!=null&&E(nt(n),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`),E(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),E(kr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),E(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=L(l,[1,l.shape[0],l.shape[1],l.shape[2]]),m=L(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Kr(m,p,[1,t],n,"NHWC",[1,s],a);return c?L(g,[g.shape[2],g.shape[3]]):L(g,[g.shape[0],g.shape[2],g.shape[3]])}var _u=T({conv1d_:bW});function wW(r,e,t,n,o,s="NHWC",a){E(r.length===e.rank,()=>`Length of inShape (${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]]),E(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),E(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),E(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],p=s==="NHWC"?l.shape[3]:l.shape[1];E(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),E(p===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${t.shape[3]}.`),a!=null&&E(nt(o),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let m={dy:l,filter:t},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:a,inputShape:i},d=D.runKernel(Jn,m,f);return u?L(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Xc=T({conv2DBackpropInput_:wW});function kW(r,e,t,n,o,s){let a=v(r,"x","conv2dTranspose"),i=v(e,"filter","conv2dTranspose");return Xc(t,a,i,n,o,"NHWC",s)}var vu=T({conv2dTranspose_:kW});function _W(r,e,t,n,o="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]])),E(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),E(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),E(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),E(kr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`);let c={x:l,filter:i},p={strides:t,pad:n,dataFormat:o,dilations:s},m=D.runKernel(oa,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var $m=T({conv3d_:_W});function vW(r,e,t,n,o){E(r.length===e.rank,()=>`Length of inShape (${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];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(l===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},p={pad:o,strides:n,inputShape:s},m=D.runKernel(Ul,c,p);return i?L(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var og=T({conv3DBackpropInput_:vW});function CW(r,e,t,n,o){let s=v(r,"x","conv3dTranspose"),a=v(e,"filter","conv3dTranspose");return og(t,s,a,n,o)}var IW=T({conv3dTranspose_:CW});function NW(r){let t={x:v(r,"x","cos")};return D.runKernel(Qn,t)}var _a=T({cos_:NW});function SW(r){let t={x:v(r,"x","cosh")};return D.runKernel(ei,t)}var Cu=T({cosh_:SW});function TW(r,e=0,t=!1,n=!1){let s={x:v(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:n};return D.runKernel(eo,s,a)}var Iu=T({cumsum_:TW});function AW(r,e,t,n=!1){let o=v(r,"x","denseBincount"),s=v(e,"weights","denseBincount");E(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),E(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let a={x:o,weights:s},i={size:t,binaryOutput:n};return D.runKernel(Hl,a,i)}var Tw=T({denseBincount_:AW});function EW(r,e,t="NHWC"){let n=v(r,"x","depthToSpace"),o=t==="NHWC"?n.shape[1]:n.shape[2],s=t==="NHWC"?n.shape[2]:n.shape[3],a=t==="NHWC"?n.shape[3]:n.shape[1];E(o*e>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${o} and ${e} for depthToSpace with input shape
|
|
${n.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${e} for depthToSpace with input shape
|
|
${n.shape}`),E(a%(e*e)==0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${n.shape}`);let i={x:n},l={blockSize:e,dataFormat:t};return D.runKernel(ri,i,l)}var Rm=T({depthToSpace_:EW});function DW(r,e,t,n,o="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]])),E(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),E(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),E(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),a!=null&&E(nt(n),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p={x:u,filter:l},m={strides:t,pad:n,dataFormat:o,dilations:s,dimRoundingMode:a},f=D.runKernel(to,p,m);return c?L(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Is=T({depthwiseConv2d_:DW});function $W(r){let t={x:v(r,"x","diag")};return D.runKernel(Xl,t)}var RW=T({diag_:$W});function FW(r,e,t,n,o=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(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:n,dilations:o},m=D.runKernel(sa,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Fm=T({dilation2d_:FW});function OW(r,e){let t=r.length,n=[];for(let o=0;o<t;o++){let s=t-1-o,a=r[s]||1;(e[e.length-1-o]||1)>1&&a===1&&n.unshift(s)}return n}function _t(r,e){let t=[];for(let n=0;n<e.length;n++){let 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t={input:v(r,"input","imag")};return D.runKernel(Ql,t)}var Nu=T({imag_:JW});function QW(r){let t={x:v(r,"x","isFinite")};return D.runKernel(ci,t)}var Ew=T({isFinite_:QW});function ej(r){let t={x:v(r,"x","isInf")};return D.runKernel(pi,t)}var Dw=T({isInf_:ej});function tj(r){let t={x:v(r,"x","isNaN")};return D.runKernel(mi,t)}var $w=T({isNaN_:tj});function rj(r,e=.2){let n={x:v(r,"x","leakyRelu")},o={alpha:e};return D.runKernel(lo,n,o)}var Ca=T({leakyRelu_:rj});function nj(r,e){let t=v(r,"a","less"),n=v(e,"b","less");[t,n]=Ge(t,n),Be(t.shape,n.shape);let o={a:t,b:n};return D.runKernel(fi,o)}var Su=T({less_:nj});function oj(r,e){let t=v(r,"a","lessEqual"),n=v(e,"b","lessEqual");[t,n]=Ge(t,n),Be(t.shape,n.shape);let o={a:t,b:n};return D.runKernel(di,o)}var Bn=T({lessEqual_:oj});function Rw(r,e,t){if(t<=0)throw new Error("The number of values should be positive.");let n={start:r,stop:e,num:t};return D.runKernel(eu,{},n)}function sj(r,e=5,t=1,n=1,o=.5){let 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rank ${s.rank}.`),E(nt(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:n,beta:o},c=D.runKernel(aa,l,u);return i?L(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Lm=T({localResponseNormalization_:sj});function ij(r){let t={x:v(r,"x","log")};return D.runKernel(uo,t)}var lr=T({log_:ij});function aj(r){let t={x:v(r,"x","log1p")};return D.runKernel(hi,t)}var Tu=T({log1p_:aj});function lj(r){return E(Us(r),()=>"The f passed in grad(f) must be a function"),(e,t)=>{let n=v(e,"x","tf.grad","string_or_numeric"),o=t!=null?v(t,"dy","tf.grad"):null;return D.tidy(()=>{let{value:s,grads:a}=D.gradients(()=>r(n),[n],o);return o!=null&&Ct(s.shape,o.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),sg(a),a[0]})}}function uj(r){return E(Us(r),()=>"The f passed in grads(f) must be a function"),(e,t)=>{E(Array.isArray(e),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let n=ha(e,"args","tf.grads","string_or_numeric"),o=t!=null?v(t,"dy","tf.grads"):null;return D.tidy(()=>{let{value:s,grads:a}=D.gradients(()=>r(...n),n,o);return o!=null&&Ct(s.shape,o.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),sg(a),a})}}function cj(r){return E(Us(r),()=>"The f passed in valueAndGrad(f) must be a function"),(e,t)=>{E(e instanceof Ve,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),E(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:n,value:o}=D.gradients(()=>r(e),[e],t);return sg(n),{grad:n[0],value:o}}}function pj(r){return E(Us(r),()=>"The f passed in valueAndGrads(f) must be a function"),(e,t)=>{E(Array.isArray(e)&&e.every(o=>o instanceof Ve),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),E(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let n=D.gradients(()=>r(...e),e,t);return t!=null&&Ct(n.value.shape,t.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),sg(n.grads),n}}function ig(r,e){E(Us(r),()=>"The f passed in variableGrads(f) must be a function"),E(e==null||Array.isArray(e)&&e.every(u=>u instanceof rl),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let t=e!=null;if(!t){e=[];for(let u in D.registeredVariables)e.push(D.registeredVariables[u])}let n=t?e.filter(u=>!u.trainable):null,o=e.length;e=e.filter(u=>u.trainable),E(e.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${o} variables is trainable.`);let s=!0,{value:a,grads:i}=D.gradients(r,e,null,s);E(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),E(a.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);let l={};return e.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),n!=null&&n.forEach(u=>l[u.name]=null),{value:a,grads:l}}function Xr(r){return D.customGrad(r)}function sg(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 mj(r){let t={x:v(r,"x","neg")};return D.runKernel(ps,t)}var He=T({neg_:mj});function fj(r){let t={x:v(r,"x","softplus")};return D.runKernel(Ti,t)}var Ts=T({softplus_:fj});function dj(r){let e=v(r,"x","logSigmoid");return Xr(n=>({value:He(Ts(He(n))),gradFunc:a=>P(a,qr(He(n)))}))(e)}var Fw=T({logSigmoid_:dj});function hj(r,e=null,t=!1){let o={x:v(r,"x","max")},s={reductionIndices:e,keepDims:t};return D.runKernel(co,o,s)}var ur=T({max_:hj});function gj(r,e){let t=v(r,"a","sub"),n=v(e,"b","sub");[t,n]=Ge(t,n);let o={a:t,b:n};return D.runKernel(Fo,o)}var ce=T({sub_:gj});function xj(r,e=null,t=!1){let n=v(r,"x","sum");n.dtype==="bool"&&(n=oe(n,"int32"));let o={x:n},s={axis:e,keepDims:t};return D.runKernel(Do,o,s)}var ge=T({sum_:xj});function yj(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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vU({x:r,filter:e,strides:t,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:a,bias:i,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Uu(D.state.gradientDepth,l)===!1){let _=Kr(r,e,t,n,o,s,a);return i!=null&&(_=ee(_,i)),ju(_,l,u,c)}let p=v(r,"x","conv2d"),m=v(e,"filter","conv2d"),f=p,d=!1;p.rank===3&&(d=!0,f=L(p,[1,p.shape[0],p.shape[1],p.shape[2]])),E(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),E(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),a!=null&&E(nt(n),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`),E(f.shape[3]===m.shape[2],()=>`Error in conv2d: depth of input (${f.shape[3]}) must match input depth for filter ${m.shape[2]}.`),E(kr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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o=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),Ct(o.shape,s.shape,"Error in hingeLoss: ");let i=le(1);o=ce(P(le(2),o),i);let l=Sr(ce(i,P(o,s)));return Tr(l,a,n)}var IS=T({hingeLoss_:oH});function sH(r,e,t,n=1,o=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),Ct(s.shape,a.shape,"Error in huberLoss: ");let l=le(n),u=Nt(ce(a,s)),c=As(u,l),p=ce(u,c),m=ee(P(le(.5),Oe(c)),P(l,p));return Tr(m,i,o)}var NS=T({huberLoss_:sH});function iH(r,e,t,n=1e-7,o=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),Ct(s.shape,a.shape,"Error in logLoss: ");let l=le(1),u=le(n),c=He(P(s,lr(ee(a,u)))),p=P(ce(l,s),lr(ee(ce(l,a),u))),m=ce(c,p);return Tr(m,i,o)}var SS=T({logLoss_:iH});function aH(r,e,t,n=Wt.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),Ct(o.shape,s.shape,"Error in meanSquaredError: ");let i=zu(o,s);return Tr(i,a,n)}var TS=T({meanSquaredError_:aH});function lH(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),n=v(e,"logits","sigmoidCrossEntropyWithLogits");Ct(t.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Sr(n),s=P(n,t),a=Tu(Zt(He(Nt(n))));return ee(ce(o,s),a)}function uH(r,e,t,n=0,o=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),Ct(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=le(n),c=le(1),p=le(.5);s=ee(P(s,ce(c,u)),P(p,u))}let l=lH(s,a);return Tr(l,i,o)}var AS=T({sigmoidCrossEntropy_:uH});function cH(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet 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Labels / logits was rank ${e.rank} and dim was ${t}`);return Xr((o,s,a)=>{let l=Bm(s,[t],!0),u=ce(oe(s,"float32"),l);a([o,u]);let c=He(P(u,o));return{value:ge(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=Bo(f.shape,[t]);return[P(L(f,x),ce(oe(h,"float32"),Zt(g))),P(L(f,x),ce(Zt(g),oe(h,"float32")))]}}})(r,e)}function pH(r,e,t,n=0,o=Wt.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),Ct(s.shape,a.shape,"Error in softmaxCrossEntropy: "),n>0){let u=le(n),c=le(1),p=le(s.shape[1]);s=ee(P(s,ce(c,u)),me(u,p))}let l=cH(s,a);return Tr(l,i,o)}var ES=T({softmaxCrossEntropy_:pH});var mH={fft:Ea,ifft:Mi,rfft:Da,irfft:Lu},fH={hammingWindow:sS,hannWindow:dg,frame:hg,stft:iS},$s={flipLeftRight:lS,resizeNearestNeighbor:xg,resizeBilinear:gg,rotateWithOffset:uS,cropAndResize:aS,nonMaxSuppression:cS,nonMaxSuppressionAsync:fS,nonMaxSuppressionWithScore:dS,nonMaxSuppressionWithScoreAsync:hS,nonMaxSuppressionPadded:gS,nonMaxSuppressionPaddedAsync:xS,transform:yS},ak={bandPart:bS,gramSchmidt:wS,qr:_S},dH={absoluteDifference:vS,computeWeightedLoss:Tr,cosineDistance:CS,hingeLoss:IS,huberLoss:NS,logLoss:SS,meanSquaredError:TS,sigmoidCrossEntropy:AS,softmaxCrossEntropy:ES};var Pr=class extends tg{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ae(s),t?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return ig(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Pr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var np=class extends Pr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n],a=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:G(()=>Ce(s).variable(a))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:G(()=>Ce(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedGrads[o].variable,u=this.accumulatedUpdates[o].variable;G(()=>{let c=ee(P(l,this.rho),P(Oe(i),1-this.rho)),p=P(me(xt(ee(u,this.epsilon)),xt(ee(l,this.epsilon))),i),m=ee(P(u,this.rho),P(Oe(p),1-this.rho));l.assign(c),u.assign(m);let f=ee(P(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};np.className="Adadelta";sn(np);var op=class extends Pr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:G(()=>va(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;G(()=>{let l=ee(i,Oe(a));i.assign(l);let u=ee(P(me(a,xt(ee(l,D.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(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};op.className="Adagrad";sn(op);var sp=class extends Pr{constructor(e,t,n,o=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],G(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(n).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);G(()=>{let n=ce(1,this.accBeta1),o=ce(1,this.accBeta2);t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:G(()=>Ce(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:G(()=>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,n),h=me(f,o);c.assign(m),p.assign(f);let g=ee(P(me(d,ee(xt(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),G(()=>{this.accBeta1.assign(Or(this.beta1,this.iterations_+1)),this.accBeta2.assign(Or(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};sp.className="Adam";sn(sp);var ip=class extends Pr{constructor(e,t,n,o=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],G(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);G(()=>{let n=ce(1,this.accBeta1),o=me(-this.learningRate,ee(P(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=D.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=Nt(u),h=Yr(f,d);c.assign(m),p.assign(h);let g=ee(P(me(o,n),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)}};ip.className="Adamax";sn(ip);var ul=class extends Pr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=Array.isArray(e)?e[o].tensor:e[n];if(s==null)return;let a=D.registeredVariables[n];G(()=>{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=Dt(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)}};ul.className="SGD";sn(ul);var ap=class extends ul{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=le(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulations[o]==null){let l=!1;this.accumulations[o]={originalName:`${n}/momentum`,variable:G(()=>Ce(s).variable(l))}}let a=this.accumulations[o].variable,i=Array.isArray(e)?e[o].tensor:e[n];i!=null&&G(()=>{let l,u=ee(P(this.m,a),i);this.useNesterov?l=ee(P(this.c,ee(i,P(u,this.m))),s):l=ee(P(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ae(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ap.className="Momentum";sn(ap);var lp=class extends Pr{constructor(e,t=.9,n=0,o=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let 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Error(`batchDims (${n}) must be less than rank(x) (
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${s}).`);if(t<n)throw new Error(`batchDims (${n}) must be less than or equal to axis (${t}).`);for(let p=0;p<n;++p)if(r.shape[p]!==e.shape[p])throw new Error(`x.shape[${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<n;++p)i.push(r.shape[p]),l*=r.shape[p];for(let p=n;p<t;p++)i.push(r.shape[p]),u*=r.shape[p];for(let p=n;p<o;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 UH(r){try{return r.map(e=>Vc(e))}catch(e){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`)}}function HH(r){return r.map(e=>tl(e))}var Ar={};Ke(Ar,{nonMaxSuppressionV3Impl:()=>ok,nonMaxSuppressionV4Impl:()=>sk,nonMaxSuppressionV5Impl:()=>ik,whereImpl:()=>Jw});function te(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var qH=Ar.whereImpl,Hu=class extends js{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Za(this,Mn())}nextDataId(){return Hu.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,j().get("IS_NODE")&&S.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 o={id:this.nextDataId()};return this.data.set(o,{values:e,dtype:n,refCount:1}),o}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(a=>y.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return{dataId:o,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,o,s){this.data.set(e,{values:t,dtype:o,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let o=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return S.mergeRealAndImagArrays(o,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>y.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,n)}makeOutput(e,t,n){let o=this.write(e,t,n);return Mn().makeTensorFromDataId(o,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=y.now();return e(),{kernelMs:y.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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l.complexTensorInfos={real:t.makeTensorInfo(n.shape,"float32",s),imag:t.makeTensorInfo(o.shape,"float32",a)},i}var FS={kernelName:Gl,backendName:"cpu",kernelFunc:mr};function up(r,e,t="float32"){if(t==="complex64"){let o=up(r,e,"float32"),s=up(r,e,"float32");return mr({inputs:{real:o,imag:s},backend:r})}let n=y.makeZerosTypedArray(y.sizeFromShape(e),t);return r.makeTensorInfo(e,t,n)}function Er(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var OS={kernelName:Fn,backendName:"cpu",kernelFunc:Er};function jo(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.data.get(n.dataId).complexTensorInfos.real,s=t.data.get(o.dataId).values;return t.makeTensorInfo(o.shape,o.dtype,s)}var PS={kernelName:iu,backendName:"cpu",kernelFunc:jo};function Uo(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Er({inputs:{x:o},backend:t});let 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GT={kernelName:Bl,backendName:"cpu",kernelFunc:Tq};function Aq(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;te([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=S.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,w=c.effectiveFilterWidth,b=w-1-c.padInfo.left,k=x-1-c.padInfo.top,_=ve(a.shape,"float32"),A=1/(f*d),N=t.data.get(o.dataId).values,$=ve(o.shape,"float32",N);for(let F=0;F<c.batchSize;++F)for(let M=0;M<c.inChannels;++M)for(let V=0;V<c.inHeight;++V)for(let W=0;W<c.inWidth;++W){let U=V-k,H=W-b,q=0;for(let X=0;X<x;X+=h){let ne=(U+X)/p;if(!(ne<0||ne>=c.outHeight||Math.floor(ne)!==ne))for(let Y=0;Y<w;Y+=g){let re=(H+Y)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;q+=$.get(F,ne,re,M)}}_.set(q*A,F,V,W,M)}return t.makeTensorInfo(_.shape,_.dtype,_.values)}var WT={kernelName:zl,backendName:"cpu",kernelFunc:Aq};function 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t.makeTensorInfo(o.shape,o.dtype,h)}var jT={kernelName:io,backendName:"cpu",kernelFunc:Eq};function Dq(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;te([o],"batchToSpaceND");let i=s.reduce((x,w)=>x*w),l=S.getReshaped(o.shape,s,i),u=S.getPermuted(l.length,s.length),c=S.getReshapedPermuted(o.shape,s,i),p=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),f=Qe({inputs:{x:o},backend:t,attrs:{shape:l}}),d=rr({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Qe({inputs:{x:d},backend:t,attrs:{shape:c}}),g=Ko({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var UT={kernelName:ra,backendName:"cpu",kernelFunc:Dq};function $q(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,u=of(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var HT={kernelName:Vl,backendName:"cpu",kernelFunc:$q};var Rq=$e(Rn,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),qT={kernelName:Rn,backendName:"cpu",kernelFunc:Rq};var Fq=r=>{let{x:e}=r.inputs,t=r.backend,n=new Float32Array(y.sizeFromShape(e.shape)),o=t.data.get(e.dataId),s=o.complexTensorInfos.real,a=o.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];n[u]=Math.hypot(c,p)}return t.makeOutput(n,e.shape,"float32")},KT={kernelName:na,backendName:"cpu",kernelFunc:Fq};function zi(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.data.get(n.dataId).complexTensorInfos.imag,s=t.data.get(o.dataId).values;return t.makeTensorInfo(o.shape,o.dtype,s)}var XT={kernelName:Ql,backendName:"cpu",kernelFunc:zi};function pl(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,e[0].shape)[0],a=S.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 Er({inputs:{x:i[0]},backend:t});let l=i.map(h=>h.shape);if(S.assertParamsConsistent(l,s),i[0].dtype==="complex64"){let h=i.map(k=>jo({inputs:{input:k},backend:t})),g=i.map(k=>zi({inputs:{input:k},backend:t})),x=pl({inputs:h,backend:t,attrs:{axis:s}}),w=pl({inputs:g,backend:t,attrs:{axis:s}}),b=mr({inputs:{real:x,imag:w},backend:t});return h.forEach(k=>t.disposeIntermediateTensorInfo(k)),g.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(w),b}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=S.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=sf(c,a,e[0].dtype,p),f=S.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var YT={kernelName:ls,backendName:"cpu",kernelFunc:pl};function Nk(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n;te([o,s],"conv2d");let p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(o.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,w=m.padInfo.top,b=m.dataFormat==="channelsLast",k=new lt(m.outShape,o.dtype),_=y.computeStrides(o.shape),A=y.computeStrides(s.shape),N=_[0],$=b?_[1]:_[2],F=b?_[2]:1,M=b?1:_[1],V=k.strides[0],W=b?k.strides[1]:k.strides[2],U=b?k.strides[2]:1,H=b?1:k.strides[1],q=t.data.get(o.dataId).values,X=t.data.get(s.dataId).values,ne=k.values;for(let Y=0;Y<m.batchSize;++Y){let re=Y*N,J=Y*V;for(let ie=0;ie<m.outHeight;++ie){let ue=J+ie*W,ae=ie*m.strideHeight-w;for(let fe=0;fe<f;++fe){let de=ae+fe*h;if(de<0||de>=m.inHeight)continue;let xe=fe*A[0],we=re+de*$;for(let 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M=Math.max(0,Math.ceil((k-F)/f)),V=Math.min(m.outHeight,(m.inHeight+k-F)/f);for(let W=0;W<g;++W){let U=Math.max(0,Math.ceil((b-W)/d)),H=Math.min(m.outWidth,(m.inWidth+b-W)/d);for(let q=0;q<m.inChannels;++q)for(let X=0;X<m.outChannels;++X){let ne=0;for(let Y=0;Y<m.batchSize;++Y)for(let re=M;re<V;++re){let J=F+re*f-k;for(let ie=U;ie<H;++ie){let ue=W+ie*d-b;x?ne+=N.get(Y,J,ue,q)*$.get(Y,re,ie,X):ne+=N.get(Y,q,J,ue)*$.get(Y,X,re,ie)}}w.set(ne,F,W,q,X)}}}return t.makeTensorInfo(w.shape,w.dtype,w.values)}var JT={kernelName:Wl,backendName:"cpu",kernelFunc:Oq};function Pq(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n;te([o,s],"conv2dBackpropInput");let p=y.computeStrides(s.shape),m=y.computeStrides(o.shape),f=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(a,s.shape,i,1,l,c,!1,f),h=new 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QT={kernelName:Jn,backendName:"cpu",kernelFunc:Pq};function Mq(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n;te([o,s],"conv3d");let u=S.computeConv3DInfo(o.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,x=g.front,w=g.left,b=g.top,k=new lt(u.outShape,o.dtype),_=t.data.get(o.dataId).values,A=t.data.get(s.dataId).values,N=k.values,$=y.computeStrides(o.shape),F=y.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let V=M*$[0],W=M*k.strides[0];for(let U=0;U<u.outDepth;++U){let H=W+U*k.strides[1],q=U*u.strideDepth-x;for(let X=0;X<c;++X){let ne=q+X*f;if(ne<0||ne>=u.inDepth)continue;let Y=X*F[0],re=V+ne*$[1];for(let J=0;J<u.outHeight;++J){let ie=H+J*k.strides[2],ue=J*u.strideHeight-b;for(let ae=0;ae<p;++ae){let fe=ue+ae*d;if(fe<0||fe>=u.inHeight)continue;let de=Y+ae*F[1],xe=re+fe*$[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*k;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*_+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*A+De;for(let At=0;At<p.inChannels;++At){let Et=At*N+it;for(let je=0;je<p.outChannels;++je){let ut=0;for(let mt=0;mt<p.batchSize;++mt){let Mt=mt*H,xn=mt*F;for(let Xt=ue;Xt<ae;++Xt){let $r=(ie+Xt*m-Y)*q+Mt,jn=Xt*M+xn;for(let nr=xe;nr<we;++nr){let Gr=(de+nr*f-J)*X+$r,br=nr*V+jn;for(let rn=ze;rn<qe;++rn){let Dl=(Ne+rn*d-re)*ne+Gr,Ua=rn*W+br;ut+=U[Dl+At]*$[Ua+je]}}}}b[Et+je]=ut}}}}}return t.makeTensorInfo(w.shape,w.dtype,w.values)}var t1={kernelName:jl,backendName:"cpu",kernelFunc:Lq};function zq(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n;te([o],"conv3dBackpropInputV2");let u=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(l,s.shape,i,1,a),m=new lt(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,w=t.data.get(o.dataId).values,[b,k,_,A]=u,N=t.data.get(s.dataId).values,[$,F,M,V]=c,{batchSize:W,filterDepth:U,filterHeight:H,filterWidth:q,inChannels:X,inDepth:ne,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<W;++ze)for(let qe=0;qe<X;++qe)for(let it=0;it<ne;++it){let At=it-we,Et=Math.max(0,Math.ceil(At/fe)),je=Math.min(ie,(U+At)/fe);for(let ut=0;ut<Y;++ut){let mt=ut-De,Mt=Math.max(0,Math.ceil(mt/de)),xn=Math.min(ue,(H+mt)/de);for(let Xt=0;Xt<re;++Xt){let tn=Xt-Ne,$r=Math.max(0,Math.ceil(tn/xe)),jn=Math.min(ae,(q+tn)/xe),nr=0;for(let yn=Et;yn<je;++yn){let Gr=yn*fe-At;for(let br=Mt;br<xn;++br){let rn=br*de-mt;for(let En=$r;En<jn;++En){let 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s1={kernelName:ti,backendName:"cpu",kernelFunc:Gq};function Wq(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n;te(o,"cumsum");let l=S.getAxesPermutation([s],o.shape.length),u=o;l!=null&&(u=rr({inputs:{x:o},backend:t,attrs:{perm:l}}));let c=S.getInnerMostAxes(1,o.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=dr(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,w)=>x+d-w-1:(x,w)=>x+w;for(let x=0;x<f.length;x+=d)for(let w=0;w<d;w++){let b=h(x,w);if(w===0)m[b]=a?0:f[b];else{let k=h(x,w-1);m[b]=a?f[k]+m[k]:f[b]+m[k]}}let g=t.makeTensorInfo(u.shape,p,m);if(l!=null){let x=S.getUndoAxesPermutation(l),w=rr({inputs:{x:g},backend:t,attrs:{perm:x}});return t.disposeIntermediateTensorInfo(g),t.disposeIntermediateTensorInfo(u),w}return g}var i1={kernelName:eo,backendName:"cpu",kernelFunc:Wq};function jq(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.data.get(o.dataId).values,u=t.data.get(s.dataId).values,c=of(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=ck(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${o.shape.length}.`)}var a1={kernelName:Hl,backendName:"cpu",kernelFunc:jq};function Uq(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;y.assert(a==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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r!==2?!1:Vn(r).fenceSync!=null}function Rs(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGL backend.`)})}var Pe=j();Pe.registerFlag("HAS_WEBGL",()=>Pe.getNumber("WEBGL_VERSION")>0);Pe.registerFlag("WEBGL_VERSION",()=>$g(2)?2:$g(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",()=>t_(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>r_(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Pe.getNumber("WEBGL_VERSION");return r===0?0:n_(r)});Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Pe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!hu.isMobile());Pe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>s_(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",()=>i_(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>a_(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",()=>hu.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 Ft(){let r,e,t,n,o,s,a,i,l,u;return j().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",n="in",o="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",n="varying",o="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:n,texture2D:o,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Fs(r,e,t="index"){let n=y.computeStrides(e);return n.map((o,s)=>{let a=`int ${r[s]} = ${t} / ${o}`,i=s===n.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${a}; ${i};`}).join("")}function gp(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 Rg=`
