mirror of https://github.com/vladmandic/human
5648 lines
1.4 MiB
5648 lines
1.4 MiB
/*
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Human
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=hr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Nh(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await hr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(hr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Yl.print(this,e)}clone(){return this.throwIfDisposed(),Yl.clone(this)}toString(e=!1){let t=this.dataSync();return iN(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Yl.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),hr().makeVariable(this,e,t,n)}};Object.defineProperty(je,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function ae(){return P1("Tensor",()=>je)}ae();var zd=class extends je{constructor(e,t,n,a){super(e.shape,e.dtype,e.dataId,a);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Dr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);hr().disposeTensor(this),this.dataId=e.dataId,hr().incRef(this,null)}dispose(){hr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(zd,Symbol.hasInstance,{value:e=>e instanceof je&&e.assign!=null&&e.assign instanceof Function});var Xa={};ze(Xa,{assertTypesMatch:()=>ub,getTensorsInContainer:()=>q1,isTensorInList:()=>hN,makeTypesMatch:()=>Ft});var V1;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(V1||(V1={}));var U1;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(U1||(U1={}));var j1;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(j1||(j1={}));var G1;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(G1||(G1={}));var H1;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(H1||(H1={}));var cN={float32:G1,int32:U1,bool:j1,complex64:H1};function Pa(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return cN[e][t]}function Eh(e){return Pa(e,"int32")}function Ft(e,t){if(e.dtype===t.dtype)return[e,t];let n=Pa(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function ub(e,t){$(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function hN(e,t){return t.some(n=>n.id===e.id)}function q1(e){let t=[],n=new Set;return db(e,t,n),t}function db(e,t,n){if(e==null)return;if(e instanceof je){t.push(e);return}if(!fN(e))return;let a=e;for(let r in a){let s=a[r];n.has(s)||(n.add(s),db(s,t,n))}}function fN(e){return Array.isArray(e)||typeof e=="object"}function X1(e){return e.kernelName!=null}var pb=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Ld=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new pb}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(pr(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new tN(this.backendInstance),!0}setupRegisteredKernels(){$r(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){$r(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof fd)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,pr(`Initialization of backend ${e} failed`),pr(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return pr(`Initialization of backend ${e} failed`),pr(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Ld.nextTensorId++}nextVariableId(){return Ld.nextVariableId++}clone(e){let t=W.runKernel(mi,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return W.runKernel(ei,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,Ih(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=X1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(X1(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Ih(h,this.backendName);$(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:k,dtype:N}=b;return this.makeTensorFromDataId(v,k,N)});if(a){let b=this.getTensorsForGradient(h,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:h}=e,f=m=>{!a||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:d,attrs:u}=e,p=X1(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(l,d,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(l,d,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(d).map(h=>d[h]!=null?d[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=z1(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?($(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,d)=>s[d]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&is(e[0])&&(r=e.map(o=>$d(o)));let s=a.write(r,t,n),i=new je(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=q5(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new je(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new zd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*F1(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 zd||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*F1(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=z1(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=l=>(l=l.map((d,u)=>{if(d==null){let p=n[u],c=Lc(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return d}),a(l.length>1?l:l[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=q1(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if($(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));$(r instanceof je,()=>"The result y returned by f() must be a tensor.");let s=rN(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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Actual: ${r}.
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Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=r[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
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Actual: ${r}.