|
|
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 l_=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ml.DENSE;let t=fl(e),n=Ft();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Fs(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}};var u_=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ml.DENSE;let t=fl(e),n=Ft();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Fs(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}};var c_=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Dr.DOWNLOAD;let t=Ft();this.outputShape=e,this.userCode=`
|
|
${Rg}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}};var p_=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Dr.DOWNLOAD;let t=Ft();this.outputShape=e,this.userCode=`
|
|
${Rg}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}};var m_=class{constructor(e,t,n=!1){this.variableNames=["A"];let o=Ft(),[s,a]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${gp(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 = ${o.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];
|
|
}
|
|
|
|
${o.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}};var f_=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Ft(),[s,a]=t;this.outputShape=e;let i="",l="result";n&&(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 = ${o.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${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=`
|
|
${gp(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${o.output} = ${l};
|
|
}
|
|
`}};var fE={};Ke(fE,{bindVertexProgramAttributeStreams:()=>__,createBufferFromOutputTexture:()=>I_,createFloat16MatrixTexture:()=>y_,createFloat16PackedMatrixTexture:()=>k_,createFloat32MatrixTexture:()=>x_,createIndexBuffer:()=>g_,createPackedMatrixTexture:()=>w_,createUnsignedBytesMatrixTexture:()=>b_,createVertexBuffer:()=>h_,createVertexShader:()=>d_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>S_,downloadFloat32MatrixFromBuffer:()=>N_,downloadMatrixFromPackedOutputTexture:()=>A_,downloadPackedMatrixFromBuffer:()=>T_,getInternalFormatForFloat16MatrixTexture:()=>Og,getInternalFormatForFloat16PackedMatrixTexture:()=>Lg,getInternalFormatForFloat32MatrixTexture:()=>Fg,getInternalFormatForPackedMatrixTexture:()=>Mg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Pg,uploadDenseMatrixToTexture:()=>v_,uploadPixelDataToTexture:()=>C_});function d_(r){let e=Ft(),t=`${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 Gk(r,t)}function h_(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 Hk(r,e)}function g_(r){let e=new Uint16Array([0,1,2,2,1,3]);return qk(r,e)}function kf(r,e,t,n,o,s){Xk(e,t);let a=Kk(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,n,e,t,0,o,s,null)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function Fg(r){return r.internalFormatFloat}function x_(r,e,t,n){let[o,s]=Yu(e,t);return kf(r,o,s,Fg(n),n.textureFormatFloat,r.FLOAT)}function Og(r){return r.internalFormatHalfFloat}function y_(r,e,t,n){let[o,s]=Yu(e,t);return kf(r,o,s,Og(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Pg(r){return r.downloadTextureFormat}function b_(r,e,t,n){let[o,s]=Yu(e,t);return kf(r,o,s,Pg(n),r.RGBA,r.UNSIGNED_BYTE)}function Mg(r){return r.internalFormatPackedFloat}function w_(r,e,t,n){let[o,s]=Bi(e,t);return kf(r,o,s,Mg(n),r.RGBA,r.FLOAT)}function Lg(r){return r.internalFormatPackedHalfFloat}function k_(r,e,t,n){let[o,s]=Bi(e,t);return kf(r,o,s,Lg(n),r.RGBA,n.textureTypeHalfFloat)}function __(r,e,t){let n=0,o=3*4,s=3*4+2*4;return Ie(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Sg(r,e,"clipSpacePos",t,3,s,n)&&Sg(r,e,"uv",t,2,s,o)}function v_(r,e,t,n,o,s){Ie(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;o instanceof Uint8Array?(a=new Uint8Array(t*n*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*n*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(o),Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,n,0,r.RGBA,i,a)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function C_(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 I_(r,e,t,n){let o=r.createBuffer();Ie(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));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)),o}function N_(r,e,t){let n=r,o=new Float32Array(t);return n.bindBuffer(n.PIXEL_PACK_BUFFER,e),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function S_(r,e,t,n){let[o,s]=Yu(e,t),a=4,i=new Uint8Array(iE(e*t,a));return Ie(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function T_(r,e,t,n,o,s,a,i){let l=r,u=new Float32Array(aE(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 A_(r,e,t){let n=new Float32Array(e*t*4);return Ie(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,n)),n}var zg=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=j().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Bk(t,e)):this.gl=Vn(t);let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(j().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=dp(this.gl,s),In(this.gl,a))this.textureHalfFloatExtension=dp(this.gl,a);else if(j().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),In(this.gl,o))this.colorBufferHalfFloatExtension=dp(this.gl,o);else if(j().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",In(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(In(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=h_(this.gl),this.indexBuffer=g_(this.gl),this.framebuffer=Yk(this.gl),this.textureConfig=xf(this.gl,this.textureHalfFloatExtension)}get debug(){return j().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;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(),x_(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),y_(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),b_(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),C_(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,o){this.throwIfDisposed(),v_(this.gl,e,t,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),k_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),w_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Tg(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>S_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,o,s,a){return T_(this.gl,e,t,n,o,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return N_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let o=I_(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(j().getBool("WEBGL_FENCE_API_ENABLED")){let o=e,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=o.clientWaitSync(s,0,0);return a===o.ALREADY_SIGNALED||a===o.CONDITION_SATISFIED},t=s}else j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>A_(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Wk(t,e),o=d_(t),s=jk(t);return Ie(t,()=>t.attachShader(s,o)),Ie(t,()=>t.attachShader(s,n)),Uk(t,s),this.debug&&yf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=__(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&&yf(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Zk(this.gl,e,t):Jk(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),Qk(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[o,s]=Bi(t,n);this.setOutputMatrixTextureDriver(e,o,s)}setOutputMatrixWriteRegion(e,t,n,o){this.setOutputMatrixWriteRegionDriver(n,e,o,t)}setOutputPackedMatrixWriteRegion(e,t,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&yf(this.gl,this.program),hp(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=dp(this.gl,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=o5(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),bf(this.gl,e,this.framebuffer),this.debug&&hp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bf(this.gl,this.outputTexture,this.framebuffer),this.debug&&hp(this.gl)):Tg(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let o=this.gl;bf(o,e,this.framebuffer),this.debug&&hp(o),this.outputTexture=e,Ie(o,()=>o.viewport(0,0,t,n)),Ie(o,()=>o.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,o){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,o))}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 o5(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:dE}=S;function hE(r,e,t,n){let o=[];r.forEach(d=>{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`))});let s=o.join(`
|
|
`),a=r.map(d=>s5(d,e,n)).join(`
|
|
`),i=e.texShape,l=Ft(),u=l5(l),c,p,m=p5(l);return e.isPacked?(c=i5(e.logicalShape,i),p=c5(l)):(c=a5(e.logicalShape,i),p=u5(l)),n&&(m+=m5),[m,u,p,s,c,a,t].join(`
|
|
`)}function xp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return f5(r);case 1:return d5(r);case 2:return h5(r);case 3:return g5(r);case 4:return x5(r);case 5:return y5(r);case 6:return b5(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function gE(r){switch(r.shapeInfo.logicalShape.length){case 0:return w5(r);case 1:return k5(r);case 2:return _5(r);case 3:return v5(r);default:return C5(r)}}function s5(r,e,t=!1){let n="";t?n+=gE(r):n+=xp(r);let o=r.shapeInfo.logicalShape,s=e.logicalShape;return o.length<=s.length&&(t?n+=I5(r,e):n+=N5(r,e)),n}function i5(r,e){switch(r.length){case 0:return xE();case 1:return S5(r,e);case 2:return E5(r,e);case 3:return T5(r,e);default:return A5(r,e)}}function a5(r,e){switch(r.length){case 0:return xE();case 1:return D5(r,e);case 2:return P5(r,e);case 3:return $5(r,e);case 4:return R5(r,e);case 5:return F5(r,e);case 6:return O5(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function l5(r){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${r.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function u5(r){return`
|
|
void setOutput(float val) {
|
|
${r.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function c5(r){return`
|
|
void setOutput(vec4 val) {
|
|
${r.output} = val;
|
|
}
|
|
`}function p5(r){return`${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);
|
|
}
|
|
|
|
${M5}
|
|
${L5}
|
|
${z5}
|
|
`}var M5=`
|
|
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);
|
|
}
|
|
`,L5=`
|
|
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);
|
|
}
|
|
`,z5=`
|
|
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);
|
|
}
|
|
`,m5=`
|
|
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 xE(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function S5(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 D5(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 * ${e[1]} + resTexRC.y;
|
|
}
|
|
`}function T5(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),o=n*Math.ceil(r[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
int b = index / ${o};
|
|
index -= b * ${o};
|
|
|
|
int r = 2 * (index / ${n});
|
|
int c = imod(index, ${n}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function $5(r,e){let t=Fs(["r","c","d"],r);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
${t}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function A5(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),o=n*Math.ceil(r[r.length-2]/2),s=o,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
|
|
int b${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 / ${o};
|
|
index -= b * ${o};
|
|
|
|
int r = 2 * (index / ${n});
|
|
int c = imod(index, ${n}) * 2;
|
|
|
|
return ivec${r.length}(${i});
|
|
}
|
|
`}function R5(r,e){let t=Fs(["r","c","d","d2"],r);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
${t}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function F5(r,e){let t=Fs(["r","c","d","d2","d3"],r);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
|
|
${e[1]}));
|
|
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
|
|
${t}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function O5(r,e){let t=Fs(["r","c","d","d2","d3","d4"],r);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
|
|
${t}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function E5(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`;let n=Math.ceil(r[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${n});
|
|
int c = imod(index, ${n}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function P5(r,e){return y.arraysEqual(r,e)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 * ${r[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Zu(r){return`offset${r}`}function w5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=Ft();return`
|
|
vec4 ${t}() {
|
|
return ${n.texture2D}(${e}, halfCR);
|
|
}
|
|
`}function f5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[n,o]=r.shapeInfo.texShape;if(n===1&&o===1)return`
|
|
float ${t}() {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let[s,a]=r.shapeInfo.texShape,i=Zu(e);return`
|
|
float ${t}() {
|
|
vec2 uv = uvFromFlat(${s}, ${a}, ${i});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function k5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=r.shapeInfo.texShape,o=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],s=Ft();return`
|
|
vec4 ${t}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${o[0]}, ${o[1]}, index);
|
|
return ${s.texture2D}(${e}, uv);
|
|
}
|
|
`}function d5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
|
|
float ${t}(int index) {
|
|
${yp(r)}
|
|
}
|
|
`;let n=r.shapeInfo.texShape,o=n[0],s=n[1];if(s===1&&o===1)return`
|
|
float ${t}(int index) {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let a=Zu(e);return s===1?`
|
|
float ${t}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${o}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:o===1?`
|
|
float ${t}(int index) {
|
|
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:`
|
|
float ${t}(int index) {
|
|
vec2 uv = uvFromFlat(${o}, ${s}, index + ${a});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function _5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=o[0],a=o[1],i=Ft();if(o!=null&&y.arraysEqual(e,o))return`
|
|
vec4 ${n}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
|
|
|
|
return ${i.texture2D}(${t}, uv);
|
|
}
|
|
`;let l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=Math.ceil(e[1]/2);return`
|
|
vec4 ${n}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${i.texture2D}(${t}, uv);
|
|
}
|
|
`}function h5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape;if(o!=null&&y.arraysEqual(e,o)){let p=o[0],m=o[1];return`
|
|
float ${n}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${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=bp(r,i),m=["row","col"];return`
|
|
${xp(p)}
|
|
float ${n}(int row, int col) {
|
|
return ${n}(${wp(m,a)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
|
|
${yp(r)}
|
|
}
|
|
`;let l=o[0],u=o[1],c=Zu(t);return u===1?`
|
|
float ${n}(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 ${n}(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 ${n}(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 v5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=bp(r,p),d=["b","row","col"];return`
|
|
${gE(f)}
|
|
vec4 ${n}(int b, int row, int col) {
|
|
return ${n}(${wp(d,m)});
|
|
}
|
|
`}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Ft();return`
|
|
vec4 ${n}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${a}, ${i}, ${u}, ${l}, b, row, col);
|
|
return ${c.texture2D}(${t}, uv);
|
|
}
|
|
`}function g5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=bp(r,l),h=["row","col","depth"];return`
|
|
${xp(d)}
|
|
float ${n}(int row, int col, int depth) {
|
|
return ${n}(${wp(h,i)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${o}, ${s}, 1)));
|
|
${yp(r)}
|
|
}
|
|
`;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===o&&m==null)return`
|
|
float ${n}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${c}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(p===s&&m==null)return`
|
|
float ${n}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let f=Zu(t);return`
|
|
float ${n}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${s} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${c}, ${p}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function C5(r){let e=r.shapeInfo.logicalShape,t=e.length,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.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=Ft();return`
|
|
vec4 ${o}(${p}) {
|
|
int index = ${m};
|
|
int texR = index / ${l};
|
|
int texC = index - texR * ${l};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
|
|
return ${f.texture2D}(${n}, uv);
|
|
}
|
|
`}function x5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[3],s=e[2]*o,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=bp(r,i),h=["row","col","depth","depth2"];return`
|
|
${xp(d)}
|
|
float ${n}(int row, int col, int depth, int depth2) {
|
|
return ${n}(${wp(h,l)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${a}, ${s}, ${o}, 1)));
|
|
${yp(r)}
|
|
}
|
|
`;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(m===o&&u==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${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=Zu(t);return`
|
|
float ${n}(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 * ${o} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function y5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[4],s=e[3]*o,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=bp(r,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${xp(h)}
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${n}(${wp(g,u)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${a}, ${s}, ${o})) +
|
|
depth3;
|
|
${yp(r)}
|
|
}
|
|
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${a}, ${s}, ${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(f===o&&c==null)return`
|
|
float ${n}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${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=Zu(t);return`
|
|
float ${n}(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 * ${o} + depth3 + ${d};
|
|
vec2 uv = uvFromFlat(${m}, ${f}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function b5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:o,keptDims:s}=y.squeezeShape(e);if(o.length<e.length){let g=bp(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${xp(g)}
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${n}(${wp(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 ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${a}, 1)));
|
|
${yp(r)}
|
|
}
|
|
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
|
|
float ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${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 ${n}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${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=Zu(t);return`
|
|
float ${n}(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 yp(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 ${e}[i];
|
|
}
|
|
}
|
|
`}function I5(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=dE(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(w=>`coords.${p[w+u]} = 0;`).join(`
|
|
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((w,b)=>`coords.${p[b+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 w=s-2,b=s-1;i.indexOf(w)>-1&&i.indexOf(b)>-1?f="return vec4(outputValue.x);":i.indexOf(w)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(b)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${o}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${n}(${m});
|
|
${f}
|
|
}
|
|
`}function N5(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"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 ${o}() {
|
|
return sampleTexture(${t}, resultUV);
|
|
}
|
|
`;let u=Le(l),c=dE(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 ${o}() {
|
|
${u} coords = getOutputCoords();
|
|
${m}
|
|
return get${n}(${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 ${r} is not yet supported`)}function bp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function wp(r,e){return e.map(t=>r[t]).join(", ")}function yE(r,e,t,n){let o=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},l=hE(s,i,o,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);j().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${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 bE(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,n)=>{let o=t.logicalShape,s=e[n],a=s.shape;if(!y.arraysEqual(o,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} 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 ${l} must match`)})}function wE(r,e,t,n,o){bE(e.inShapeInfos,t),bE([e.outShapeInfo],[n]);let s=n.texData.texture,a=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),j().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${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)}}),o!=null&&o(r,e.webGLProgram),r.executeProgram()}function kE(r,e,t){let n="";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;n+=`${a.shape}_${l}_${i}`});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o,s}var{addImpl:_E,bincountImpl:Bg,bincountReduceImpl:vE,ceilImpl:CE,concatImpl:IE,expImpl:NE,expm1Impl:SE,floorImpl:TE,gatherV2Impl:AE,greaterImpl:EE,lessImpl:DE,linSpaceImpl:$E,logImpl:RE,maxImpl:FE,maximumImpl:OE,minimumImpl:PE,multiplyImpl:ME,negImpl:LE,prodImpl:zE,rangeImpl:BE,rsqrtImpl:VE,simpleAbsImpl:Vg,sliceImpl:GE,stridedSliceImpl:WE,subImpl:jE,tileImpl:UE,topKImpl:HE,transposeImpl:kp,uniqueImpl:qE}=wg;function E_(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function jt(r,e){return e===1?[r]:E_(r,e)}function KE(r,e){if(r===1)return"rc";let t="";for(let n=0;n<r;n++)t+=e[n],n<r-1&&(t+=",");return t}var D_=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let n=jt("rc",t),o=Le(t),s=B5(t,e,n),a=V5(t,e[e.length-1],e[e.length-2],n),i=G5(e,n);this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
|
|
if(${s}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function W5(r,e){let t=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function B5(r,e,t){if(r===1)return`rc > ${e[0]}`;let n="";for(let o=r-2;o<r;o++)n+=`${t[o]} >= ${e[o]}`,o<r-1&&(n+="||");return n}function V5(r,e,t,n){if(r===1)return"";let o=n.slice(-2);return`
|
|
int r = ${o[0]};
|
|
int c = ${o[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${e};
|
|
bool rEdge = rp1 >= ${t};
|
|
`}function G5(r,e){let t=r.length,n=W5(t,e);return t===1?`getA(rc),
|
|
rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${n[0]}),
|
|
cEdge ? 0. : getA(${n[1]}),
|
|
rEdge ? 0. : getA(${n[2]}),
|
|
rEdge || cEdge ? 0. : getA(${n[3]})`}var _f=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2==1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
|
|
${s}
|
|
${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${o}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${o>0?"}":""}
|
|
`}this.userCode=`
|
|
${j5(t)}
|
|
${gp(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function j5(r){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${Fs(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var $_=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let o=YE(t,n),s=ZE(e,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=XE(e,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return o===_r.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===_r.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===_r.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===_r.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===_r.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,o){if(this.freeTextures==null)return;let s=YE(n,o),a=ZE(t,s,o);a in this.freeTextures||(this.freeTextures[a]=[]);let i=XE(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),l=j().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 U5(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 XE(r,e,t,n,o){let s=H5(e,n),a;if(o){let[l,u]=Bi(r[0],r[1]);a=l*u}else{let[l,u]=Yu(r[0],r[1]);a=l*u}let i=U5(t,s);return a*i}function H5(r,e){switch(r){case _r.PACKED_2X2_FLOAT32:return Mg(e);case _r.PACKED_2X2_FLOAT16:return Lg(e);case _r.UNPACKED_FLOAT32:return Fg(e);case _r.UNPACKED_FLOAT16:return Og(e);case _r.PACKED_4X1_UNSIGNED_BYTE:return Pg(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function q5(r){return j().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?_r.PACKED_2X2_FLOAT32:_r.UNPACKED_FLOAT32:r?_r.PACKED_2X2_FLOAT16:_r.UNPACKED_FLOAT16}function YE(r,e){if(r===Dr.UPLOAD)return _r.PACKED_2X2_FLOAT32;if(r===Dr.RENDER||r==null)return q5(e);if(r===Dr.DOWNLOAD||r===Dr.PIXELS)return _r.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function ZE(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var ln=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);
|
|
}
|
|
`}},gr="if (isnan(x)) return x;",JE="return x;",R_="return abs(x);";var QE="return (x >= 0.0) ? x : (exp(x) - 1.0);",e2=gr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,t2=gr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,vf="return x;";var r2="return x;",n2=`
|
|
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;
|
|
`,o2=`
|
|
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;
|
|
`,s2=`
|
|
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;
|
|
`,Os=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}};var F_=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=jt("rc",t),o=Le(t),s=KE(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${s});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}};var K5=Ar.whereImpl,X5=1e-7,Y5=1e-4,Gg={};function Z5(r){return r in Gg||(Gg[r]={}),Gg[r]}var J5=128,Q5=600;function e8(){return j().global.screen==null?1024:j().global.screen.height*j().global.screen.width*window.devicePixelRatio*Q5/1024/1024}var Ju=class extends js{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,!j().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Vn(j().getNumber("WEBGL_VERSION"));this.binaryCache=Z5(j().getNumber("WEBGL_VERSION")),this.gpgpu=new zg(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 $_(this.gpgpu),this.numMBBeforeWarning=e8(),this.texData=new Za(this,Mn())}nextDataId(){return Ju.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((j().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||j().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:t,dtype:n,values:e,usage:Dr.UPLOAD,refCount:1}),o}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,o,s){if(j().getBool("DEBUG")&&this.checkNumericalProblems(t),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:o,values:t,usage:Dr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:o,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Os(i,vf):m=new ln(i,vf);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(e);if(o==="string")return n;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.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:n,shape:o,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Os(o,vf):d=new ln(o,vf);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(e);if(!j().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&j().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&j().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...fl(o))}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=S.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(o);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)&&Mn().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>y.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Vk(n))throw j().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:o}=this.texData.get(e),s=y.sizeFromShape(t);if(j().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...fl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=j().getBool("WEBGL_PACK")&&o===!0,i=a?wf(t):t,l=a?new p_(i):new c_(i),u=this.runWebGLProgram(l,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${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 j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(j().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:o,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(o,n),this.textureManager.releaseTexture(t,o,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 j().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Mn().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=J5){let n=this.getCPUBackend();return!j().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return K5(e.shape,t)}packedUnaryOp(e,t,n){let o=new Os(e.shape,t),s=this.compileAndRun(o,[e],n);return Mn().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=Vg(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(j().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,R_,e.dtype);let t=new ln(e.shape,R_),n=this.compileAndRun(t,[e]);return Mn().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(a=>y.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return this.texData.get(o).usage=null,{dataId:o,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:o}=this.makeTensorInfo(e,t,n);return Mn().makeTensorFromDataId(o,e,t,this)}unpackTensor(e){let t=new F_(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new D_(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Oa(e.shape),...Pa(e.shape)],o={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Oa(t),...Pa(t)],a=new _f(s,n),i=!0,l=this.runWebGLProgram(a,[o],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:o,dtype:s}=t,a=wf(o),i;n?i=new u_(a):i=new l_(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:o,dataId:u.dataId}}runWebGLProgram(e,t,n,o,s=!1){let a=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===ml.DENSE){let g=fl(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)<=j().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&&!dl(x.shape,g.shape)){let w=g,b=g.shape;g.shape=x.shape,g=this.packedReshape(g,b),l.push(g),x=this.texData.get(g.dataId),w.shape=b}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=kE(e,u,c),m=this.getAndSaveBinary(p,()=>yE(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),wE(this.gpgpu,m,u,c,o),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=j().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=y.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!j().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,n,o,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,o,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(j().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(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=G(()=>{if(!j().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=j().getBool("DEBUG");j().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(j().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?X5:Y5}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:o,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=e_(n,l),t.texShape=p),s!=null){let m=wf(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Bi(p[0],p[1]),f=new f_(m,[h,d],g)):f=new m_(m,[h,d],g);let x=this.makeTensorInfo([h,d],o);g?this.texData.get(x.dataId).usage=Dr.PIXELS:this.texData.get(x.dataId).usage=Dr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let w=!0,b=this.runWebGLProgram(f,[x],o,null,w),k=this.texData.get(b.dataId);t.texture=k.texture,t.texShape=k.texShape,t.isPacked=k.isPacked,t.usage=k.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(b.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,o,l);t.texture=m}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:o}=n;return this.releaseGPUData(e),t!=null&&(n.values=t8(t,o)),n.values}acquireTexture(e,t,n,o){if(this.numBytesInGPU+=this.computeBytes(e,n),!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,o)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};Ju.nextDataId=0;function t8(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 n=0;n<t.length;++n)t[n]=Math.round(r[n]);return t}else throw new Error(`Unknown dtype ${e}`)}var O_="3.3.0";function P_(){j().set("WEBGL_FORCE_F16_TEXTURES",!0)}hu.isBrowser()&&xu("webgl",()=>new Ju,2);var r8={forceHalfFloat:P_};var Wg=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`;var Xo=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}};var hl=`
|
|
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 Ps=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length,a="";if(o)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) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let l=jt("coords",s);a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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 Ut(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var i2={kernelName:Fn,backendName:"webgl",kernelFunc:Ut};function un(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=Ut({inputs:{x:n},backend:t}),l=Ut({inputs:{x:o},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var a2={kernelName:Gl,backendName:"webgl",kernelFunc:un};var M_="return (a < 0.) ? b * a : a;",L_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function n8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ps(L_,o.shape,a.shape):new Xo(M_,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],o.dtype);return t.disposeIntermediateTensorInfo(a),l}var l2={kernelName:lo,backendName:"webgl",kernelFunc:n8};var z_="return (a < 0.) ? b * a : a;",B_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function o8(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ps(B_,n.shape,o.shape):new Xo(z_,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)}var u2={kernelName:ko,backendName:"webgl",kernelFunc:o8};var jg="if (isnan(x)) return x;",c2=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,p2=`
|
|
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 ke({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||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=j().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Os(a.shape,e):c=new ln(a.shape,r),i.runWebGLProgram(c,[a],l)}}function ot({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&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(b=>{let[k,_]=b,A={dataId:k.dataId,dtype:k.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:u.shape},$=new Xo(r,l.shape,u.shape);return c.runWebGLProgram($,[A,N],dr(k.dtype,_.dtype))}),w=un({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),w}let p=s||dr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&o!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=o(l.shape,u.shape,d.values,h.values,p),w=c.makeTensorInfo(x,p),b=c.texData.get(w.dataId);return b.values=g,w}let m=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Ps(e,l.shape,u.shape,t):f=new Xo(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function gl(r,e=!1){if(r==="linear")return e?r2:JE;if(r==="relu")return e?o2:e2;if(r==="elu")return e?n2:QE;if(r==="relu6")return e?s2:t2;if(r==="prelu")return e?B_:z_;if(r==="leakyrelu")return e?L_:M_;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Cf=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["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 w=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let b="rc.x",k="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(k=`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 = ${b};
|
|
int batchB = ${k};
|
|
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);
|
|
|
|
${w}
|
|
|
|
${x}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};var V_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Ug=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}};var m2="return a * b;";function G_(r){let{inputs:e,backend:t}=r,{a:n,b:o}=e,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),u=new Ug(V_.REAL,n.shape,o.shape),c=new Ug(V_.IMAG,n.shape,o.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:n.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:o.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:o.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=un({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([n,o])){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),[u,c]=ME(n.shape,o.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Ps(m2,n.shape,o.shape):a=new Xo(m2,n.shape,o.shape),t.runWebGLProgram(a,[n,o],s)}var f2={kernelName:xo,backendName:"webgl",kernelFunc:G_};function d2(r,e,t){let n=[Oa(r.shape),...Pa(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Oa(e),...Pa(e)],a=new _f(s,n),i=!0,l=t.runWebGLProgram(a,[o],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function pe(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{shape:s}=n,a=t,i=y.sizeFromShape(o.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!dl(o.shape,l)&&!(c.texture!==null&&dl(c.shape,l))?d2(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var h2={kernelName:ds,backendName:"webgl",kernelFunc:pe};var Hg=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%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%n>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 * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${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);
|
|
|
|
${u}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}};var W_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${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(n/4)*4,p=n%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%n>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 * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${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 s8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function Nn(r,e,t,n){let o=s8(r.shape),s=r;for(let a=0;a<o.length;a++){let{inSize:i,windowSize:l,outSize:u}=o[a],c,p;t==="mean"?c=a===0?new Hg({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new Hg({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new W_({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=n.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var j_=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let o=Le(this.rank),s=i8(t);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function i8(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"],n=new Array(e);for(let o=0;o<r.length;o++)n[r[o]]=t[o];return n.join()}var U_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Le(this.rank),s=E_("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${u};
|
|
if(${l}) {
|
|
result[1] = ${u};
|
|
}
|
|
--${s[this.rank-1]};
|
|
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${u};
|
|
if(${l}) {
|
|
result[3] = ${u};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function xl(r,e,t){let n=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new U_(r.shape,e):new j_(r.shape,e);return t.runWebGLProgram(n,[r],r.dtype)}function g2(r,e,t,n){let o=e,s=r.shape.length,a=y.parseAxisParam(o,r.shape),i=a,l=S.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=xl(r,l,n),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=S.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=pe({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),w=fu(r.dtype),b=Nn(x,w,"sum",n),k=pe({inputs:{x:b},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),u&&n.disposeIntermediateTensorInfo(c),k}function If(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;return g2(o,s,a,t)}var x2={kernelName:Do,backendName:"webgl",kernelFunc:If};function Lt(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{perm:s}=n,a=t,i=o.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=o.shape[s[c]];let u;if(a.shouldExecuteOnCPU([o])){let p=a.texData.get(o.dataId).values,m=kp(p,o.shape,o.dtype,s,l);u=a.makeTensorInfo(l,o.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=xl(o,s,a);return u}var y2={kernelName:Po,backendName:"webgl",kernelFunc:Lt};var H_=1e3;function Qu({a:r,b:e,transposeA:t,transposeB:n,backend:o,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=n?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=n?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),w=y.sizeFromShape(g),b=x===w||x===1||w===1;y.assert(u>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let _=(x>w?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${n} must match.`);let A=t?[x,p,f]:[x,f,p],N=n?[w,d,m]:[w,m,d],$=pe({inputs:{x:r},backend:o,attrs:{shape:A}}),F=pe({inputs:{x:e},backend:o,attrs:{shape:N}}),M=[$,F],V=Math.max(x,w),W=t?$.shape[1]:$.shape[2],U=s!=null,H=a!=null,q=l==="leakyrelu",X=l!=null?gl(l,!0):null,ne=U||H||q||X!=null,Y;if((f===1||d===1)&&W>H_&&ne===!1){let J=$,ie=F;t&&(J=Lt({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),M.push(J)),n&&(ie=Lt({inputs:{x:F},backend:o,attrs:{perm:[0,2,1]}}),M.push(ie));let ue=d!==1,ae=d===1,fe=J;ue&&(fe=pe({inputs:{x:J},backend:o,attrs:{shape:[V,W,1]}}),M.push(fe));let de=d===1?2:1,xe=ie;ae&&(xe=pe({inputs:{x:ie},backend:o,attrs:{shape:[V,1,W]}}),M.push(xe));let we=G_({inputs:{a:fe,b:xe},backend:o});Y=If({inputs:{x:we},backend:o,attrs:{axis:de,keepDims:!0}}),M.push(we)}else{let J=dr(r.dtype,e.dtype),ie=new Cf(A,N,[V,f,d],t,n,U,X,H,q),ue=[$,F];if(s!=null&&ue.push(s),H&&ue.push(a),q){let ae=o.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));ue.push(ae),M.push(ae)}Y=o.runWebGLProgram(ie,ue,J)}let re=pe({inputs:{x:Y},backend:o,attrs:{shape:_}});M.push(Y);for(let J of M)o.disposeIntermediateTensorInfo(J);return re}function a8(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n;return Qu({a:o,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var b2={kernelName:ws,backendName:"webgl",kernelFunc:a8};var w2="return abs(x);";function l8(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=t.texData.get(n.dataId),a=Vg(s.values);return t.makeTensorInfo(n.shape,n.dtype,a)}let o;return j().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Os(n.shape,w2):o=new ln(n.shape,w2),t.runWebGLProgram(o,[n],n.dtype)}var k2={kernelName:as,backendName:"webgl",kernelFunc:l8};var u8=gr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,c8=ke({opSnippet:u8}),_2={kernelName:qs,backendName:"webgl",kernelFunc:c8};var p8=gr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,m8=ke({opSnippet:p8}),v2={kernelName:Ks,backendName:"webgl",kernelFunc:m8};var C2="return a + b;",f8=ot({opSnippet:C2,packedOpSnippet:C2,supportsComplex:!0,cpuKernelImpl:_E}),I2={kernelName:wn,backendName:"webgl",kernelFunc:f8};var q_=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${o};
|
|
setOutput(result);
|
|
}
|
|
`}};var K_=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${o};
|
|
setOutput(result);
|
|
}
|
|
`}};function qg(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return Ut({inputs:{x:n[0]},backend:t});if(n.length>j().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=qg({inputs:n.slice(0,l),backend:t}),c=qg({inputs:n.slice(l),backend:t});return qg({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>dr(l,u)),s=n.map(l=>l.shape),i=j().getBool("WEBGL_PACK")?new K_(n[0].shape,s):new q_(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var N2={kernelName:Hn,backendName:"webgl",kernelFunc:qg};function d8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=o;c!=null&&(p=Lt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Nn(h,h.dtype,"all",t),x;if(a){let w=S.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:w}})}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 S2={kernelName:Ml,backendName:"webgl",kernelFunc:d8};function h8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=o;c!=null&&(p=Lt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Nn(h,h.dtype,"any",t),x;if(a){let w=S.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:w}})}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 T2={kernelName:Ll,backendName:"webgl",kernelFunc:h8};var X_=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${o};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${o}; i++) {
|
|
int inIdx = ${l};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}};var Y_=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.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),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=jt("coords",l),p,m;if(a===1){m=l+1;let $=Le(m);p=`
|
|
${$} sourceLocR = ${$}(${c.join()}, 0);
|
|
++${c[l-1]};
|
|
${$} sourceLocG = ${$}(${c.join()}, 0);
|
|
++${c[l-2]};
|
|
${$} sourceLocA = ${$}(${c.join()}, 0);
|
|
--${c[l-1]};
|
|
${$} sourceLocB = ${$}(${c.join()}, 0);
|
|
--${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($=>"int "+$),g=jt("sourceLocR",m-1).concat("inIdx.r"),x=jt("sourceLocG",m-1).concat("inIdx.g"),w=jt("sourceLocB",m-1).concat("inIdx.b"),b=jt("sourceLocA",m-1).concat("inIdx.a"),k=n==="max"?"greaterThan":"lessThan",_=o?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${x.join()}),
|
|
getBestIndicesAChannel(${w.join()}),
|
|
getBestIndicesAChannel(${b.join()})));`,A=`vec4(
|
|
getAChannel(${g.join()}),
|
|
hasNextCol ? getAChannel(${x.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${w.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,N=o?"":`
|
|
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()}));
|
|
}
|
|
${N}
|
|
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 = ${A};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${_}
|
|
vec4 candidate = ${A};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${k}(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 A2(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new X_(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=A2(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function E2(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=S.computeOptimalWindowSize(s),i=new Y_(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=E2(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Kg(r,e,t,n){let o=[t];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!j().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=S.computeOutAndReduceShapes(e.shape,o),l=y.sizeFromShape(i),u=pe({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=A2(r,u,n);s.push(c);let p=pe({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return E2(r,e,n)}function g8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=y.parseAxisParam(s,o.shape),i=S.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Lt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Kg(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var D2={kernelName:qn,backendName:"webgl",kernelFunc:g8};function x8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=y.parseAxisParam(s,o.shape),i=S.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Lt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Kg(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var $2={kernelName:ea,backendName:"webgl",kernelFunc:x8};var y8=gr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,b8=ke({opSnippet:y8}),R2={kernelName:Xs,backendName:"webgl",kernelFunc:b8};var w8=gr+"return log(x + sqrt(x * x + 1.0));",k8=ke({opSnippet:w8}),F2={kernelName:Ys,backendName:"webgl",kernelFunc:k8};var _8=gr+`
|
|
return atan(x);
|
|
`,v8=ke({opSnippet:_8}),O2={kernelName:Zs,backendName:"webgl",kernelFunc:v8};var C8=c2+`
|
|
return atan(a, b);
|
|
`,I8=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+p2+`
|
|
return result;
|
|
`,N8=ot({opSnippet:C8,packedOpSnippet:I8}),P2={kernelName:Qs,backendName:"webgl",kernelFunc:N8};var S8=gr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,T8=ke({opSnippet:S8}),M2={kernelName:Js,backendName:"webgl",kernelFunc:T8};var Vi=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)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`,w="0.0";if(h||(w="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${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 ${$} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let _=Math.floor(a/4)*4,A=a%4,N=`
|
|
if (${h}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${f}, ${d});
|
|
const float initializationValue = ${w};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${w});
|
|
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 < ${_}; 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)
|
|
);
|
|
|
|
${N}
|
|
}
|
|
|
|
int xC = xCCorner + ${_};
|
|
if (${A===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${A===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${A===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
}
|
|
}
|
|
setOutput(${k});
|
|
}
|
|
`}},ec=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)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,w=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",k="0.0";if(b||(k="-1.0 / 1e-20"),n){let M=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${l}, ${u});
|
|
const ivec3 pads = ivec3(${g}, ${x}, ${w});
|
|
|
|
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 = ${o?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 * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let _="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let N=Math.floor(a/4)*4,$=a%4,F=`
|
|
if (${b}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${_}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${l}, ${u});
|
|
const ivec3 pads = ivec3(${g}, ${x}, ${w});
|
|
const float initializationValue = ${k};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${k});
|
|
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 < ${N}; 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)
|
|
);
|
|
|
|
${F}
|
|
}
|
|
|
|
int xC = xCCorner + ${N};
|
|
if (${$===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${F}
|
|
} else if (${$===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${F}
|
|
} else if (${$===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
|
|
);
|
|
|
|
${F}
|
|
}
|
|
}
|
|
setOutput(${A});
|
|
}
|
|
}
|
|
`}};function A8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Rs(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ut({inputs:{x:o},backend:t});let p=new Vi(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var L2={kernelName:Kn,backendName:"webgl",kernelFunc:A8};function E8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new ec(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var z2={kernelName:ta,backendName:"webgl",kernelFunc:E8};var Z_=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=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*n);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) / ${o}.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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},J_=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*n*o);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 D8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new J_(m);return t.runWebGLProgram(f,[o],a.dtype)}var B2={kernelName:Bl,backendName:"webgl",kernelFunc:D8};function $8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;Rs([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=S.computePool2DInfo(a.shape,i,l,1,u),p=new Z_(c);return t.runWebGLProgram(p,[o],a.dtype)}var V2={kernelName:zl,backendName:"webgl",kernelFunc:$8};function R8(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return Qu({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var G2={kernelName:Xn,backendName:"webgl",kernelFunc:R8};var Q_=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(S.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(S.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 ev=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(S.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 F8=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||o.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=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=j().getBool("WEBGL_PACK_NORMALIZATION")?new ev(n.shape,o.shape,s.shape,c,p,l):new Q_(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},W2={kernelName:io,backendName:"webgl",kernelFunc:F8};var tv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),n=`uniform int start[${this.rank}];`,o=O8(this.rank),s,a=e.map((i,l)=>`sourceLoc.${rv[l]} = start[${l}] + coords.${rv[l]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${a.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${n}