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${r} and ${t} for depthToSpace with input shape
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${a.shape}`),$(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${s} and ${t} for depthToSpace with input shape
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${a.shape}`),$(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},l={blockSize:t,dataFormat:n};return W.runKernel(ll,o,l)}var Fg=B({depthToSpace_:ZE});function YE(e,t,n,a,r="NHWC",s=[1,1],i){let o=F(e,"x","depthwiseConv2d","float32"),l=F(t,"filter","depthwiseConv2d","float32"),d=o,u=!1;o.rank===3&&(u=!0,d=V(o,[1,o.shape[0],o.shape[1],o.shape[2]])),$(d.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`),$(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),$(d.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&$(un(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:d,filter:l},c={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=W.runKernel(oi,p,c);return u?V(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var ou=B({depthwiseConv2d_:YE});function JE(e){let t={x:F(e,"x","diag")};return W.runKernel(Yc,t)}var QE=B({diag_:JE});function eR(e,t,n,a,r=[1,1],s="NHWC"){let i=F(e,"x","dilation2d"),o=F(t,"filter","dilation2d");$(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),$(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),$(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,d=!1;i.rank===3&&(l=V(i,[1,i.shape[0],i.shape[1],i.shape[2]]),d=!0);let u={x:l,filter:o},p={strides:n,pad:a,dilations:r},c=W.runKernel(vd,u,p);return d?V(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Dg=B({dilation2d_:eR});function tR(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let 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s=F(e,"labels","logLoss"),i=F(t,"predictions","logLoss"),o=null;n!=null&&(o=F(n,"weights","logLoss")),Nn(s.shape,i.shape,"Error in logLoss: ");let l=Ce(1),d=Ce(a),u=Nt(L(s,la(le(i,d)))),p=L(Ae(l,s),la(le(Ae(l,i),d))),c=Ae(u,p);return zr(c,o,r)}var JD=B({logLoss_:YD});function QD(e,t,n,a=Fn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),s=F(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=F(n,"weights","meanSquaredError")),Nn(r.shape,s.shape,"Error in meanSquaredError: ");let o=r0(r,s);return zr(o,i,a)}var e$=B({meanSquaredError_:QD});function t$(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),a=F(t,"logits","sigmoidCrossEntropyWithLogits");Nn(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Ja(a),s=L(a,n),i=Zd(oa(Nt(jt(a))));return le(Ae(r,s),i)}function n$(e,t,n,a=0,r=Fn.SUM_BY_NONZERO_WEIGHTS){let s=F(e,"multiClassLabels","sigmoidCrossEntropy"),i=F(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=F(n,"weights","sigmoidCrossEntropy")),Nn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let d=Ce(a),u=Ce(1),p=Ce(.5);s=le(L(s,Ae(u,d)),L(p,d))}let l=t$(s,i);return zr(l,o,r)}var a$=B({sigmoidCrossEntropy_:n$});function r$(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let l={indices:r,values:s,denseShape:i,defaultValue:o},d=W.runKernel(fh,l);return{outputIndices:d[0],outputValues:d[1],emptyRowIndicator:d[2],reverseIndexMap:d[3]}}var l$=B({sparseFillEmptyRows_:o$});function u$(e,t,n){let a=F(e,"inputIndices","sparseReshape"),r=F(t,"inputShape","sparseReshape"),s=F(n,"newShape","sparseReshape");if(a.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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|
${a.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=W.runKernel(mh,i);return{outputIndices:o[0],outputShape:o[1]}}var d$=B({sparseReshape_:u$});function p$(e,t,n){let a=F(e,"data","sparseSegmentMean"),r=F(t,"indices","sparseSegmentMean"),s=F(n,"segmentIds","sparseSegmentMean");if(a.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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|
${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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|
${s.shape}`);let i={data:a,indices:r,segmentIds:s};return W.runKernel(gh,i)}var c$=B({sparseSegmentMean_:p$});function h$(e,t,n){let a=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum"),s=F(n,"segmentIds","sparseSegmentSum");if(a.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${s.shape}`);let i={data:a,indices:r,segmentIds:s};return W.runKernel(yh,i)}var f$=B({sparseSegmentSum_:h$});function m$(e,t,n,a,r,s,i,o){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let d=F(t,"dataSplits","stringNGrams");if(d.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let u={separator:n,nGramWidths:a,leftPad:r,rightPad:s,padWidth:i,preserveShortSequences:o},p={data:l,dataSplits:d},c=W.runKernel(xh,p,u);return{nGrams:c[0],nGramsSplits:c[1]}}var g$=B({stringNGrams_:m$});function y$(e,t,n=!0){let a=F(e,"input","stringSplit","string"),r=F(t,"delimiter","stringSplit","string");if(a.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${a.