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${o}));
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},rv=["x","y","z","w","u","v"];function O8(r){if(r===1)return"sourceLoc";if(r<=6)return rv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var nv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),n=jt("coords",this.rank),o=jt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=`
|
|
result.x = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${o[this.rank-1]};
|
|
result.y = ${a};
|
|
--${o[this.rank-1]};
|
|
}
|
|
`,l=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${o[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${o[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,u=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${o[p]} = ${n[p]} + start[${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,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function P8(r,e,t,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(t,r.dtype),a=n.texData.get(s.dataId);Object.assign(a,o),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=sr.computeFlatOffset(e,y.computeStrides(r.shape));o.slice&&(i+=o.slice.flatOffset),a.slice={flatOffset:i,origDataId:o.slice&&o.slice.origDataId||r.dataId};let l=n.dataRefCount.get(a.slice.origDataId)||1;return n.dataRefCount.set(a.slice.origDataId,l+1),s}function Ma(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,size:a}=n,[i,l]=sr.parseSliceParams(o,s,a);if(sr.assertParamsValid(o,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,o.dtype,[]);if(t.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=t.texData.get(o.dataId),m=GE(p.values,i,l,o.shape,o.dtype);return t.makeTensorInfo(l,o.dtype,m)}let{isPacked:u}=t.texData.get(o.dataId),c=sr.isSliceContinous(o.shape,i,l);if(u||!c){let p=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nv(l):new tv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[o],o.dtype,m)}return t.uploadToGPU(o.dataId),P8(o,i,l,t)}var j2={kernelName:gs,backendName:"webgl",kernelFunc:Ma};var M8=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((w,b)=>w*b),l=S.getReshaped(o.shape,s,i),u=S.getPermuted(l.length,s.length),c=S.getReshapedPermuted(o.shape,s,i),p=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),f=[],d=pe({inputs:{x:o},backend:t,attrs:{shape:l}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ma({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(w=>t.disposeIntermediateTensorInfo(w)),x},U2={kernelName:ra,backendName:"webgl",kernelFunc:M8};function L8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.readSync(o.dataId),l=t.readSync(s.dataId),u=Bg(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var H2={kernelName:Vl,backendName:"webgl",kernelFunc:L8};var z8="return float(a != b);",ov=ot({opSnippet:z8,dtype:"bool"}),q2={kernelName:yi,backendName:"webgl",kernelFunc:ov};function La(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Ut({inputs:{x:o.complexTensorInfos.real},backend:t})}var K2={kernelName:iu,backendName:"webgl",kernelFunc:La};var B8="return float(int(x));";function X2(r,e){let t=new ln(r.shape,B8),n=e.runWebGLProgram(t,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function sv(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Ut({inputs:{x:o},backend:t});let a=gt(o.shape),i=sv({inputs:{x:o},backend:t,attrs:{dtype:"float32"}}),l=un({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(o.dtype==="complex64"){let a=La({inputs:{input:o},backend:t}),i=sv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(o.dtype,s)){let a=Ut({inputs:{x:o},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return X2(o,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=ov({inputs:{a:o,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var Y2={kernelName:$n,backendName:"webgl",kernelFunc:sv};var Z2="return ceil(x);",V8=ke({opSnippet:Z2,packedOpSnippet:Z2,cpuKernelImpl:CE}),J2={kernelName:Yn,backendName:"webgl",kernelFunc:V8};var iv=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};var av=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function G8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i;j().getBool("WEBGL_PACK_CLIP")?i=new av(o.shape):i=new iv(o.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[o],o.dtype,l)}var Q2={kernelName:Rn,backendName:"webgl",kernelFunc:G8};var lv=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 eD(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function W8(r){let{inputs:e,backend:t}=r,{x:n}=e,o=t.texData.get(n.dataId),s=new lv(n.shape),a=[eD(n,o.complexTensorInfos.real),eD(n,o.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var tD={kernelName:na,backendName:"webgl",kernelFunc:W8};var uv=class{constructor(e){this.outputShape=[],this.outputShape=S.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 n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let o=t.length,s=t[t.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}};var cv=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,o=n.length,s=Le(o),a=jt("coords",o),i=["x","y","z","w","u","v"].slice(0,o);this.variableNames=e.map((h,g)=>`T${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}(${Xg(i,u,g)}),
|
|
vec2(${Xg(c,u,g)}));
|
|
}`}let f=l.length,d=l[l.length-1];m+=`
|
|
return getChannel(
|
|
getT${f}(${Xg(i,u,d)}),
|
|
vec2(${Xg(c,u,d)}));`,this.userCode=`
|
|
float getValue(${i.map(h=>"int "+h)}) {
|
|
${m}
|
|
}
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[o-1]} = ${a[o-1]} + 1;
|
|
if (${a[o-1]} < ${n[o-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[o-2]} = ${a[o-2]} + 1;
|
|
if (${a[o-2]} < ${n[o-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[o-1]} = ${a[o-1]} - 1;
|
|
if (${a[o-2]} < ${n[o-2]} &&
|
|
${a[o-1]} < ${n[o-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Xg(r,e,t){let n=r.indexOf(e);return r.map((s,a)=>a===n?`${s} - ${t}`:s).join()}function tc(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Ut({inputs:{x:o.complexTensorInfos.imag},backend:t})}var rD={kernelName:Ql,backendName:"webgl",kernelFunc:tc};function rc(r,e,t){let n=r[0].dtype;if(n==="complex64"){let u=r.map(d=>La({inputs:{input:d},backend:t})),c=r.map(d=>tc({inputs:{input:d},backend:t})),p=rc(u,e,t),m=rc(c,e,t),f=un({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(n==="string"){let{tensors2D:u,outShape:c}=nD(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=IE(p,c,n,m),d=S.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,n,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>j().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=rc(r.slice(0,u),e,t),p=rc(r.slice(u),e,t),m=rc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new cv(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,n)}let{tensors2D:o,outShape:s}=nD(r,e,t),a=new uv(o.map(u=>u.shape)),i=t.runWebGLProgram(a,o,n);o.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=pe({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function nD(r,e,t){let n=S.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:n}}function pv(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,e[0].shape)[0],a=S.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 Ut({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return S.assertParamsConsistent(l,s),rc(i,s,t)}var oD={kernelName:ls,backendName:"webgl",kernelFunc:pv};var Nf=class{constructor(e,t=!1,n=null,o=!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,w=g?2:3,b=g?3:1,k="",_="";n&&(o?k=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?k=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:k=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,_="result = activation(result);");let A=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${k}
|
|
|
|
const ivec2 strides = ivec2(${l}, ${u});
|
|
const ivec2 pads = ivec2(${a}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${b}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${x}], coords[${w}]) * 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;
|
|
${A}
|
|
${_}
|
|
setOutput(result);
|
|
}
|
|
`}},mv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${s}, ${a}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${o});
|
|
|
|
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 fv=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:o,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=n,{left:f,top:d}=l,h=s*o,g=Ft(),x=m==="channelsLast",w=x?0:1,b=x?1:2,k="";for(let _=0;_<=1;_++)for(let A=0;A<=1;A++)k+=`
|
|
blockIndex = rc.y + ${A};
|
|
pos = rc.x + ${_};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${u})) * ${i} - ${d};
|
|
d0 = offsetY + ${p} * (pos / ${h});
|
|
|
|
if(d0 < ${t[w]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
|
|
d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));
|
|
|
|
if(d1 < ${t[b]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${s}.));
|
|
|
|
if (${x}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${_*2+A}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${_*2+A}] = 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;
|
|
|
|
${k}
|
|
|
|
${g.output} = result;
|
|
}
|
|
`}};function Yg({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.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=[],w=(p===1||m===1)&&c>H_,b=l[2]%2!=0&&!!u.isPacked;if(w||!j().getBool("WEBGL_LAZILY_UNPACK")||!j().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!b){let k=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=pe({inputs:{x:r},backend:n,attrs:{shape:[1,k,t.inChannels]}}),A=pe({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),N=Qu({a:_,b:A,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=pe({inputs:{x:N},backend:n,attrs:{shape:t.outShape}}),x.push(_),x.push(A),x.push(N)}else{let k=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:r.dataId,shape:[1,k,t.inChannels],dtype:r.dtype},A=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(dl(u.shape,_.shape),()=>`packed reshape ${u.shape} to ${_.shape} isn't free`);let N=pe({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(N);let $=Qu({a:_,b:N,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),F=n.texData.get($.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=A,F.shape=t.outShape,g=Ut({inputs:{x:$},backend:n}),g.shape=t.outShape,x.push($)}for(let k of x)n.disposeIntermediateTensorInfo(k);return g}function Zg({x:r,filter:e,convInfo:t,backend:n,bias:o=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],w=!0,b=!1,k=[],_=pe({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),A=pe({inputs:{x:e},backend:n,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});k.push(_),k.push(A);let N=new fv(x,_.shape,t),$=n.runWebGLProgram(N,[_],"float32"),F=pe({inputs:{x:$},backend:n,attrs:{shape:[1,x[0],x[1]]}});k.push($),k.push(F);let M=o!=null,V=s!=null,W=i==="leakyrelu",U=i?gl(i,!0):null,H=new Cf(F.shape,A.shape,[1,g,t.outChannels],w,b,M,U,V,W),q=[F,A];if(o&&q.push(o),V&&q.push(s),W){let re=n.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));q.push(re),k.push(re)}let X=n.runWebGLProgram(H,q,"float32"),ne=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=pe({inputs:{x:X},backend:n,attrs:{shape:ne}});k.push(X);for(let re of k)n.disposeIntermediateTensorInfo(re);return Y}function j8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(o.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=Yg({x:o,filter:s,convInfo:m,backend:t});else if(j().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=Zg({x:o,filter:s,convInfo:m,backend:t});else{let h=new Nf(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=pe({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var sD={kernelName:Zn,backendName:"webgl",kernelFunc:j8};var dv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=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} - ${o};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${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);
|
|
}
|
|
`}},hv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=n-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) / ${o}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${a}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},gv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=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 * ${n} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${o} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},xv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=o-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${o}; 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 = ${o} - 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 U8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new dv(m);return t.runWebGLProgram(f,[o,s],"float32")}var iD={kernelName:Wl,backendName:"webgl",kernelFunc:U8};function H8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new hv(m);return t.runWebGLProgram(f,[o,s],"float32")}var aD={kernelName:Jn,backendName:"webgl",kernelFunc:H8};function q8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=S.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new mv(u);return t.runWebGLProgram(c,[o,s],"float32")}var lD={kernelName:oa,backendName:"webgl",kernelFunc:q8};function K8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=S.computeConv3DInfo(o.shape,l,a,1,i),c=new gv(u);return t.runWebGLProgram(c,[o,s],"float32")}var uD={kernelName:jl,backendName:"webgl",kernelFunc:K8};function X8(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=S.computeConv3DInfo(l,s.shape,i,1,a),c=new xv(u);return t.runWebGLProgram(c,[o,s],"float32")}var cD={kernelName:Ul,backendName:"webgl",kernelFunc:X8};var Y8=jg+`
|
|
return cos(x);
|
|
`,Z8=ke({opSnippet:Y8}),pD={kernelName:Qn,backendName:"webgl",kernelFunc:Z8};var J8=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Q8=ke({opSnippet:J8}),mD={kernelName:ei,backendName:"webgl",kernelFunc:Q8};var yv=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,w]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[b,k,_]=m>1?[`${(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(${b});
|
|
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 = ${k};
|
|
|
|
float in_y = ${w};
|
|
if( in_y < 0.0 || in_y > ${d} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
float in_x = ${_};
|
|
if( in_x < 0.0 || in_x > ${h} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${f} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}};var eX=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new yv(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},fD={kernelName:ti,backendName:"webgl",kernelFunc:eX};var Jg=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${dD(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${Le(o)} coords = getOutputCoords();
|
|
int end = ${hD(o,"coords")};
|
|
float val = ${s};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${l};
|
|
${hD(o,"coords")} = idx;
|
|
val += getX(${dD(o,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function dD(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 ${r} is not yet supported`)}function hD(r,e){if(r===1)return`${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 ${r} is not yet supported`)}function tX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=S.getAxesPermutation([s],l),c=o;u!=null&&(c=Lt({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=S.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=Ut({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Jg(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new Jg(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=S.getUndoAxesPermutation(u),h=Lt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var gD={kernelName:eo,backendName:"webgl",kernelFunc:tX};function rX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=Bg(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=vE(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var xD={kernelName:Hl,backendName:"webgl",kernelFunc:rX};var bv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int h = ${this.getHeightCoordString()};
|
|
int w = ${this.getWidthCoordString()};
|
|
int d = ${this.getDepthCoordString()};
|
|
|
|
int in_h = h / ${t};
|
|
int offset_h = imod(h, ${t});
|
|
int in_w = w / ${t};
|
|
int offset_w = imod(w, ${t});
|
|
int offset_d = (offset_h * ${t} + offset_w) *
|
|
${this.getOutputDepthSize()};
|
|
int in_d = d + offset_d;
|
|
|
|
float result = ${this.getInputSamplingString()};
|
|
setOutput(result);
|
|
}
|
|
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function nX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.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 bv(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var yD={kernelName:ri,backendName:"webgl",kernelFunc:nX};var Sf=class{constructor(e,t=!1,n=null,o=!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="",w="";n&&(o?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,w="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&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;
|
|
${b}
|
|
${w}
|
|
setOutput(result);
|
|
}
|
|
`}};var Tf=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let _=0;_<d;_++)for(let A=0;A<h;A++)x+=`
|
|
vec4 xTexelR${_}C${A*2} = vec4(0.);
|
|
vec4 wR${_}C${A} = vec4(0.);
|
|
vec4 xR${_}C${A} = vec4(0.);`;for(let _=0;_<d;_++)for(let A=0;A<g;A++){let N=A*2;if(x+=`
|
|
xR = xRCorner + ${_*m};
|
|
xC = xCCorner + ${N*f};
|
|
`,p===1){if(N<h&&(u%2==1?x+=`
|
|
xCOffset = xC + 1;
|
|
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${N} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
xTexelR${_}C${N}.zw = vec2(0.);
|
|
}
|
|
} else {
|
|
xTexelR${_}C${N} = 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 >= ${i}) {
|
|
previous.zw = vec2(0.);
|
|
}
|
|
|
|
xR${_}C${N} = vec4(previous.zw, xTexelR${_}C${N}.xy);
|
|
} else {
|
|
xR${_}C${N} = vec4(0, 0, xTexelR${_}C${N}.xy);
|
|
}
|
|
`:x+=`
|
|
if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
|
|
xTexelR${_}C${N} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${_}C${N} = vec4(0.);
|
|
}
|
|
|
|
xR${_}C${N} = xTexelR${_}C${N};
|
|
`,N+1<h)){let $=u%2==0?y.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(x+=`
|
|
xCOffset = xC + ${u%2} + ${$};
|
|
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${N+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`,f>1&&(x+=`
|
|
xCOffset -= 2;
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${N} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${_}C${N} = vec4(0.);
|
|
}
|
|
`),x+=`
|
|
xR${_}C${N+1} = vec4(
|
|
xTexelR${_}C${N}.zw, xTexelR${_}C${N+2}.xy);
|
|
`):x+=`
|
|
xCOffset = xC + ${$};
|
|
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${N+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
|
|
xR${_}C${N+1} = xTexelR${_}C${N+2};
|
|
`}}else N<h&&(x+=`
|
|
if(xR >= 0 && xR < ${a}) {
|
|
`,u%2==1?(x+=`
|
|
xCOffset = xC + 1 - ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${N} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${_}C${N} = vec4(0.);
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${i}) {
|
|
xTexelR${_}C${N+2} = getX(batch, xR, xC + 1, d1);
|
|
} else {
|
|
xTexelR${_}C${N+2} = vec4(0.);
|
|
}
|
|
|
|
xR${_}C${N} = vec4(
|
|
xTexelR${_}C${N}.zw, xTexelR${_}C${N+2}.zw);
|
|
`,N+1<h&&(x+=`
|
|
vec4 final = vec4(0.);
|
|
xCOffset = xC + 1 + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xR${_}C${N+1} = vec4(xTexelR${_}C${N+2}.xy, final.xy);
|
|
`)):(x+=`
|
|
if(xC >= 0 && xC < ${i}) {
|
|
xTexelR${_}C${N} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${_}C${N} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${N+2} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${_}C${N+2} = vec4(0.);
|
|
}
|
|
|
|
xR${_}C${N} = vec4(
|
|
xTexelR${_}C${N}.xy, xTexelR${_}C${N+2}.xy);
|
|
`,N+1<h&&(x+=`
|
|
xR${_}C${N+1} = vec4(
|
|
xTexelR${_}C${N}.zw, xTexelR${_}C${N+2}.zw);
|
|
`)),x+="}");N<h&&(x+=`
|
|
vec4 wTexelR${_}C${N} = getW(${_}, ${N}, d1, q);
|
|
wR${_}C${N} = vec4(wTexelR${_}C${N}.xz, wTexelR${_}C${N}.xz);
|
|
`,N+1<h&&(x+=`
|
|
vec4 wTexelR${_}C${N+1} = getW(${_}, ${N+1}, d1, q);
|
|
wR${_}C${N+1} =
|
|
vec4(wTexelR${_}C${N+1}.xz, wTexelR${_}C${N+1}.xz);`))}for(let _=0;_<d;_++)for(let A=0;A<h;A++)x+=`dotProd += xR${_}C${A} * wR${_}C${A};`;let w="",b="";n&&(o?w=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:s?w=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:w=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,b="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${w}
|
|
|
|
const ivec2 strides = ivec2(${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;
|
|
${k}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}};function oX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=n,c=l;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,a,c,i,u,!0),m;return j().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Tf(p):m=new Sf(p),t.runWebGLProgram(m,[o,s],"float32")}var bD={kernelName:to,backendName:"webgl",kernelFunc:oX};var wv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=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} - ${o};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${s};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},kv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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) / ${o}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${l}; dm++) {
|
|
int d2 = d1 * ${l} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function sX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new wv(p);return t.runWebGLProgram(m,[o,s],"float32")}var wD={kernelName:ql,backendName:"webgl",kernelFunc:sX};function iX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new kv(p);return t.runWebGLProgram(m,[o,s],"float32")}var kD={kernelName:Kl,backendName:"webgl",kernelFunc:iX};var _v=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function aX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),a=pe({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new _v(s),l=t.runWebGLProgram(i,[a],a.dtype),u=pe({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var _D={kernelName:Xl,backendName:"webgl",kernelFunc:aX};var vv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function lX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=S.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new vv(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=pe({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var vD={kernelName:sa,backendName:"webgl",kernelFunc:lX};var uX="return (x >= 0.0) ? x : (exp(x) - 1.0);",cX=`
|
|
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;
|
|
`,pX=ke({opSnippet:uX,packedOpSnippet:cX}),CD={kernelName:ni,backendName:"webgl",kernelFunc:pX};var mX="return (b >= 1.0) ? a : a * (b + 1.0);",fX=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,dX=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=j().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ps(fX,n.shape,o.shape):new Xo(mX,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},ID={kernelName:Yl,backendName:"webgl",kernelFunc:dX};var hX=`
|
|
return vec4(equal(a, b));
|
|
`,gX="return float(a == b);",xX=ot({opSnippet:gX,packedOpSnippet:hX,dtype:"bool"}),ND={kernelName:si,backendName:"webgl",kernelFunc:xX};var yX=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${S.ERF_P};
|
|
float a1 = ${S.ERF_A1};
|
|
float a2 = ${S.ERF_A2};
|
|
float a3 = ${S.ERF_A3};
|
|
float a4 = ${S.ERF_A4};
|
|
float a5 = ${S.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,bX=ke({opSnippet:yX}),SD={kernelName:oi,backendName:"webgl",kernelFunc:bX};var TD="return exp(x);",Cv=ke({opSnippet:TD,packedOpSnippet:TD,cpuKernelImpl:NE}),AD={kernelName:no,backendName:"webgl",kernelFunc:Cv};function Qg(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(y.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),pe({inputs:{x:s},backend:n,attrs:{shape:i}})}var ED={kernelName:us,backendName:"webgl",kernelFunc:Qg};var DD="return exp(x) - 1.0;",wX=ke({opSnippet:DD,packedOpSnippet:DD,cpuKernelImpl:SE}),$D={kernelName:ii,backendName:"webgl",kernelFunc:wX};var ex=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.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) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${o});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${o}; 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 tx(r,e,t){let n=t.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=o/s,i=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new ex("real",l,e),c=new ex("imag",l,e),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=un({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 kX(r){let{inputs:e,backend:t}=r,{input:n}=e;return tx(n,!1,t)}var RD={kernelName:Zl,backendName:"webgl",kernelFunc:kX};var Iv=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
uniform float value;
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Af(r){let{backend:e,attrs:t}=r,{shape:n,value:o}=t,{dtype:s}=t;if(s=s||y.inferDtype(o),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(n));return a.fill(o),e.makeTensorInfo(n,s,a)}else{let a=new Iv(n,o),i=a.getCustomSetupFunc(o);return e.runWebGLProgram(a,[],s,i)}}var FD={kernelName:ia,backendName:"webgl",kernelFunc:Af};var Nv=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var OD={kernelName:ai,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,n=e,o=new Nv(t.shape);return n.runWebGLProgram(o,[t],t.dtype)}};var PD="return floor(x);",_X=ke({opSnippet:PD,packedOpSnippet:PD,cpuKernelImpl:TE}),MD={kernelName:oo,backendName:"webgl",kernelFunc:_X};var vX=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,CX=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,IX=ot({opSnippet:vX,packedOpSnippet:CX,dtype:"int32"}),LD={kernelName:so,backendName:"webgl",kernelFunc:IX};var Sv=class{constructor(e){this.variableNames=["A"];let t=Ft(),[n,o]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}};var Tv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ft(),[n,o]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${o}.0, ${n}.0);
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}};var zD={kernelName:Mc,backendName:"webgl",kernelFunc:NX},_p;function NX(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[l,u]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[u,l],p=[u,l,s];(i||a)&&(_p==null&&(_p=document.createElement("canvas").getContext("2d")),_p.canvas.width=l,_p.canvas.height=u,_p.drawImage(o,0,0,l,u),o=_p.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=Dr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),o);let f=j().getBool("WEBGL_PACK")?new Tv(p):new Sv(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function SX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),x,w=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=Yg({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(j().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)x=Zg({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let k=a!=null,_=i!=null,A=f==="leakyrelu",N=f?gl(f,!1):null,$=new Nf(g,k,N,_,A),F=[o,s];if(a&&F.push(a),i&&F.push(i),A){let M=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));F.push(M),w.push(M)}x=t.runWebGLProgram($,F,"float32")}let b=pe({inputs:{x},backend:t,attrs:{shape:g.outShape}});return w.push(x),w.forEach(k=>t.disposeIntermediateTensorInfo(k)),b}var BD={kernelName:ks,backendName:"webgl",kernelFunc:SX};function TX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,l,h,u,p,!0),x=j().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,w=m?gl(m,x):null,b=[o,s],k=a!=null,_=i!=null,A=m==="leakyrelu";if(k&&b.push(a),_&&b.push(i),A){let F=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));b.push(F),d.push(F)}let N;x?N=new Tf(g,k,w,_,A):N=new Sf(g,k,w,_,A);let $=t.runWebGLProgram(N,b,"float32");return d.forEach(F=>t.disposeIntermediateTensorInfo(F)),$}var VD={kernelName:_s,backendName:"webgl",kernelFunc:TX};var Av=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let o=Le(t.length),s=Le(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${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 AX(r){let{inputs:e,backend:t}=r,{params:n,indices:o}=e,s=o.shape,a=s[s.length-1],[i,l,u,c]=S.prepareAndValidate(n,o),p=pe({inputs:{x:o},backend:t,attrs:{shape:[l,a]}}),m=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/u,u]}}),f=new Av(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 GD={kernelName:li,backendName:"webgl",kernelFunc:AX};var Ev=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=Le(this.rank),o=EX(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${o}));
|
|
}
|
|
`}};function EX(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("int(getIndices(resRC.x, resRC.z))"):n.push(`${t[o]}`);return n.join()}function DX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,indices:s}=e,{axis:a,batchDims:i}=n,l=y.parseAxisParam(a,o.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(o,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=pe({inputs:{x:o},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([o,s])||o.dtype==="string"){let w=t.bufferSync(f),b=t.bufferSync(m),k=AE(b,w,d);return p.forEach(_=>t.disposeIntermediateTensorInfo(_)),t.makeTensorInfo(u.outputShape,k.dtype,k.values)}let h=new Ev(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(w=>t.disposeIntermediateTensorInfo(w)),x}var WD={kernelName:cs,backendName:"webgl",kernelFunc:DX};var $X="return float(a > b);",RX=`
|
|
return vec4(greaterThan(a, b));
|
|
`,FX=ot({opSnippet:$X,packedOpSnippet:RX,cpuKernelImpl:EE,dtype:"bool"}),jD={kernelName:ui,backendName:"webgl",kernelFunc:FX};var OX="return float(a >= b);",PX=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,MX=ot({opSnippet:OX,packedOpSnippet:PX,dtype:"bool"}),UD={kernelName:ao,backendName:"webgl",kernelFunc:MX};function LX(r){let{inputs:e,backend:t}=r,{input:n}=e;return tx(n,!0,t)}var HD={kernelName:Jl,backendName:"webgl",kernelFunc:LX};var zX="return float(!isnan(x) && !isinf(x));",BX=ke({opSnippet:zX,dtype:"bool"}),qD={kernelName:ci,backendName:"webgl",kernelFunc:BX};var VX="return float(isinf(x));",GX=ke({opSnippet:VX,dtype:"bool"}),KD={kernelName:pi,backendName:"webgl",kernelFunc:GX};var WX="return float(isnan(x));",jX=ke({opSnippet:WX,dtype:"bool"}),XD={kernelName:mi,backendName:"webgl",kernelFunc:jX};var UX="return float(a < b);",HX=`
|
|
return vec4(lessThan(a, b));
|
|
`,qX=ot({opSnippet:UX,packedOpSnippet:HX,cpuKernelImpl:DE,dtype:"bool"}),YD={kernelName:fi,backendName:"webgl",kernelFunc:qX};var KX="return float(a <= b);",XX=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,YX=ot({opSnippet:KX,packedOpSnippet:XX,dtype:"bool"}),ZD={kernelName:di,backendName:"webgl",kernelFunc:YX};function ZX(r){let{backend:e,attrs:t}=r,{start:n,stop:o,num:s}=t,a=$E(n,o,s);return e.makeTensorInfo([a.length],"float32",a)}var JD={kernelName:eu,backendName:"webgl",kernelFunc:ZX};var JX=`if (x < 0.0) return NAN;
|
|
return log(x);`,QX=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,e7=ke({opSnippet:JX,packedOpSnippet:QX,cpuKernelImpl:RE}),QD={kernelName:uo,backendName:"webgl",kernelFunc:e7};var t7="return log(1.0 + x);",r7=ke({opSnippet:t7}),e$={kernelName:hi,backendName:"webgl",kernelFunc:r7};var n7="return float(a >= 1.0 && b >= 1.0);",o7=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,s7=ot({opSnippet:n7,packedOpSnippet:o7,dtype:"bool"}),t$={kernelName:gi,backendName:"webgl",kernelFunc:s7};var i7="return float(!(x >= 1.0));",a7=ke({opSnippet:i7}),r$={kernelName:Ja,backendName:"webgl",kernelFunc:a7};var l7="return float(a >= 1.0 || b >= 1.0);",u7=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,c7=ot({opSnippet:l7,packedOpSnippet:u7,dtype:"bool"}),n$={kernelName:Qa,backendName:"webgl",kernelFunc:c7};var Dv=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${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 $v=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${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 p7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=j().getBool("WEBGL_PACK_NORMALIZATION")?new $v(o.shape,s,a,i,l):new Dv(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},o$={kernelName:aa,backendName:"webgl",kernelFunc:p7};var Rv=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=o,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 = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${o}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${o})
|
|
* float(${s})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${s});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};var m7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new Rv(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},s$={kernelName:tu,backendName:"webgl",kernelFunc:m7};function i$(r,e,t,n){let o=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/o,i=pe({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Nn(i,r.dtype,"max",n),u=pe({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function Fv(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let b=t.texData.get(f.dataId).values,k=new Array(i);for(let N=0;N<k.length;N++)k[N]=o.shape[c[N]];let _=kp(b,o.shape,o.dtype,c,k);f=t.makeTensorInfo(k,o.dtype);let A=t.texData.get(f.dataId);A.values=_}else f=xl(o,c,t);u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("max",u,i);let[d,h]=S.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=S.expandShapeToKeepDim(d,l));let x;if(m){let b=t.texData.get(f.dataId).values,k=FE(b,y.sizeFromShape(h),g,o.dtype);x=t.makeTensorInfo(g,o.dtype);let _=t.texData.get(x.dataId);_.values=k}else x=i$(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var a$={kernelName:co,backendName:"webgl",kernelFunc:Fv};var f7=Wg+`
|
|
return max(a, b);
|
|
`,d7=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+hl+`
|
|
return result;
|
|
`,h7=ot({opSnippet:f7,packedOpSnippet:d7,cpuKernelImpl:OE}),l$={kernelName:po,backendName:"webgl",kernelFunc:h7};function g7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Rs(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ut({inputs:{x:o},backend:t});let p=new Vi(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var u$={kernelName:mo,backendName:"webgl",kernelFunc:g7};function x7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new ec(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var c$={kernelName:la,backendName:"webgl",kernelFunc:x7};var Ov=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=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 += ${o}) {
|
|
float dyR = float(dyRCorner + wR) / ${t}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Pv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
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 * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function y7(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new ec(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new Pv(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var p$={kernelName:nu,backendName:"webgl",kernelFunc:y7};function b7(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;Rs([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Vi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new Ov(m),x=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var m$={kernelName:ru,backendName:"webgl",kernelFunc:b7};function f$(r,e,t,n){let o=new Vi(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Vi(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var d$={kernelName:ou,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,o,s,u,a),[p,m]=f$(n,i,c,l);return[p,m]}};function h$(r,e,t,n){let o=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/o,i=pe({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Nn(i,"float32","mean",n),u=pe({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var g$={kernelName:fo,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let k=a.texData.get(d.dataId).values,_=new Array(i);for(let $=0;$<_.length;$++)_[$]=n.shape[c[$]];let A=kp(k,n.shape,n.dtype,c,_);d=a.makeTensorInfo(_,n.dtype);let N=a.texData.get(d.dataId);N.values=A}else d=xl(n,c,a);f.push(d),u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=S.computeOutAndReduceShapes(d.shape,u),x=h;o&&(x=S.expandShapeToKeepDim(h,l));let w=h$(d,g,x,a);for(let b of f)a.disposeIntermediateTensorInfo(b);return w}};function w7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=o;c!=null&&(p=Lt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Nn(h,h.dtype,"min",t),x;if(a){let w=S.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:w}})}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 x$={kernelName:ho,backendName:"webgl",kernelFunc:w7};var k7=Wg+`
|
|
return min(a, b);
|
|
`,_7=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+hl+`
|
|
return result;
|
|
`,v7=ot({opSnippet:k7,packedOpSnippet:_7,cpuKernelImpl:PE}),y$={kernelName:go,backendName:"webgl",kernelFunc:v7};var Mv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let o=e.length,s=Le(o),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,o),u=n==="reflect"?0:1;if(o===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 < ${o}; 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 Lv=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=Le(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=jt("rc",o),u=jt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===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});
|
|
${l[o-1]} += 1;
|
|
if(${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});
|
|
${l[o-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
rc = outputLoc;
|
|
${l[o-2]} += 1;
|
|
if(${l[o-2]} < ${this.outputShape[o-2]}) {
|
|
${d}
|
|
result[2] = getChannel(getX(${u.join()}), ${p});
|
|
${l[o-1]} += 1;
|
|
if(${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.);
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};var C7=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lv(n.shape,o,s):new Mv(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},b$={kernelName:ua,backendName:"webgl",kernelFunc:C7};var I7=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,N7=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+hl+`
|
|
return result;
|
|
`,S7=ot({opSnippet:I7,packedOpSnippet:N7}),w$={kernelName:xi,backendName:"webgl",kernelFunc:S7};var zv=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
|
|
uniform float seed;
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var T7=`
|
|
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;
|
|
`,Bv=ot({opSnippet:T7,packedOpSnippet:A7,checkOutOfBounds:!0}),k$={kernelName:ro,backendName:"webgl",kernelFunc:Bv};var _$="return a - b;",Vv=ot({opSnippet:_$,packedOpSnippet:_$,supportsComplex:!0,cpuKernelImpl:jE}),v$={kernelName:Fo,backendName:"webgl",kernelFunc:Vv};function Gv(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{dim:s}=n,a=y.parseAxisParam([s],o.shape),i=Fv({inputs:{x:o},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=S.expandShapeToKeepDim(i.shape,a),u=pe({inputs:{x:i},backend:t,attrs:{shape:l}}),c=Vv({inputs:{a:o,b:u},backend:t}),p=Cv({inputs:{x:c},backend:t}),m=If({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=pe({inputs:{x:m},backend:t,attrs:{shape:l}}),d=Bv({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var C$={kernelName:$o,backendName:"webgl",kernelFunc:Gv};function E7(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{numSamples:s,seed:a,normalized:i}=n,l=i?o:Gv({inputs:{logits:o},backend:t,attrs:{dim:o.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new zv(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var I$={kernelName:su,backendName:"webgl",kernelFunc:E7};var N$="return -x;";function D7(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])){let s=t.texData.get(n.dataId),[a,i]=LE(s.values,n.shape,n.dtype);return t.makeTensorInfo(i,n.dtype,a)}let o;return j().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Os(n.shape,N$):o=new ln(n.shape,N$),t.runWebGLProgram(o,[n],n.dtype)}var S$={kernelName:ps,backendName:"webgl",kernelFunc:D7};var $7=Ar.nonMaxSuppressionV3Impl;function R7(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n,u=t.readSync(o.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=$7(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var T$={kernelName:bi,backendName:"webgl",kernelFunc:R7};var F7=Ar.nonMaxSuppressionV4Impl;function O7(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=F7(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var A$={kernelName:wi,backendName:"webgl",kernelFunc:O7};var P7=Ar.nonMaxSuppressionV5Impl;function M7(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=P7(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var E$={kernelName:ki,backendName:"webgl",kernelFunc:M7};var Wv=class{constructor(e,t,n,o){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${o}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}};var L7=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=y.sizeFromShape(o.shape),u=new Wv(l,s,a,i),c=pe({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=pe({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},D$={kernelName:yo,backendName:"webgl",kernelFunc:L7};function Ef(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=La({inputs:{input:n},backend:t}),s=Ef({inputs:{x:o},backend:t}),a=tc({inputs:{input:n},backend:t}),i=Ef({inputs:{x:a},backend:t}),l=un({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Af({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var $$={kernelName:bs,backendName:"webgl",kernelFunc:Ef};function R$(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=La({inputs:{input:n},backend:t}),s=R$({inputs:{x:o},backend:t}),a=tc({inputs:{input:n},backend:t}),i=Ef({inputs:{x:a},backend:t}),l=un({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Af({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var F$={kernelName:ms,backendName:"webgl",kernelFunc:R$};function z7(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return Qg({inputs:{input:e[0]},backend:t,attrs:{dim:o}});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=Qg({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=pv({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var O$={kernelName:fs,backendName:"webgl",kernelFunc:z7};var jv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=Le(o),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,o);if(o===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,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var Uv=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=Le(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=jt("rc",o),u=jt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1;
|
|
if(${c}) {
|
|
`,o===1?"":`}
|
|
rc = outputLoc;
|
|
${l[o-2]} += 1;
|
|
if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${l[o-1]} += 1;
|
|
if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===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+=o===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,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var Hv=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n,i=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Uv(o.shape,s,a):new jv(o.shape,s,a),l=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[o],o.dtype,l)},P$={kernelName:bo,backendName:"webgl",kernelFunc:Hv};var B7=`
|
|
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);
|
|
`,V7=`
|
|
// 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));
|
|
`+hl+`
|
|
return result;
|
|
`,G7=ot({opSnippet:B7,packedOpSnippet:V7}),M$={kernelName:wo,backendName:"webgl",kernelFunc:G7};function W7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=[],u=y.parseAxisParam(s,o.shape),c=u,p=S.getAxesPermutation(c,i),m=o;p!=null&&(m=Lt({inputs:{x:o},backend:t,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,i),l.push(m)),S.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=zE(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=pe({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),w=fu(o.dtype),b=Nn(x,w,"prod",t);f=pe({inputs:{x:b},backend:t,attrs:{shape:d}}),l.push(x),l.push(b)}if(a){l.push(f);let d=S.expandShapeToKeepDim(f.shape,u);f=pe({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var L$={kernelName:_i,backendName:"webgl",kernelFunc:W7};var qv=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=BE(n,o,s,a);return e.makeTensorInfo([i.length],a,i)},z$={kernelName:ca,backendName:"webgl",kernelFunc:qv};var j7="return 1.0 / x;",U7=ke({opSnippet:j7}),B$={kernelName:vi,backendName:"webgl",kernelFunc:U7};var H7=gr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,q7=`
|
|
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;
|
|
`,K7=ke({opSnippet:H7,packedOpSnippet:q7}),V$={kernelName:_o,backendName:"webgl",kernelFunc:K7};var X7=gr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Y7=`
|
|
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;
|
|
`,Z7=ke({opSnippet:X7,packedOpSnippet:Y7}),G$={kernelName:Co,backendName:"webgl",kernelFunc:Z7};var Kv=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],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 Xv=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],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 < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function J7(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=j().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Xv(o.shape,l,u,s,a):new Kv(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var W$={kernelName:vo,backendName:"webgl",kernelFunc:J7};var Yv=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&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), ${o-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 Q7(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new Yv(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var j$={kernelName:lu,backendName:"webgl",kernelFunc:Q7};var Zv=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"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 eY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=new Zv(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var U$={kernelName:pa,backendName:"webgl",kernelFunc:eY};var Jv=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&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(${o}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${s}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function tY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new Jv(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var H$={kernelName:au,backendName:"webgl",kernelFunc:tY};var Qv=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let o=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>o(l)).join(","),a=Le(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${s}));
|
|
}
|
|
`}};var eC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let o=jt("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,i=Le(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${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(o.slice())};
|
|
if(${s}){
|
|
result.g = ${u(o.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${c(o.slice())};
|
|
if(${s}) {
|
|
result.a = ${p(o.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function l(d){return m(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=e.map((w,b)=>f(b,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 rY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=o.shape.length,i=y.parseAxisParam(s,o.shape);if(a===0)return Ut({inputs:{x:o},backend:t});let l=j().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eC(o.shape,i):new Qv(o.shape,i);return t.runWebGLProgram(l,[o],o.dtype)}var q$={kernelName:Io,backendName:"webgl",kernelFunc:rY};var tC=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],o=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 < ${o} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,o){return(s,a)=>{this.paramsLoc==null&&(this.paramsLoc=s.getUniformLocationNoThrow(a,"params")),s.gl.uniform4f(this.paramsLoc,e,t,n,o)}}};var K$={kernelName:$i,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:n}=r,{radians:o,fillValue:s,center:a}=e,i=t,l=new tC(n.shape,s),[u,c]=S.getImageCenter(a,n.shape[1],n.shape[2]),p=l.getCustomSetupFunc(u,c,Math.sin(o),Math.cos(o));return i.runWebGLProgram(l,[n],n.dtype,p)}};var nY=`
|
|
// 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;
|
|
}
|
|
}
|
|
`,oY=ke({opSnippet:nY}),X$={kernelName:No,backendName:"webgl",kernelFunc:oY};var sY="return inversesqrt(x);",iY=ke({opSnippet:sY,cpuKernelImpl:VE}),Y$={kernelName:So,backendName:"webgl",kernelFunc:iY};var Df=class{constructor(e,t,n,o,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===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 aY(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o,updates:s}=e,{shape:a}=n,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=S.calculateShapes(s,o,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,o.dtype);let f=pe({inputs:{x:o},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 Df(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),w=pe({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),w}var Z$={kernelName:Ci,backendName:"webgl",kernelFunc:aY};var rC=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="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]}`);o=l.join(),s=u.join()}let a=Le(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${o});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${s}));
|
|
} else {
|
|
setOutput(getB(${s}));
|
|
}
|
|
}
|
|
`}};function lY(r){let{inputs:e,backend:t}=r,{condition:n,t:o,e:s}=e,a=new rC(n.shape.length,o.shape,o.shape.length);return t.runWebGLProgram(a,[n,o,s],dr(o.dtype,s.dtype))}var J$={kernelName:hs,backendName:"webgl",kernelFunc:lY};var uY=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${S.SELU_SCALEALPHA};
|
|
float scale = ${S.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,cY=ke({opSnippet:uY}),Q$={kernelName:Ii,backendName:"webgl",kernelFunc:cY};var pY="return 1.0 / (1.0 + exp(-1.0 * x));",mY=ke({opSnippet:pY}),eR={kernelName:Ao,backendName:"webgl",kernelFunc:mY};var fY=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,dY=ke({opSnippet:fY}),tR={kernelName:Si,backendName:"webgl",kernelFunc:dY};var hY=jg+`
|
|
return sin(x);
|
|
`,gY=ke({opSnippet:hY}),rR={kernelName:To,backendName:"webgl",kernelFunc:gY};var xY=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,yY=ke({opSnippet:xY}),nR={kernelName:Ni,backendName:"webgl",kernelFunc:yY};var bY=`
|
|
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;
|
|
`,wY=ke({opSnippet:bY}),oR={kernelName:Ti,backendName:"webgl",kernelFunc:wY};var kY=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n;y.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,w)=>x*w),l=[[0,0]];l.push(...a);for(let x=1+s.length;x<o.shape.length;++x)l.push([0,0]);let u=[],c=Hv({inputs:{x:o},backend:t,attrs:{paddings:l,constantValue:0}}),p=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,i,!1),d=pe({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Lt({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},sR={kernelName:ma,backendName:"webgl",kernelFunc:kY};function _Y(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=!1,f=new Df(u,l,o.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,o,a],s.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var iR={kernelName:uu,backendName:"webgl",kernelFunc:_Y};function vY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=y.parseAxisParam(a,o.shape)[0],l=S.prepareSplitSize(o,s,i),u=o.shape.length,c=new Array(u).fill(0),p=o.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Ma({inputs:{x:o},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var aR={kernelName:xs,backendName:"webgl",kernelFunc:vY};var CY="return sqrt(x);",IY=ke({opSnippet:CY}),lR={kernelName:Eo,backendName:"webgl",kernelFunc:IY};var NY="return x * x;",SY=ke({opSnippet:NY}),uR={kernelName:fa,backendName:"webgl",kernelFunc:SY};var cR="return (a - b) * (a - b);",TY=ot({opSnippet:cR,packedOpSnippet:cR}),pR={kernelName:Ro,backendName:"webgl",kernelFunc:TY};function AY({inputs:r,attrs:e,backend:t}){let{x:n}=r,o=gr+`
|
|
return x > 0.0 ? 1.0 : float(${e.alpha});
|
|