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let 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b$=B({stringToHashBucketFast_:x$}),v$={fft:rp,ifft:mu,rfft:sp,irfft:a0},w$={hammingWindow:JF,hannWindow:W3,frame:B3,stft:nD},Me={flipLeftRight:iD,grayscaleToRGB:lD,resizeNearestNeighbor:q3,resizeBilinear:H3,rotateWithOffset:dD,cropAndResize:rD,nonMaxSuppression:cD,nonMaxSuppressionAsync:bD,nonMaxSuppressionWithScore:wD,nonMaxSuppressionWithScoreAsync:ID,nonMaxSuppressionPadded:SD,nonMaxSuppressionPaddedAsync:CD,threshold:DD,transform:OD},K3={bandPart:PD,gramSchmidt:LD,qr:BD},k$={absoluteDifference:jD,computeWeightedLoss:zr,cosineDistance:HD,hingeLoss:XD,huberLoss:ZD,logLoss:JD,meanSquaredError:e$,sigmoidCrossEntropy:a$,softmaxCrossEntropy:i$},ip={sparseFillEmptyRows:l$,sparseReshape:d$,sparseSegmentMean:c$,sparseSegmentSum:f$},c0={stringNGrams:g$,stringSplit:A$,stringToHashBucketFast:b$},Lr=class extends Zb{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else 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Lr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=W.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:G(()=>Ye(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:G(()=>Ye(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;G(()=>{let l=le(L(i,this.rho),L(ht(s),1-this.rho)),d=L(fe(An(le(o,this.epsilon)),An(le(i,this.epsilon))),s),u=le(L(o,this.rho),L(ht(d),1-this.rho));i.assign(l),o.assign(u);let p=le(L(d,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Q(this.accumulatedGrads.map(e=>e.variable)),Q(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};h0.className="Adadelta";ys(h0);var f0=class extends Lr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=W.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:G(()=>uu(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;G(()=>{let i=le(s,ht(r));s.assign(i);let o=le(L(fe(r,An(le(i,W.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Q(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};f0.className="Adagrad";ys(f0);var m0=class extends Lr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],G(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(n).variable()}),a==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);G(()=>{let n=Ae(1,this.accBeta1),a=Ae(1,this.accBeta2);t.forEach((r,s)=>{let i=W.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:G(()=>Ye(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:G(()=>Ye(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let d=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,p=le(L(d,this.beta1),L(l,1-this.beta1)),c=le(L(u,this.beta2),L(ht(l),1-this.beta2)),h=fe(p,n),f=fe(c,a);d.assign(p),u.assign(c);let m=le(L(fe(h,le(An(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Q(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Q(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(Pr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Pr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};m0.className="Adam";ys(m0);var g0=class extends Lr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],G(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),a==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);G(()=>{let n=Ae(1,this.accBeta1),a=fe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=W.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ye(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ye(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let d=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,p=le(L(d,this.beta1),L(l,1-this.beta1)),c=L(u,this.beta2),h=jt(l),f=Ar(c,h);d.assign(p),u.assign(f);let m=le(L(fe(a,n),fe(p,le(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Q(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Q(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)}};g0.className="Adamax";ys(g0);var op=class extends Lr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=W.registeredVariables[t];G(()=>{let s=le(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=pn(Ce(-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)}};op.className="SGD";ys(op);var y0=class extends op{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ce(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=W.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:G(()=>Ye(a).variable(i))}}let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&G(()=>{let i,o=le(L(this.m,r),s);this.useNesterov?i=le(L(this.c,le(s,L(o,this.m))),a):i=le(L(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Q(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)}};y0.className="Momentum";ys(y0);var A0=class extends Lr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=W.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:G(()=>Ye(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:G(()=>Ye(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:G(()=>Ye(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;G(()=>{let l=le(L(i,this.decay),L(ht(s),1-this.decay));if(this.centered){let d=this.accumulatedMeanGrads[n].variable,u=le(L(d,this.decay),L(s,1-this.decay)),p=fe(L(s,this.learningRate),An(Ae(l,le(ht(u),this.epsilon)))),c=le(L(o,this.momentum),p);i.assign(l),d.assign(u),o.assign(c);let h=Ae(a,c);a.assign(h)}else{let d=le(L(i,this.decay),L(ht(s),1-this.decay)),u=le(L(o,this.momentum),fe(L(s,this.learningRate),An(le(d,this.epsilon))));i.assign(d),o.assign(u);let p=Ae(a,u);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Q(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Q(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Q(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};A0.className="RMSProp";ys(A0);var uo=class{static sgd(e){return new op(e)}static momentum(e,t,n=!1){return new y0(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new A0(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new m0(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new h0(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new g0(e,t,n,a,r)}static 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);ws(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;br(x===0,"input layer has >1 nodes"),br(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof bu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,A,x,b,v,k)=>{(b==null||v==null||k==null)&&(b=y.sourceLayer,v=y.nodeIndex,k=y.tensorIndex);let N=b.inboundNodes[v];if(x.indexOf(N)!