`,s=new ln(n.shape,o);return t.runWebGLProgram(s,[n],n.dtype)}var mR={kernelName:On,backendName:"webgl",kernelFunc:AY};var nC=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=Le(n.length),a=Le(n.length),i="";if(o===1)i="coords * strides + begin";else{let l=0;i=n.map((u,c)=>(l++,n.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 EY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:w}=sr.sliceInfo(o.shape,s,a,i,l,u,c,p,m),b=pe({inputs:{x:o},backend:t,attrs:{shape:x}}),k;if(f){let A=Ma({inputs:{x:b},backend:t,attrs:{begin:d,size:g}});k=pe({inputs:{x:A},backend:t,attrs:{shape:w}}),t.disposeIntermediateTensorInfo(A)}else if(w.some(A=>A===0))k=t.makeTensorInfo(w,o.dtype,[]);else if(t.shouldExecuteOnCPU([b])){let $=t.texData.get(b.dataId).values,F=ve(b.shape,b.dtype,$),M=WE(w,F,h,d);k=t.makeTensorInfo(w,b.dtype,M.values)}else{let N=new nC(d,h,w);k=t.runWebGLProgram(N,[b],b.dtype)}let _=pe({inputs:{x:k},backend:t,attrs:{shape:w}});return t.disposeIntermediateTensorInfo(b),t.disposeIntermediateTensorInfo(k),_}var fR={kernelName:Ai,backendName:"webgl",kernelFunc:EY};var DY="return tan(x);",$Y=ke({opSnippet:DY}),dR={kernelName:Ei,backendName:"webgl",kernelFunc:$Y};var RY=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,FY=ke({opSnippet:RY}),hR={kernelName:Oo,backendName:"webgl",kernelFunc:FY};var oC=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let o=Le(this.rank),s=OY(e);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function OY(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"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${t[o]}, ${r[o]})`);return n.join()}function sC(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;if(o.dtype==="string"){let u=t.readSync(o.dataId).map(m=>y.decodeString(m)),c=ve(o.shape,o.dtype,u),p=UE(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new oC(o.shape,s);return t.runWebGLProgram(a,[o],o.dtype)}var gR={kernelName:kn,backendName:"webgl",kernelFunc:sC};function PY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:s,sorted:a}=n,i=t.readSync(o.dataId),[l,u]=HE(i,o.shape,o.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var xR={kernelName:Di,backendName:"webgl",kernelFunc:PY};var iC=class{constructor(e,t,n,o,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,l;switch(o){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 MY(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=n,[c,p,m,f]=o.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],x=new iC(p,m,a,i,l,g);return t.runWebGLProgram(x,[o,s],"float32")}var yR={kernelName:cu,backendName:"webgl",kernelFunc:MY};function LY(r){let{inputs:e,attrs:t,backend:n}=r,{axis:o}=t,{x:s}=e;Rs(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=n.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=qE(a,o,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,i),n.makeTensorInfo([u.length],"int32",u)]}var bR={kernelName:pu,backendName:"webgl",kernelFunc:LY};function zY(r){let{inputs:e,backend:t,attrs:n}=r,{value:o}=e,{axis:s}=n;s<0&&(s+=o.shape.length);let a=o,i=a.shape.length,l=o.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=Ma({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 wR={kernelName:ys,backendName:"webgl",kernelFunc:zY};var aC=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,o=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/n);this.outputShape=[o,i];let l="0.0",u="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
|
|
sumValue += dot(values, segFilter);
|
|
`,f="";s%n>0&&(f=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`);let d="";s%n>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(${n}));
|
|
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 BY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,segmentIds:s}=e,{numSegments:a}=n,i=o.shape.length,l=[],u=0,c=S.getAxesPermutation([u],i),p=o;c!=null&&(p=Lt({inputs:{x:o},backend:t,attrs:{perm:c}}),l.push(p),u=S.getInnerMostAxes(1,i)[0]);let m=S.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=fu(o.dtype),g=(k,_,A,N,$)=>{let F=k.shape[0],M=k.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,$),W={windowSize:V,inSize:M,batchSize:F,numSegments:$},U=new aC(W,_),H=t.compileAndRun(U,[k,A],N);if(l.push(H),H.shape[1]===$)return H;let q=qv({backend:t,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),X=sC({inputs:{x:q},backend:t,attrs:{reps:[M/V]}});return l.push(q),l.push(X),g(H,_,X,N,$)},x=g(d,"unsortedSegmentSum",s,h,a),w=pe({inputs:{x},backend:t,attrs:{shape:m}}),b=w;if(c!=null){l.push(w);let k=S.getUndoAxesPermutation(c);b=Lt({inputs:{x:b},backend:t,attrs:{perm:k}})}return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),b}var kR={kernelName:da,backendName:"webgl",kernelFunc:BY};var VY=[o$,s$,b2,k2,_2,v2,I2,N2,S2,T2,D2,$2,R2,F2,P2,O2,M2,z2,L2,B2,V2,G2,W2,U2,H2,Y2,J2,Q2,tD,a2,oD,iD,aD,sD,uD,cD,lD,pD,mD,fD,gD,xD,yD,wD,kD,bD,_D,vD,CD,ID,ND,SD,AD,ED,$D,RD,FD,OD,MD,LD,zD,BD,VD,GD,WD,jD,UD,i2,HD,rD,qD,KD,XD,l2,YD,ZD,JD,e$,QD,t$,r$,n$,a$,c$,u$,p$,m$,d$,l$,g$,x$,y$,b$,w$,I$,f2,S$,T$,A$,E$,q2,D$,F$,O$,P$,M$,u2,L$,z$,K2,k$,B$,G$,V$,h2,W$,j$,U$,H$,q$,K$,X$,Y$,Z$,J$,Q$,eR,tR,rR,nR,j2,C$,oR,sR,iR,aR,lR,uR,pR,mR,fR,v$,x2,dR,hR,gR,xR,yR,y2,bR,wR,kR,$$];for(let r of VY)el(r);var zt;(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"})(zt||(zt={}));var yl;(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"})(yl||(yl={}));var _R;function GY(r){_R=r.wasm.cwrap(ws,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function WY(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e;if(o.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}=n,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let $=t.dataIdMap.get(a.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);d=$.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=yl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=l?o.shape[2]:o.shape[1],w=u?s.shape[1]:s.shape[2],b=o.shape[0],k=t.makeOutput([b,x,w],o.dtype),_=t.dataIdMap.get(k.dataId).id,A=new Uint8Array(new Int32Array(o.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return _R(m,A,o.shape.length,f,N,s.shape.length,l,u,g,d,h,p||0,_),k}var vR={kernelName:ws,backendName:"wasm",setupFunc:GY,kernelFunc:WY};function St(r){let e;function t(o){e=o.wasm.cwrap(r,null,["number","number"])}function n(o){let{backend:s,inputs:{x:a}}=o,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:n}}var CR=St(as);function yt(r,e,t){let n;function o(a){n=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=S.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),w=i.dataIdMap.get(h.dataId).id,b=()=>n(p,g,u.shape.length,m,x,c.shape.length,zt[u.dtype],w);if(e&&u.dtype==="float32")return b(),h;let k=S.getBroadcastDims(u.shape,d),_=S.getBroadcastDims(c.shape,d),A=k.every(($,F)=>$===F),N=_.every(($,F)=>$===F);if(A&&N)return b(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var jY=!0,IR=yt(wn,jY);var NR;function UY(r){NR=r.wasm.cwrap(Hn,null,["array","number","number","number"])}function HY(r){let{inputs:e,backend:t}=r,n=t.makeOutput(e[0].shape,e[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),a=t.dataIdMap.get(n.dataId).id;return NR(s,o.length,zt[n.dtype],a),n}var SR={kernelName:Hn,backendName:"wasm",setupFunc:UY,kernelFunc:HY};function nc(r){let{inputs:{x:e},backend:t}=r,n=t.makeOutput(e.shape,e.dtype),o=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(n).set(o),n}var TR={kernelName:Fn,backendName:"wasm",kernelFunc:nc};var AR;function qY(r){AR=r.wasm.cwrap(Po,null,["number","array","number","number","number","array","number"])}function vp(r){let{inputs:e,backend:t,attrs:n}=r,[o,s]=XY(e.x.shape,n.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=KY(e.x.shape,n.perm),l={dataId:e.x.dataId,shape:o,dtype:e.x.dtype};if(a){let d=nc({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 AR(c,f,l.shape.length,zt[l.dtype],p,m,s.length),u}function KY(r,e){let t=new Array(r.length);for(let n=0;n<t.length;n++)t[n]=r[e[n]];return t}function XY(r,e){let t=[],n=[];for(let o=0;o<r.length;++o)r[o]!==1&&t.push(r[o]),r[e[o]]!==1&&n.push(e[o]);for(let o=0;o<n.length;++o){let s=-1;for(let a=0;a<n.length;++a)n[a]>=o&&(s===-1||n[s]>n[a])&&(s=a);n[s]=o}return[t,n]}var ER={kernelName:Po,backendName:"wasm",kernelFunc:vp,setupFunc:qY};function Yo(r,e,t){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(e,n),a=s,i=S.getAxesPermutation(a,o),l=null,u=!1;if(i!=null){let c=new Array(o);for(let f=0;f<c.length;f++)c[f]=n[i[f]];a=S.getInnerMostAxes(a.length,o),l=vp({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 DR;function YY(r){DR=r.wasm.cwrap(qn,null,["number","number","number","number","number"])}function ZY(r){let{backend:e,inputs:t,attrs:n}=r,{axis:o}=n,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=a,l=s,{transposed:u,axes:c,inputWasTransposed:p}=Yo(s,o,e);if(p){let x=e.dataIdMap.get(u.dataId).id;x!==a&&(l=u,i=x)}let 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They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],w=m.nodeIndices[g],b=m.tensorIndices[g],k=Wn.nodeKey(x,w),_=t[k];_==null&&(_=0),h.push([x.name,_,b,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,n.push(c)}e.layers=n;let o=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=Wn.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];o.push([i.name,c,p])}e.inputLayers=o;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=Wn.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,n={},o=!1){let s={},a={};function i(g,x){g.name in a?a[g.name].push(x):a[g.name]=[x]}function l(g,x){let w=[],b;for(let k of x){let _=k[0],A=k[1],N=k[2];if(b=k[3]==null?{}:k[3],!(_ in s)){i(g,x);return}let $=s[_];if($.inboundNodes.length<=A){i(g,x);return}let F=$.inboundNodes[A];w.push(F.outputTensors[N])}w.length>0&&g.apply(xr(w),b)}function u(g){let x=g.name,w=Qr(g,t.customObjects!=null?t.customObjects:{});w.setFastWeightInitDuringBuild(o),s[x]=w,g.inboundNodes.forEach(k=>{if(!(k instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${k}`);i(w,k)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!qM(a);)for(let g of p){let x=s[g.name];if(x.name in a){let w=a[x.name];delete a[x.name];for(let b of w)l(x,b)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let x=g[0],w=g[1],b=g[2];Gn(x in s);let _=s[x].inboundNodes[w].outputTensors;m.push(_[b])}let h=t.outputLayers;for(let g of h){let x=g[0],w=g[1],b=g[2];Gn(x in s);let _=s[x].inboundNodes[w].outputTensors;f.push(_[b])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){G(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function pQ(r,e,t){let n=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(o=>null);if(n===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==n)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let o=[];return e.forEach(s=>{s in r?o.push(r[s]):o.push(null)}),o}else throw new Error(`The model has multiple (${n}) outputs, so ${t} must be either an array with ${n} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function $x(r,e){return pQ(r,e,"classWeight")}async function Rx(r,e,t,n){if(e!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let o=G(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 o.data());Ae(o);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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(Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l<s.length;l++)y.assert(s[l].shape[0]===i,()=>`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l<a.length;l++)y.assert(a[l].shape[0]===i,()=>`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function DL(r,e,t){if(t instanceof Ve)return[t];if(Array.isArray(t))return y.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let n=[];for(let o of e){if(t[o]==null)throw new z(`The feature data generated by the dataset lacks the required ${r} key '${o}'.`);n.push(t[o])}return n}}function fQ(r){if(r.length===3)throw new Se("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function FL(r,e,t){let n=t.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(t!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(t.epochs!=null&&t.epochs>0&&Number.isInteger(t.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${t.epochs}`),y.assert(!n||t.batchesPerEpoch>0&&Number.isInteger(t.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${t.batchesPerEpoch}`),y.assert(t.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),o=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let p=[];for(let h=0;h<this.inputs.length;++h)p.push({key:this.inputs[h],value:n[h]});let m=new zs(p),f=pc(this.outputs,m,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let x=this.lossFunctions[h](o[h],f[h]);s[h]!=null&&(x=EL(x,s[h]));let w=ht(x);t.push(w),h===0?d=x:d=ee(d,x)}for(let h=0;h<this.metricsTensors.length;++h){let g;if(this.outputs.length>1&&h<this.outputs.length)g=t[h];else{let x=this.metricsTensors[h][0],w=this.metricsTensors[h][1];g=ht(x(o[w],f[w]))}Dt(g),a.push(g)}return d=ht(d),this.calculateLosses().forEach(h=>{d=ee(d,h)}),d},l=this.collectedTrainableWeights.map(p=>p.read()),u=!0;return[this.optimizer_.minimize(i,u,l)].concat(a)}}makeTestFunction(){this.testFunction=e=>G(()=>{let 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stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=qc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-qc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Jo(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Jo(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let o of t)if(typeof n[o]=="string")e[o]=Jo(n[o]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)s[a]=za(e.metrics[a])}this.compile({loss:o,metrics:s,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=Ir.getSaveHandlers(e);if(u.length===0)throw new z(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new z(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new z("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Ir.encodeWeights(this.getNamedWeights(t)),o=!1,s=null,i={modelTopology:this.toJSON(s,o),format:kQ,generatedBy:`TensorFlow.js tfjs-layers v${Wp}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:p}=await Ir.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...p),n.data=Ir.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let u=!0;PC(this.userDefinedMetadata,this.name,u),i.userDefinedMetadata=this.userDefinedMetadata}return i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){PC(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Tn.className="Model";Q.registerClass(Tn);var BC=class extends Tn{};BC.className="Functional";Q.registerClass(BC);async function BL(r,e){"modelTopology"in r||(r={modelTopology:r}),r=r;let t=r.modelTopology;t.model_config!=null&&(t=t.model_config);let n=cc(t),o=Qr(n,e);if(r.weightsManifest!=null){let s=await Ir.loadWeights(r.weightsManifest,r.pathPrefix,o.weights.map(i=>i.originalName)),a={};for(let i of o.weights)a[i.originalName]=s[i.originalName];o.loadWeights(a),Ae(s)}return o}async function VL(r,e){if(e==null&&(e={}),typeof r=="string"){let t=Ir.getLoadHandlers(r,e);if(t.length===0)t.push(Ir.browserHTTPRequest(r,e));else if(t.length>1)throw new z(`Found more than one (${t.length}) load handlers for URL 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},o=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=t}else y.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof qi))throw new Se(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=Qr(l,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new z("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};qi.className="Sequential";Q.registerClass(qi);function GL(r){return new Tn(r)}function WL(r){return new qi(r)}function jL(r,e){return e==null&&(e={}),VL(r,e)}function Mx(r){return bx(r)}function UL(r,e){mn.registerCallbackConstructor(r,e)}var fn=class extends Q.Serializable{getConfig(){return{}}},VC=class extends fn{apply(e,t=1){return pL(e,t)}};VC.className="elu";Q.registerClass(VC);var GC=class extends fn{apply(e){return Ou(e)}};GC.className="selu";Q.registerClass(GC);var WC=class extends fn{apply(e){return Sr(e)}};WC.className="relu";Q.registerClass(WC);var jC=class extends fn{apply(e){return G(()=>As(6,Sr(e)))}};jC.className="relu6";Q.registerClass(jC);var UC=class extends fn{apply(e){return e}};UC.className="linear";Q.registerClass(UC);var HC=class extends fn{apply(e){return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};cd.className="ThresholdedReLU";Q.registerClass(cd);var pd=class extends Me{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new sd().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Fe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};pd.className="Softmax";Q.registerClass(pd);function Il(r,e,t){if(typeof r=="number")return Zo(r,e);if(r.length!==e)throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. 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Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function dn(r,e,t,n,o=1){if(r==null)return r;let s=e+(e-1)*(o-1),a;return t==="same"?a=r:a=r-s+1,Math.floor((a+n-1)/n)}function md(r,e,t,n){if(r==null)return null;if(n==="valid")r=r*e+Ls([t-e,0]);else if(n==="same")r=r*e;else throw new z(`Unsupport padding mode: ${n}.`);return r}function fd(r,e){return G(()=>(Rt(e),e==="channelsFirst"?Ue(r,[0,2,3,1]):r))}function r0(r,e){return G(()=>(Rt(e),e==="channelsFirst"?Ue(r,[0,2,3,4,1]):r))}function CQ(r,e,t,n=1,o="valid",s,a=1){return G(()=>{if(s==null&&(s=Zr()),Rt(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),o==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=_u(r,e,n,o==="same"?"same":"valid","NWC",a);return t!=null&&(i=cn(i,t)),i})}function YL(r,e,t,n=[1,1],o="valid",s,a,i=null){return G(()=>{if(s==null&&(s=Zr()),Rt(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=fd(r,s);if(o==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Go.conv2d({x:l,filter:e,strides:n,pad:o==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function IQ(r,e,t,n=[1,1,1],o="valid",s,a){return G(()=>{if(s==null&&(s=Zr()),Rt(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 ${r.rank}.`);let i=r0(r,s);if(o==="causal")throw new Se("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=$m(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=cn(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Up=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Up.verifyArgs(t),this.rank=e,Ht(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=Il(t.kernelSize,e,"kernelSize"),this.strides=Il(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Jr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=Vs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=wt(t.biasRegularizer),this.activityRegularizer=wt(t.activityRegularizer),this.dilationRate=Il(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(Gn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!ux(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:Bs(this.activation),useBias:this.useBias,biasInitializer:vt(this.biasInitializer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},fc=class extends Up{constructor(e,t){super(e,t);this.kernel=null,fc.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=wt(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 n=e[t],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return G(()=>{e=Fe(e);let n,o=this.bias==null?null:this.bias.read(),s=cx(this.activation.getClassName());if(s!=null&&this.rank===2)n=YL(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=CQ(e,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=YL(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IQ(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Se("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Je(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=dn(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let o=[e[0]];return this.dataFormat==="channelsLast"?(o=o.concat(t),o.push(this.filters)):(o.push(this.filters),o=o.concat(t)),o}getConfig(){let e={filters:this.filters,kernelInitializer:vt(this.kernelInitializer),kernelRegularizer:st(this.kernelRegularizer),kernelConstraint:Ot(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)}`)}},Nl=class extends fc{constructor(e){super(2,e);Nl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!ux(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)}.`)}};Nl.className="Conv2D";Q.registerClass(Nl);var dc=class extends fc{constructor(e){super(3,e);dc.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)}.`)}};dc.className="Conv3D";Q.registerClass(dc);var dd=class extends Nl{constructor(e){super(e);if(this.inputSpec=[new Tt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Tt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return G(()=>{let n=Fe(e);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=o[a],u=o[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=md(l,m,c,this.padding),h=md(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let x=vu(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ue(x,[0,3,1,2])),this.bias!=null&&(x=cn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Je(e);let t=e.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[o]=md(t[o],l,a,this.padding),t[s]=md(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};dd.className="Conv2DTranspose";Q.registerClass(dd);var n0=class extends fc{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=wt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=wt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${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 n=e[t],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"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 Tt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return G(()=>{e=Fe(e);let n;if(this.rank===1)throw new Se("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),n=Hm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=cn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.pointwiseInitializer=vt(this.pointwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.pointwiseRegularizer=st(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};n0.className="SeparableConv";var hd=class extends n0{constructor(e){super(2,e)}};hd.className="SeparableConv2D";Q.registerClass(hd);var hc=class extends fc{constructor(e){super(1,e);hc.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"&&!ux(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)}.`)}};hc.className="Conv1D";Q.registerClass(hc);var gd=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 G(()=>{if(e=Fe(e),this.dataFormat==="channelsLast"){let n=Gf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};gd.className="Cropping2D";Q.registerClass(gd);var xd=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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,nL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return G(()=>{let n=Fe(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};xd.className="UpSampling2D";Q.registerClass(xd);function NQ(r,e,t=[1,1],n="valid",o,s){return G(()=>{o==null&&(o=Zr()),Rt(o);let a=fd(r,o);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=Is(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var yd=class extends Up{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=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=wt(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 n=e[t],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return G(()=>{e=Fe(e);let n=NQ(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=cn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=dn(t,this.kernelSize[0],this.padding,this.strides[0]),a=dn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};yd.className="DepthwiseConv2D";Q.registerClass(yd);function o0(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function s0(r,e,t,n=!1,o,s,a=!1,i=!1){return G(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(zr(2,l));if(e=Ue(e,u),s!=null)throw new Se("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===l-1&&(o=ar(o,-1)),o=Ue(o,u)),n&&(e=qt(e,0),o!=null&&(o=qt(o,0)));let c=[],p,m=t,f=e.shape[0],d=pr(e),h;o!=null&&(h=pr(o));for(let x=0;x<f;++x){let w=d[x],b=G(()=>r(w,m));if(o==null)p=b[0],m=b[1];else{let k=G(()=>{let _=h[x],A=tr(_).sub(_),N=b[0].mul(_).add(m[0].mul(A)),$=m.map((F,M)=>b[1][M].mul(_).add(F.mul(A)));return{output:N,newStates:$}});p=k.output,m=k.newStates}i&&c.push(p)}let g;return i&&(g=Vt(c,1)),[p,g,m]})}var hn=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 Hp({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 Tt({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 zr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){yx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return G(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Se("Constants support is not implemented in RNN yet.");yx(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,o=e.slice(2);this.inputSpec[0]=new Tt({shape:[n,null,...o]});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 Tt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){G(()=>{if(!this.stateful)throw new Sn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==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(o=>gt([n,o])):this.states_=[gt([n,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(o=>gt([n,o])):this.states_[0]=gt([n,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 o=0;o<this.states_.length;++o){let s=e[o],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,i=[n,a];if(!y.arraysEqual(s.shape,i))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Dt(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=o0(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Tt({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof Vr){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 G(()=>{let n=t==null?null:t.mask,o=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:o},u=s0((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return G(()=>{let t=gt(e.shape);return t=ge(t,[1,2]),t=Va(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?fx(t,[1,n]):t):this.cell.stateSize>1?[fx(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===hn.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=Qr(o,n);return new e(Object.assign(t,{cell:s}))}};hn.className="RNN";Q.registerClass(hn);var Sl=class extends Me{},qp=class extends Sl{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,Ht(this.units,"units"),this.activation=Vs(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=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ic([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ic([1,Ls([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 G(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>tr(e),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>tr(n),rate:this.recurrentDropout,training:o}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=rs(P(e,a),this.kernel.read()):s=rs(e,this.kernel.read()),this.bias!=null&&(s=cn(s,this.bias.read())),i!=null&&(n=P(n,i));let l=ee(s,rs(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};qp.className="SimpleRNNCell";Q.registerClass(qp);var bd=class extends hn{constructor(e){e.cell=new qp(e),super(e)}call(e,t){return G(()=>{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 n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};bd.className="SimpleRNN";Q.registerClass(bd);var Kp=class extends Sl{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,Ht(this.units,"units"),this.activation=Vs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vs(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=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ic([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ic([1,Ls([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 G(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>tr(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>tr(o),rate:this.recurrentDropout,training:n,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=rs(e,this.kernel.read());this.useBias&&(c=cn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=P(o,a[0]));let p=this.recurrentKernel.read(),[m,f]=cr(p,[2*this.units,this.units],p.rank-1),d=rs(o,m),[h,g,x]=cr(c,3,c.rank-1),[w,b]=cr(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,w)),l=this.recurrentActivation.apply(ee(g,b));let k=rs(P(l,o),f);u=this.activation.apply(ee(x,k));let _=ee(P(i,o),P(ee(1,He(i)),u));return[_,_]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),recurrentActivation:Bs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Kp.className="GRUCell";Q.registerClass(Kp);var wd=class extends hn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Kp(e),super(e)}call(e,t){return G(()=>{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 n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};wd.className="GRU";Q.registerClass(wd);var Tl=class extends Sl{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,Ht(this.units,"units"),this.activation=Vs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vs(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=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ic([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ic([1,Ls([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 n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends pn{apply(l,u){let c=s.apply([a]),p=new lc().apply([a]),m=s.apply([a*2]);return AC(AC(c,p),m)}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return G(()=>{let n=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 o=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>tr(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>tr(o),rate:this.recurrentDropout,training:n,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=rs(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=P(o,i[0])),m=ee(m,rs(o,this.recurrentKernel.read())),this.useBias&&(m=cn(m,this.bias.read()));let[f,d,h,g]=cr(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:Bs(this.activation),recurrentActivation:Bs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Tl.className="LSTMCell";Q.registerClass(Tl);var kd=class extends hn{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 Tl(e),super(e)}call(e,t){return G(()=>{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 n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};kd.className="LSTM";Q.registerClass(kd);var Hp=class extends Sl{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 G(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];n=o[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=l.call(a,t),s.push(a.slice(1))}n=[];for(let i of s.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){yx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,o)=>{Ms(`RNNCell_${o}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(Qr(s,n));return new e({cells:o})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Zf(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}zp(t)}};Hp.className="StackedRNNCells";Q.registerClass(Hp);function Wa(r){let{ones:e,rate:t,training:n=!1,count:o=1}=r,s=()=>hx(e(),t),a=()=>bl(s,e,n);return!o||o<=1?Dt(a().clone()):Array(o).fill(void 0).map(a).map(l=>Dt(l.clone()))}var SQ=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)e.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(t[n[o]]=r[n[o]]);return t};var i0=class extends hn{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 Tt({ndim:5})]}call(e,t){return G(()=>{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 n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,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 G(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=gt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){G(()=>{if(!this.stateful)throw new Sn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[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(()=>gt(s)):this.states_=[gt(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(()=>gt(s)):this.states_[0]=gt(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=>Dt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=dn(u,o[0],s,a[0],i[0]),m=dn(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};i0.className="ConvRNN2D";var Xp=class extends Tl{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Il(n,2,"kernelSize"),this.kernelSize.forEach(l=>Ht(l,"kernelSize")),this.strides=Il(o||1,2,"strides"),this.strides.forEach(l=>Ht(l,"strides")),this.padding=s||"valid",Jr(this.padding),this.dataFormat=a||"channelsLast",Rt(this.dataFormat),this.dilationRate=Il(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Ht(l,"dilationRate"))}build(e){var t;e=Je(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,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 pn{apply(m,f){let d=u.apply([c]),h=Nr([c]),g=u.apply([c*2]);return Ap([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 G(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>tr(o),rate:this.dropout,training:n,count:i}));let l=this.dropoutMask,u=(J,ie,ue)=>!ie||!ie[ue]?J:P(ie[ue],J),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>tr(s),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),w=u(s,d,3),b=3,[k,_,A,N]=cr(this.kernel.read(),i,b),[$,F,M,V]=this.useBias?cr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,k,$,this.padding),p=this.inputConv(p,_,F,this.padding),m=this.inputConv(m,A,M,this.padding),f=this.inputConv(f,N,V,this.padding);let[W,U,H,q]=cr(this.recurrentKernel.read(),i,b);h=this.recurrentConv(h,W),g=this.recurrentConv(g,U),x=this.recurrentConv(x,H),w=this.recurrentConv(w,q);let X=this.recurrentActivation.apply(ee(c,h)),ne=this.recurrentActivation.apply(ee(p,g)),Y=ee(P(ne,a),P(X,this.activation.apply(ee(m,x)))),re=P(this.recurrentActivation.apply(ee(f,w)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=SQ(e,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(e,t,n,o){let s=Kr(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?cn(s,n,this.dataFormat):s}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xp.className="ConvLSTM2DCell";Q.registerClass(Xp);var _d=class extends i0{constructor(e){let t=new Xp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};_d.className="ConvLSTM2D";Q.registerClass(_d);var Yp=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,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?t[o]:this.noiseShape[o]);return n}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Fe(e);if(0<this.rate&&this.rate<1){let o=t.training==null?!1:t.training,s=this.getNoiseShape(n);return bl(()=>hx(n,this.rate,s,this.seed),()=>n,o)}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()}};Yp.className="Dropout";Q.registerClass(Yp);var vd=class extends Yp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};vd.className="SpatialDropout1D";Q.registerClass(vd);var Cd=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,Ht(this.units,"units"),this.activation=Vs(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=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=wt(e.kernelRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.activityRegularizer=wt(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 G(()=>{this.invokeCallHook(e,t);let n=Fe(e),o=cx(this.activation.getClassName()),s;return o!=null?s=rs(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=rs(n,this.kernel.read()),this.bias!=null&&(s=cn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Bs(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Cd.className="Dense";Q.registerClass(Cd);var Id=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],ts(e,1)]}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Fe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=n.transpose(o)}return cL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Id.className="Flatten";Q.registerClass(Id);var Nd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vs(e.activation)}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Fe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Bs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Nd.className="Activation";Q.registerClass(Nd);var Sd=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 G(()=>(e=Fe(e),lL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Sd.className="RepeatVector";Q.registerClass(Sd);var Td=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 n="Total size of new array must be unchanged.",o=t.slice(),s=1,a=null;for(let l=0;l<o.length;++l){let u=o[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=ts(e);if(a!==null){if(s===0||i%s!=0)throw new z(n);o[a]=i/s}else if(i!==s)throw new z(n);return o}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Fe(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return n.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Td.className="Reshape";Q.registerClass(Td);var Ad=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=zr(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 Tt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Je(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return Ue(Fe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ad.className="Permute";Q.registerClass(Ad);var Ed=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 n=Fe(e),o=-1;return sl(Vo(n,this.maskValue),o)}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Fe(e),o=-1,s=!0,a=sl(Vo(n,this.maskValue),o,s);return n.mul(a.asType(n.dtype))})}};Ed.className="Masking";Q.registerClass(Ed);var Dd=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(bt(e.inputLength))}this.inputDim=e.inputDim,Ht(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ht(this.outputDim,"outputDim"),this.embeddingsInitializer=pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=wt(e.embeddingsRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.embeddingsConstraint=Pt(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 G(()=>this.maskZero?(e=Fe(e),Vo(e,Ce(e))):null)}computeOutputShape(e){if(e=Je(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let o=0;o<t.length;++o){let s=t[o],a=e[o+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[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Fe(e);return n.dtype!=="int32"&&(n=Ba(n,"int32")),dx(this.embeddings.read(),n.as1D()).reshape(Je(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:vt(this.embeddingsInitializer),embeddingsRegularizer:st(this.embeddingsRegularizer),activityRegularizer:st(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Dd.className="Embedding";Q.registerClass(Dd);var Al=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 n=e.slice(0,e.length-t.length);for(let o=0;o<t.length;++o){let s=e[e.length-t.length+o],a=t[o];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}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=es(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let o=e.map(s=>s.length);e.indexOf(null)===-1&&es(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return G(()=>{if(e=e,this.reshapeRequired){let n=[],o=e.map(s=>s.rank);if(o.indexOf(null)===-1){let s=Ls(o);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Va(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(ts(c.slice(1))));f=Ue(f,[1,0]),f=f.reshape(m),n.push(f),s=!0}else if(u>1){let c=zr(1,u).concat([0]);n.push(Ue(l,c)),s=!0}else n.push(l)}let a=this.mergeFunction(n),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ue(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(zr(0,i-1));a=Ue(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let o=1;o<e.length;++o){let s=e[o]==null?null:e[o].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let o of e)o!=null&&o[0]!==null&&n.push(o[0]);return n=es(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return G(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(o=>o==null))return null;t=t.map(o=>o==null?o:ar(o,0));let n=t[0];for(let o=1;o<t.length-1;++o)n=hr(n,t[o]);return n})}},$d=class extends Al{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ee(t,e[n]);return t})}};$d.className="Add";Q.registerClass($d);var Rd=class extends Al{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=P(t,e[n]);return t})}};Rd.className="Multiply";Q.registerClass(Rd);var Fd=class extends Al{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ee(t,e[n]);return P(1/e.length,t)})}};Fd.className="Average";Q.registerClass(Fd);var Od=class extends Al{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Yr(t,e[n]);return t})}};Od.className="Maximum";Q.registerClass(Od);var Pd=class extends Al{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=As(t,e[n]);return t})}};Pd.className="Minimum";Q.registerClass(Pd);var Md=class extends Al{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 o of e)if(o!=null){t=!1;break}if(t)return;let n=[];for(let o=0;o<e.length;++o){let s=e[o].slice();s.splice(this.axis,1);let a=!1;for(let i of n)if(y.arraysEqual(i,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return G(()=>Ap(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of t.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return G(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let o=[];for(let a=0;a<e.length;++a)t[a]==null?o.push(tr(e[a]).asType("bool")):t[a].rank<e[a].rank?o.push(ar(t[a],-1)):o.push(t[a]);let s=Ze(o,this.axis);return bu(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Md.className="Concatenate";Q.registerClass(Md);function Ld(r,e){for(;r<0;)r+=e;return r}function TQ(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 n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return G(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(o>n){a=o-n;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;n>o?l=n+o-3:l=n-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 zd=class extends Al{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],n=e[1];if(t.length>3||n.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[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],n=e[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,a)=>Ld(s,e[a].shape.length)):o=[Ld(this.axes,t.shape.length),Ld(this.axes,n.shape.length)],this.normalize&&(t=Jf(t,o[0]),n=Jf(n,o[1])),TQ(t,n,o)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ld(this.axes,e.length),Ld(this.axes,t.length)],n}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(),n=e[1].slice();if(t.length>3||n.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);t.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=t.concat(n);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}};zd.className="Dot";Q.registerClass(zd);var Bd=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 G(()=>{this.invokeCallHook(e,t);let n=Fe(e);return bl(()=>Ep(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Bd.className="GaussianNoise";Q.registerClass(Bd);var Vd=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 G(()=>{this.invokeCallHook(e,t);let n=Fe(e);return this.rate>0&&this.rate<1?bl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return n.mul(Ep(n.shape,1,s))},()=>n,t.training||!1):n})}};Vd.className="GaussianDropout";Q.registerClass(Vd);var Gd=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 G(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return bl(()=>{let s=Fe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=an(Es(n),this.rate);u=Ba(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})}};Gd.className="AlphaDropout";Q.registerClass(Gd);function Wd(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=ww(r,e,t,n,o,s);else if(r.rank===3)a=kw(r,e,t,n,o,s);else if(r.rank===4)a=_w(r,e,t,n,o,s);else throw new Se(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function AQ(r,e,t,n,o=.001){return G(()=>{let s=Zc(r,n),a=s.mean,i=s.variance;return[Wd(r,a,i,t,e,o),a,i]})}function EQ(r,e,t,n,o=.001){return G(()=>{let s=Zc(r,n),a=s.mean,i=s.variance,l=[];for(let d of zr(0,r.rank))n.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[Wd(r,u,c,m,p,o),a,i]})}function DQ(r,e,t,n,o=.001){return y.arraysEqual(n.slice().sort(),zr(0,r.rank-1))?AQ(r,e,t,n,o):EQ(r,e,t,n,o)}var jd=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=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer)}build(e){e=Je(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==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 Tt({ndim:e.length,axes:{[t]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return G(()=>{let n=t.training==null?!1:t.training,o=Fe(e),s=o.shape,a=s.length,i=zr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Zo(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,zr(0,a).slice(0,a-1)),m=()=>{if(p){let w=this.movingMean.read().reshape(u),b=this.movingVariance.read().reshape(u),k=this.center?this.beta.read().reshape(u):null,_=this.scale?this.gamma.read().reshape(u):null;return Wd(o,w,b,k,_,this.epsilon)}else return Wd(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=DQ(o,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(w,b,k)=>{G(()=>{let _=1-k,A=w.read(),N=A.sub(b).mul(_);w.write(A.sub(N))})};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:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),movingMeanInitializer:vt(this.movingMeanInitializer),movingVarianceInitializer:vt(this.movingVarianceInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};jd.className="BatchNormalization";Q.registerClass(jd);var Ud=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=wt(e.betaRegularizer),this.gammaRegularizer=wt(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!==es(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(e,t){let n=Fe(e),o=n.shape,s=o.length;return G(()=>{let a=!0,{mean:i,variance:l}=Zc(n,this.axis,a),u=Zo(1,s);for(let h of this.axis)u[h]=o[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(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),Wd(n,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ud.className="LayerNormalization";Q.registerClass(Ud);function $Q(r,e,t){return G(()=>{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=Zr()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Tt({ndim:4})]}computeOutputShape(e){e=Je(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return G(()=>$Q(Fe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Hd.className="ZeroPadding2D";Q.registerClass(Hd);function Lx(r,e,t,n,o,s){return G(()=>{Rt(o),SC(s),Jr(n),t==null&&(t=[1,1]),n==null&&(n="valid"),o==null&&(o=Zr()),s==null&&(s="max"),r=fd(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Na(r,e,t,i):a=wa(r,e,t,i),o==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function ZL(r,e,t,n,o,s){return G(()=>{Rt(o),SC(s),Jr(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=Zr()),s==null&&(s="max"),r=r0(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Vm(r,e,t,i):a=Em(r,e,t,i),o==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var a0=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(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Jr(this.padding),this.inputSpec=[new Tt({ndim:3})]}computeOutputShape(e){e=Je(e);let t=dn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return G(()=>{this.invokeCallHook(e,t),e=Va(Fe(e),2);let n=this.poolingFunction(Fe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Cn(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},qd=class extends a0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),Jr(o),Lx(e,t,n,o,s,"max")}};qd.className="MaxPooling1D";Q.registerClass(qd);var Kd=class extends a0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),Jr(o),Lx(e,t,n,o,s,"avg")}};Kd.className="AveragePooling1D";Q.registerClass(Kd);var l0=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Jr(this.padding),this.inputSpec=[new Tt({ndim:4})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=dn(t,this.poolSize[0],this.padding,this.strides[0]),n=dn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return G(()=>(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}},Xd=class extends l0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),Jr(o),Lx(e,t,n,o,s,"max")}};Xd.className="MaxPooling2D";Q.registerClass(Xd);var Yd=class extends l0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),Jr(o),Lx(e,t,n,o,s,"avg")}};Yd.className="AveragePooling2D";Q.registerClass(Yd);var u0=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];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Jr(this.padding),this.inputSpec=[new Tt({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=dn(t,this.poolSize[0],this.padding,this.strides[0]),n=dn(n,this.poolSize[1],this.padding,this.strides[1]),o=dn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return G(()=>(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}},Zd=class extends u0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),Jr(o),ZL(e,t,n,o,s,"max")}};Zd.className="MaxPooling3D";Q.registerClass(Zd);var Jd=class extends u0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Rt(s),Jr(o),ZL(e,t,n,o,s,"avg")}};Jd.className="AveragePooling3D";Q.registerClass(Jd);var c0=class extends Me{constructor(e){super(e);this.inputSpec=[new Tt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Se}},Qd=class extends c0{constructor(e){super(e||{})}call(e,t){return G(()=>{let n=Fe(e);return ht(n,1)})}};Qd.className="GlobalAveragePooling1D";Q.registerClass(Qd);var eh=class extends c0{constructor(e){super(e||{})}call(e,t){return G(()=>{let n=Fe(e);return ur(n,1)})}};eh.className="GlobalMaxPooling1D";Q.registerClass(eh);var p0=class extends Me{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Tt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Se}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},th=class extends p0{call(e,t){return G(()=>{let n=Fe(e);return this.dataFormat==="channelsLast"?ht(n,[1,2]):ht(n,[2,3])})}};th.className="GlobalAveragePooling2D";Q.registerClass(th);var rh=class extends p0{call(e,t){return G(()=>{let n=Fe(e);return this.dataFormat==="channelsLast"?ur(n,[1,2]):ur(n,[2,3])})}};rh.className="GlobalMaxPooling2D";Q.registerClass(rh);var m0=class extends Me{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let o=t.layer,s=Qr(o,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},nh=class extends m0{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Je(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Je(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),o=e[1];return[n[0],o].concat(n.slice(1))}call(e,t){return G(()=>(e=Fe(e),s0((a,i)=>[Fe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};nh.className="TimeDistributed";Q.registerClass(nh);function RQ(r){Wi(rL,"BidirectionalMergeMode",r)}var FQ="concat",oh=class extends m0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Qr(n),t.goBackwards=t.goBackwards!==!0;let o={};if(o.className=e.layer.getClassName(),o.config=t,this.backwardLayer=Qr(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?FQ:e.mergeMode,RQ(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 t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,o,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):xr(o)}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=o0(e,n,o,this.numConstants);if(e=s.inputs,n=s.initialState,o=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&o==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(p=>new 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B0=class{constructor(e,t,n,o,s,a,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=le(0),Dt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
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because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),An(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Dt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,o)=>this.write(n,t[o]))}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 o=0;o<this.size();o++)e.push(o)}if(e.length===0)return Rr([],[0].concat(this.elementShape));let n=this.readMany(e);return An(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Vt(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Rr([],[0].concat(this.elementShape));let t=[];for(let o=0;o<this.size();o++)t.push(o);let n=this.readMany(t);return An(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),Ze(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,pr(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,o=e.map(l=>(n+=l,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let s=n===0?0:t.size/n,a=[];G(()=>{t=L(t,[1,n,s]);for(let l=0;l<e.length;++l){let u=l===0?0:o[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 gc=class{constructor(e,t,n,o=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(s=>{if(n!==s.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${s.dtype}`);An(t,s.shape,"TensorList shape mismatch: "),Dt(s)}),this.idTensor=le(0),this.maxNumElements=o,Dt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new gc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);An(e,this.elementShape,"TensorList shape mismatch: ");let o=Zp(this.elementShape,this.tensors,e);return G(()=>{let s=this.tensors.map(a=>L(a,o));return Vt(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 n=Zp(this.elementShape,this.tensors,e),o=this.tensors.pop();return An(o.shape,e,"TensorList shape mismatch: "),L(o,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(An(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Dt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);An(this.tensors[e].shape,t,"TensorList shape mismatch: ");let o=Zp(this.elementShape,this.tensors,t);return L(this.tensors[e],o)}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.`);An(this.elementShape,t.shape,"TensorList shape mismatch: "),Dt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);An(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let o=Zp(this.elementShape,this.tensors,n);return e.length===0?Rr([],[0].concat(o)):G(()=>{let s=e.map(a=>L(this.tensors[a],o));return Vt(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);An(this.elementShape,t,"TensorList shape mismatch: ");let n=Zp(this.elementShape,this.tensors,t);return this.size()===0?Rr([],[0].concat(n)):G(()=>{let o=this.tensors.map(s=>L(s,n));return Ze(o,0)})}};function dz(r,e,t){let n=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 o=r.shape.slice(1);An(o,e,"TensorList shape mismatch: ");let s=pr(r);return new gc(s,e,n)}function hz(r,e,t){return new gc([],r,e,t)}function gz(r,e,t,n){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let o=Math.max(...e);if(n!=null&&n!==-1&&o>=n)throw new Error(`Max index must be < array size (${o} vs. ${n})`);let s=new gc([],t,r.dtype,n),a=pr(r,0);return e.forEach((i,l)=>{s.setItem(i,a[l])}),s}function xz(r,e,t){let n=0,o=e.map(c=>(n+=c,n));if(n!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=ty(s,t),i=n===0?0:r.size/n,l=G(()=>{let c=[];r=L(r,[1,n,i]);for(let p=0;p<e.length;++p){let m=p===0?0:o[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 gc([],t,r.dtype,e.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var yz=async(r,e,t)=>{switch(r.op){case"If":case"StatelessIf":{let n=C("thenBranch",r,e,t),o=C("elseBranch",r,e,t),s=C("cond",r,e,t),a=C("args",r,e,t);return(await s.data())[0]?t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case"While":case"StatelessWhile":{let n=C("body",r,e,t),o=C("cond",r,e,t),s=C("args",r,e,t),a=await t.functionMap[o].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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n=C("elementShape",r,e,t),o=C("elementDType",r,e,t),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let a=C(s,r,e,t),i=hz(n,o,a);return t.addTensorList(i),[i.idTensor]}case"TensorListGather":{let n=C("tensorListId",r,e,t),o=C("indices",r,e,t),s=C("elementShape",r,e,t),a=C("elementDType",r,e,t);return[t.getTensorList(n.id).gather(o,a,s)]}case"TensorListStack":{let n=C("tensorListId",r,e,t),o=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=C("numElements",r,e,t);return[t.getTensorList(n.id).stack(o,s,a)]}case"TensorListFromTensor":{let n=C("tensor",r,e,t),o=C("elementShape",r,e,t),s=C("elementDType",r,e,t),a=dz(n,o,s);return t.addTensorList(a),[a.idTensor]}case"TensorListConcat":{let n=C("tensorListId",r,e,t),o=t.getTensorList(n.id),s=C("dtype",r,e,t),a=C("elementShape",r,e,t);return[o.concat(s,a)]}case"TensorListPushBack":{let n=C("tensorListId",r,e,t),o=C("tensor",r,e,t),s=t.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let 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n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t),a=C("includeBatchInIndex",r,e,t),{result:i,indexes:l}=zw(C("x",r,e,t),[s[1],s[2]],[n[1],n[2]],o,a);return[i,l]}case"AvgPool3D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Em(C("x",r,e,t),[s[1],s[2],s[3]],[n[1],n[2],n[3]],o)]}case"MaxPool3D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("kernelSize",r,e,t);return[Vm(C("x",r,e,t),[s[1],s[2],s[3]],[n[1],n[2],n[3]],o)]}case"Dilation2D":{let n=C("strides",r,e,t),o=C("pad",r,e,t),s=C("dilations",r,e,t),a=n[1],i=n[2],l=s[1],u=s[2];return[Fm(C("x",r,e,t),C("filter",r,e,t),[a,i],o,[l,u],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kz=(r,e,t)=>{switch(r.op){case"Fill":{let n=C("shape",r,e,t),o=C("dtype",r,e,t),s=C("value",r,e,t);return[va(n,s,o)]}case"LinSpace":{let n=C("start",r,e,t),o=C("stop",r,e,t),s=C("num",r,e,t);return[Rw(n,o,s)]}case"Multinomial":{let 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Tz=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[Ue(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[n,o]=C("fusedOps",r,e,t),s=n==="biasadd",a=o==="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[Go.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:o,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var Az=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Lo(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[Lo(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[Lm(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[Aa(C("x",r,e,t))];case"LogSoftmax":return[Au(C("x",r,e,t))];case"SparseToDense":return[tf(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 Ez=(r,e,t)=>{switch(r.op){case"Max":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ur(C("x",r,e,t),a,i)]}case"Mean":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[ht(C("x",r,e,t),a,i)]}case"Min":{let a=C("axis",r,e,t),i=C("keepDims",r,e,t);return[Pi(C("x",r,e,t),a,i)]}case"Sum":{let 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function U0(r,e,t,n){let o=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>en(m)[0]),c=[];n!=null&&(c=n.map(m=>en(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((j0(m)||Ste(m)||Tte(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>o.has(f))),o.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:o,missingInputs:s,dynamicNode:a,syncInputs:i}}function Fz(r,e,t){let{usedNodes:n,inputs:o}=t,s=[],a=Object.keys(o).map(c=>en(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{n.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)&&n.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var Ate=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Ete=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Dte=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function j0(r){return Ate.indexOf(r.op)>=0}function Ste(r){return Ete.indexOf(r.op)>=0}function Tte(r){return Dte.indexOf(r.op)>=0}var Jp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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You can use model.execute() instead.");let w=l.filter(b=>!j0(b)&&!yr(b.name,d,t)).map(b=>b.name);if(w.length>0){let b="";throw p!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${b}`)}return d}processStack(e,t,n,o,s,a,i,l,u){let c=[];for(;t.length>0;){let p=t.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,o,n)&&([m]=Gs(p.node.name,n)),o[p.node.name]==null){let f=W0(p.node,o,n,this._resourceManager);m||([m]=Gs(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,a,i,l),this.processChildNodes(p.node,t,n,o,s,u),h))):(o[m]=f,this.checkTensorForDisposal(m,p.node,o,n,a,i,l),this.processChildNodes(p.node,t,n,o,s,u))}else this.processChildNodes(p.node,t,n,o,s,u)}return c}processChildNodes(e,t,n,o,s,a){e.children.forEach(i=>{let[l]=Gs(i.name,n);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!yr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!yr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[o]=en(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let o=this._signature.inputs[n];t[o.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[o]=en(n);return this.graph.nodes[o]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=en(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}};var H0=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=Ir.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ir.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let o=Ir.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Jp(jx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=jx.Instance.transformGraph(e.modelInitializer);this.initializer=new Jp(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Ir.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ve)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,o)=>(t[n]=e[o],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Oz(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");e==null&&(e={}),e.fromTFHub&&r.load==null&&(r.endsWith("/")||(r=r+"/"),r=`${r}${Rte}${$te}`);let t=new ny(r,e);return await t.load(),t}var Pz="3.3.0";var py={};Ke(py,{CSVDataset:()=>ch,Dataset:()=>Ki,FileDataSource:()=>hh,TextLineDataset:()=>lh,URLDataSource:()=>gh,array:()=>b3,csv:()=>A3,func:()=>E3,generator:()=>D3,microphone:()=>R3,version_data:()=>F3,webcam:()=>$3,zip:()=>w3});var y3=Ec(Q0());var n3=Ec(Q0());function Jz(r,e){return sy(r,e)}function sy(r,e,t=new Map,n=new Set){if(r==null)return null;if(n.has(r))throw new Error("Circular references are not supported.");if(t.has(r))return t.get(r);let o=e(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(El(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let a in r){let i=r[a],l=sy(i,e,t,n);s[a]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return t.set(r,o.value),o.value}function e3(r,e=eI){return Qz(r,e)}function Qz(r,e,t=new Set){let n=r[0];if(t.has(n))throw new Error("Circular references are not supported.");let o=e(r);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(El(n)){let s=Array.isArray(n)?[]:{};t.add(n);for(let a in n){let i=r.map(u=>u[a]),l=Qz(i,e,t);s[a]=l}return t.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return o.value}function eI(r){return r===null?null:El(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function iy(r,e){let t=new Map;sy(r,e,t);for(let o of Array.from(t.keys())){let s=t.get(o);if(y.isPromise(s)){let a=await s;t.set(o,a)}}return sy(r,e,t)}function El(r){return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Ve))}function t3(r){return r==null||Bte(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Ve||y.isTypedArray(r)}function Bte(r){return r===null||typeof r!="object"&&typeof r!="function"}function r3(r){return Jz(r,Vte)}function Vte(r){return r instanceof Ve?{value:r.clone(),recurse:!1}:El(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var ih=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}};var Qp=class extends ih{constructor(){super(Qp.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let o=0;o<n;o++)t[o]=this.get(this.wrap(this.begin+o));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};Qp.INITIAL_CAPACITY=32;function tI(r){return new o3(r)}function ah(r){return new s3(r)}function i3(r,e){return new rI(r,e)}function l3(r,e=ja.FAIL){return new a3(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=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new h3(this,e)}filter(e){return new f3(this,e)}map(e){return new d3(this,e)}mapAsync(e){return new nI(this,e)}serialMapAsync(e){return new nI(this,e).serial()}flatmap(e){return new g3(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new m3(this,e,t)}columnMajorBatch(e,t=!0,n=eI){return this.rowMajorBatch(e,t).map(s=>e3(s,n))}concatenate(e,t){return new rI(tI([this,e]),t)}take(e){return e<0||e==null?this:new p3(this,e)}skip(e){return e<0||e==null?this:new c3(this,e)}prefetch(e){return new oI(this,e)}shuffle(e,t){return new x3(this,e,t)}serial(){return new u3(this)}},o3=class extends Kt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:r3(e),done:!1}}},s3=class extends Kt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},u3=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},c3=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`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},p3=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},m3=class extends Kt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},f3=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},d3=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Mo.getTensorsInContainer(e.value),n=this.transform(e.value),o=Mo.getTensorsInContainer(n);for(let s of t)Mo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},h3=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},nI=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Mo.getTensorsInContainer(e.value),n=await this.transform(e.value),o=Mo.getTensorsInContainer(n);for(let s of t)Mo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},em=class extends Kt{constructor(){super();this.outputQueue=new Qp,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},g3=class extends em{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Mo.getTensorsInContainer(e.value),n=this.transform(e.value),o=Mo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of t)Mo.isTensorInList(s,o)||s.dispose();return!0}},rI=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 n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ja;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(ja||(ja={}));var a3=class extends Kt{constructor(e,t=ja.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function o(a){return a instanceof Kt?{value:a.next().then(l=>(t++,l.done&&n++,l.value)),recurse:!1}:{value:null,recurse:!0}}let s=await iy(this.iterators,o);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ja.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ja.SHORTEST:return{value:null,done:!0};case ja.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},oI=class extends Kt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new ih(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},x3=class extends oI{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=n3.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var Ki=class{constructor(){this.size=null}batch(e,t=!0){let n=this;y.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let o;return this.size===Infinity||this.size==null?o=this.size:t?o=Math.ceil(this.size/e):o=Math.floor(this.size/e),gn(async()=>(await n.iterator()).columnMajorBatch(e,t,Gte),o)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,gn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,gn(async()=>(await t.iterator()).filter(o=>G(()=>e(o))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return gn(async()=>(await t.iterator()).map(n=>G(()=>e(n))),this.size)}mapAsync(e){let t=this;return gn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return gn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,gn(async()=>{let o=ah(async()=>({value:await t.iterator(),done:!1}));return i3(o.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,gn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=y3.alea(t||y.now().toString());return gn(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await o.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,gn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ki.MAX_BUFFER_SIZE=1e4;function gn(r,e=null){return new class extends Ki{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function b3(r){return gn(async()=>tI(r),r.length)}function w3(r){if(!El(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 gn(async()=>{let t=await iy(r,n=>{if(n instanceof Ki)return{value:n.iterator(),recurse:!1};if(El(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return l3(t,ja.SHORTEST)},e)}function Gte(r){if(r===null)return null;let e=r[0];return t3(e)?{value:Wte(r),recurse:!1}:{value:null,recurse:!0}}function Wte(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Vt(r):Rr(r)}var lh=class extends Ki{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
|
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`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var ay='"',uh=Symbol("out"),k3=Symbol("field"),ly=Symbol("quote"),sI=Symbol("quoteafterquote"),_3=Symbol("quoteinquote"),ch=class extends Ki{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 lh(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((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(t).filter(o=>t[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},o={};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?o[a]=u:n[a]=u}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],o=0,s=e.length,a=uh;for(let i=0;i<s;i++)switch(a){case uh:switch(e.charAt(i)){case ay:o=i+1,a=ly;break;case this.delimiter:if(o=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=uh;break;default:a=k3,o=i;break}break;case k3:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i)),a=uh,o=i+1;break;default:}break;case ly:switch(e.charAt(i)){case ay:a=sI;break;default:}break;case sI:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i-1)),a=uh,o=i+1;break;case ay:a=ly;break;default:a=_3;break}break;case _3:switch(e.charAt(i)){case ay:a=ly;break;default:}break;default:}if(a===sI?n.push(e.substring(o,s-1)):n.push(e.substring(o)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var ph=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(j().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new ph(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(y.sizeFromShape(t));return n.set(e,n.length-e.length),Rr(n,t)}};var mh=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=Gt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=Li([a,s,l,i],[1,4])}else this.cropBox=Li([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(j().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new mh(e,t);return await n.start(),n}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=Yh.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return G(()=>{let t=ar(oe(e,"float32"),0),n;n=$s.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return L(n,o.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 fh=class{};var uy=class extends Kt{split(e){return new v3(this,e)}},v3=class extends uy{constructor(e,t){super();this.upstream=e,this.impl=new C3(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},C3=class extends em{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}};var iI=class extends Kt{decodeUTF8(){return new N3(this)}},N3=class extends uy{constructor(e){super();this.upstream=e,this.impl=new S3(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},S3=class extends em{constructor(e){super();if(this.upstream=e,j().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=I3();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return j().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}};var dh=class extends iI{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(j().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>n(new Error("Aborted")),s.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,o);s.readAsArrayBuffer(a)}this.offset=o}),done:!1}}};async function T3(r,e={}){let t,n;typeof r=="string"?t=r:(t=r.url,n=jte(r));let o=await y.fetch(t,n);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new dh(s,e)}else throw new Error(o.statusText)}var jte=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 cy(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var hh=class extends fh{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(cy(this.input)&&j().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new dh(this.input,this.options)}};var gh=class extends fh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return cy(this.url)?new hh(this.url,this.fileOptions).iterator():T3(this.url,this.fileOptions)}};function A3(r,e={}){return new ch(new gh(r),e)}function E3(r){let e=ah(r);return gn(async()=>e)}function D3(r){return gn(async()=>{let e=await r();return ah(()=>e.next())})}async function $3(r,e){return mh.create(r,e)}async function R3(r){return ph.create(r)}var F3="3.3.0";var Ute={tfjs:bI,"tfjs-core":wI,"tfjs-data":kI,"tfjs-layers":_I,"tfjs-converter":vI,"tfjs-backend-cpu":bk,"tfjs-backend-webgl":O_,"tfjs-backend-wasm":xC};export{as as Abs,qs as Acos,Ks as Acosh,np as AdadeltaOptimizer,op as AdagradOptimizer,sp as AdamOptimizer,ip as AdamaxOptimizer,wn as Add,Hn as AddN,Ml as All,Ll as Any,qn as ArgMax,ea as ArgMin,Xs as Asin,Ys as Asinh,Zs as Atan,Qs as Atan2,Js as Atanh,Kn as AvgPool,ta as AvgPool3D,Bl as AvgPool3DGrad,zl as AvgPoolGrad,sx as BackendWasm,Xn as BatchMatMul,ra as BatchToSpaceND,Vl as Bincount,Ob as BroadcastTo,zx as Callback,kx as CallbackList,$n as Cast,Yn as Ceil,Rn as ClipByValue,Gl as Complex,na as ComplexAbs,ls as Concat,Zn as Conv2D,Wl as Conv2DBackpropFilter,Jn as Conv2DBackpropInput,oa as Conv3D,jl as Conv3DBackpropFilterV2,Ul as Conv3DBackpropInputV2,Qn as Cos,ei as Cosh,ti as CropAndResize,eo as Cumsum,vx as CustomCallback,Za as DataStorage,Hl as DenseBincount,ri as DepthToSpace,to as DepthwiseConv2dNative,ql as DepthwiseConv2dNativeBackpropFilter,Kl as DepthwiseConv2dNativeBackpropInput,Xl as Diag,sa as Dilation2D,Pc as Dilation2DBackpropFilter,Oc as Dilation2DBackpropInput,$b as ENV,Vx as EarlyStopping,ni as Elu,Yl as EluGrad,Bh as Environment,si as Equal,oi as Erf,no as Exp,us as ExpandDims,ii as Expm1,Zl as FFT,ia as Fill,ai as FlipLeftRight,oo as Floor,so as FloorDiv,Mc as FromPixels,io as FusedBatchNorm,ks as FusedConv2D,_s as FusedDepthwiseConv2D,zg as GPGPUContext,li as GatherNd,cs as GatherV2,ny as GraphModel,ui as Greater,ao as GreaterEqual,_x as History,Jl as IFFT,Fn as Identity,Ql as Imag,Tt as InputSpec,ci as IsFinite,pi as IsInf,mi as IsNan,js as KernelBackend,aa as LRN,tu as LRNGrad,Yf as LayerVariable,Tn as LayersModel,lo as LeakyRelu,fi as Less,di as LessEqual,eu as LinSpace,uo as Log,hi as Log1p,Pb as LogSoftmax,gi as LogicalAnd,Ja as LogicalNot,Qa as LogicalOr,Hu as MathBackendCPU,Ju as MathBackendWebGL,co as Max,mo as MaxPool,la as MaxPool3D,nu as MaxPool3DGrad,ru as MaxPoolGrad,ou as MaxPoolWithArgmax,po as Maximum,fo as Mean,ho as Min,go as Minimum,ua as MirrorPad,xi as Mod,ap as MomentumOptimizer,su as Multinomial,xo as Multiply,ps as Neg,bi as NonMaxSuppressionV3,wi as NonMaxSuppressionV4,ki as NonMaxSuppressionV5,yi as NotEqual,GI as OP_SCOPE_SUFFIX,yo as OneHot,ms as OnesLike,Pr as Optimizer,fs as Pack,bo as PadV2,tV as Pool,wo as Pow,ko as Prelu,_i as Prod,lp as RMSPropOptimizer,hn as RNN,ca as Range,Vb as Rank,iu as Real,ro as RealDiv,vi as Reciprocal,Wt as Reduction,_o as Relu,Co as Relu6,ds as Reshape,vo as ResizeBilinear,lu as ResizeBilinearGrad,pa as ResizeNearestNeighbor,au as ResizeNearestNeighborGrad,Io as Reverse,$i as RotateWithOffset,No as Round,So as Rsqrt,ul as SGDOptimizer,Ci as ScatterNd,hs as Select,Ii as Selu,qi as Sequential,Ao as Sigmoid,Si as Sign,To as Sin,Ni as Sinh,gs as Slice,$o as Softmax,Ti as Softplus,ma as SpaceToBatchND,uu as SparseToDense,xs as SplitV,Eo as Sqrt,fa as Square,Ro as SquaredDifference,On as Step,Ai as StridedSlice,Fo as Sub,Do as Sum,Vr as SymbolicTensor,Ei as Tan,Oo as Tanh,Ve as Tensor,lt as TensorBuffer,kn as Tile,Di as TopK,cu as Transform,Po as Transpose,pu as Unique,ys as Unpack,da as UnsortedSegmentSum,rl as Variable,bs as ZerosLike,ws as _FusedMatMul,Nt as abs,_m as acos,vm as acosh,ee as add,gw as addN,bu as all,sl as any,il as argMax,Cm as argMin,Im as asin,Nm as asinh,Sm as atan,Tm as atan2,Am as atanh,wa as avgPool,Em as avgPool3d,hw as backend,S as backend_util,oW as basicLSTMCell,Lo as batchNorm,ww as batchNorm2d,kw as batchNorm3d,_w as batchNorm4d,ka as batchToSpaceND,vw as bincount,mU as booleanMaskAsync,al as broadcastTo,Yh as browser,ve as buffer,nz as callbacks,oe as cast,Dm as ceil,ir as clipByValue,Pn as clone,_n as complex,Ze as concat,Cw as concat1d,Iw as concat2d,Nw as concat3d,Sw as concat4d,NC as constraints,_u as conv1d,Kr as conv2d,vu as conv2dTranspose,$m as conv3d,IW as conv3dTranspose,oV as copyRegisteredKernels,_a as cos,Cu as cosh,rf as cosineWindow,Iu as cumsum,Xr as customGrad,py as data,Tw as denseBincount,rg as deprecationWarn,Rm as depthToSpace,Is as depthwiseConv2d,iz as deregisterOp,hu as device_util,RW as diag,Fm as dilation2d,xG as disableDeprecationWarnings,Ae as dispose,yG as disposeVariables,me as div,Om as divNoNan,Aw as dot,tk as dropout,Ns as elu,gG as enableDebugMode,hG as enableProdMode,rk as enclosingPowerOfTwo,Mn as engine,j as env,vn as equal,Pm as erf,Zt as exp,ar as expandDims,Mm as expm1,Yc as eye,Ea as fft,va as fill,CG as findBackend,IG as findBackendFactory,Ss as floor,yu as floorDiv,P_ as forceHalfFloat,Go as fused,zo as gather,ek as gatherND,Zh as gather_util,_G as getBackend,Vh as getGradient,zc as getKernel,gm as getKernelsForBackend,fE as gpgpu_util,lj as grad,uj as grads,er as greater,an as greaterEqual,Mi as ifft,Nu as imag,$s as image,kU as inTopKAsync,DC as initializers,Mx as input,Ir as io,Lu as irfft,Ew as isFinite,Dw as isInf,$w as isNaN,Dt as keep,Ar as kernel_impls,f0 as layers,Ca as leakyRelu,Su as less,Bn as lessEqual,ak as linalg,Rw as linspace,Oz as loadGraphModel,jL as loadLayersModel,Lm as localResponseNormalization,lr as log,Tu as log1p,Fw as logSigmoid,Au as logSoftmax,Bm as logSumExp,hr as logicalAnd,Ia as logicalNot,Eu as logicalOr,Lw as logicalXor,dH as losses,We as matMul,bN as math,ur as max,Na as maxPool,Vm as maxPool3d,zw as maxPoolWithArgmax,Yr as maximum,ht as mean,qc as memory,x0 as metrics,Pi as min,As as minimum,Gm as mirrorPad,Wm as mod,GL as model,y0 as models,Zc as moments,hU as movingAverage,P as mul,Lj as multiRNNCell,Bw as multinomial,He as neg,nf as nextFrame,Vu as norm,Vo as notEqual,Cs as oneHot,Nr as ones,tr as onesLike,T as op,Wj as outerProduct,Fr as pad,Hj as pad1d,Kj as pad2d,Yj as pad3d,Jj as pad4d,Vw as pool,Or as pow,Ta as prelu,sw as print,Du as prod,bG as profile,a4 as rand,h4 as randomGamma,cg as randomNormal,Es as randomUniform,Qc as range,kG as ready,ll as real,jm as reciprocal,xu as registerBackend,UL as registerCallbackConstructor,Lb as registerGradient,el as registerKernel,sz as registerOp,b0 as regularizers,Sr as relu,Ru as relu6,vG as removeBackend,L as reshape,qt as reverse,C4 as reverse1d,N4 as reverse2d,T4 as reverse3d,E4 as reverse4d,Da as rfft,Um as round,Fu as rsqrt,le as scalar,Qw as scatterND,Jh as scatter_util,Ou as selu,Hm as separableConv2d,WL as sequential,Q as serialization,PN as setBackend,NG as setPlatform,ZZ as setWasmPath,JZ as setWasmPaths,Bk as setWebGLContext,Yw as setdiff1dAsync,wg as shared,qr as sigmoid,qm as sign,fH as signal,Pu as sin,Mu as sinh,Re as slice,Km as slice1d,pg as slice2d,Xm as slice3d,ep as slice4d,sr as slice_util,Aa as softmax,Ts as softplus,Sa as spaceToBatchND,tf as sparseToDense,mH as spectral,cr as split,xt as sqrt,Oe as square,zu as squaredDifference,Cn as squeeze,Vt as stack,Ds as step,Ym as stridedSlice,ce as sub,ge as sum,fu as sumOutType,Zm as tan,Oi as tanh,Rr as tensor,Gt as tensor1d,Li as tensor2d,uw as tensor3d,rU as tensor4d,nU as tensor5d,oU as tensor6d,Mo as tensor_util,RN as test_util,G as tidy,zn as tile,wG as time,Jm as topk,cl as train,Ue as transpose,Bu as truncatedNormal,tp as unique,nV as unregisterGradient,rV as unregisterKernel,Qm as unsortedSegmentSum,pr as unstack,dr as upcastType,y as util,cj as valueAndGrad,pj as valueAndGrads,Zw as variable,ig as variableGrads,Ute as version,Pz as version_converter,dG as version_core,bk as version_cpu,Wp as version_layers,xC as version_wasm,O_ as version_webgl,r8 as webgl,lE as webgl_util,$t as where,ef as whereAsync,gt 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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*
|
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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
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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.
|
|
* 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 2021 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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/** @license See the LICENSE file. */
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//# sourceMappingURL=tfjs.esm.js.map
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