==-1)throw new tr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(N)!==-1)return;this.containerNodes.add(wr.nodeKey(b,v)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(N)===-1&&x.push(N);let C=N.inboundLayers.length;for(let E=0;E<C;E++){let O=N.inputTensors[E],D=N.inboundLayers[E],T=N.nodeIndices[E],P=N.tensorIndices[E];o(O,A,x,D,T,P)}for(A.push(N);x.indexOf(N)>=0;)x.splice(x.indexOf(N),1);i.push(N)},l=[],d=[];for(let y of this.outputs)o(y,l,d);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];A=Math.max(A,x),a[y.outboundLayer.id]=A,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],k=y.nodeIndices[b],N=v.inboundNodes[k],C=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(A+1,C),n[N.id]=N}}let p={};for(let y in t){let A=t[y];A in p||(p[A]=[]),p[A].push(n[y])}let c={};for(let y in a){let A=a[y];A in c||(c[A]=[]),c[A].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(x0);this.layers=[];for(let y of h){let A=c[y];A.sort((x,b)=>{let v=s[x.id],k=s[b.id];return v<k?-1:v>k?1:0});for(let x of A)x instanceof wr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(y=>parseInt(y,10)).sort(x0);let f=this.inputs.slice(),m=[];for(let y of h)for(let A of p[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new tr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new tr(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new $0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new H("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new H(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new H(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new H(`${s.length} of ${a} weights are not set: ${s}`)}My(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Ly}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=zy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return G(()=>{e=vt(e);let n=new Ao;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return gp(this.outputs,n,t)})}computeMask(e,t){return G(()=>{e=vt(e);let n;return t==null?n=co(null,e.length):n=vt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=F0(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],d=o.name+"_0_0";n[d]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(x0);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let d=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(d.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=n[A];u.push(x)}let p=d.computeOutputShape(Jn(u)),c=F0(p),h=d.inboundNodes.indexOf(l);for(let f=0;f<c.length;f++){let m=`${d.name}_${h}_${f}`;n[m]=c[f]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],d=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${d}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];br(o in n),r.push(n[o])}return Jn(r)}runInternalGraph(e,t){t==null&&(t=co(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],d=e[o],u=t[o];n[l.id]=[d,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(x0);for(let o of a){let l=this.nodesByDepth[o];for(let d of l){let u=d.outboundLayer,p=d.inputTensors,c=d.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,y,A;if(d.callArgs!=null&&(f=d.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),y=vt(u.call(x,f)),A=vt(u.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),y=vt(u.call(m,f)),A=vt(u.computeMask(m,g));if(u.activityRegularizer)throw new Pe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let b=c[x],v=y[x],k=A[x];n[b.id]=[v,k]}}}}let r=[],s=[],i=[];for(let o of this.outputs){br(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,d]=n[o.id];i.push(l.shape),r.push(l),s.push(d)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof wr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=wr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return G(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=wr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],c=wr.nodeKey(s,u),h={};if(this.containerNodes.has(c)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],y=p.nodeIndices[m],A=p.tensorIndices[m],x=wr.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,h])}l.push(f)}}}let d={};d.name=s.name,d.className=i,d.config=o,d.inboundNodes=l,n.push(d)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=wr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let d=t[l];d==null&&(d=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,d,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=wr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let d=t[l];d==null&&(d=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,d,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],k=x[2];if(A=x[3]==null?{}:x[3],!(b in r)){i(m,g);return}let N=r[b];if(N.inboundNodes.length<=v){i(m,g);return}let C=N.inboundNodes[v];y.push(C.outputTensors[k])}y.length>0&&m.apply(Jn(y),A)}function l(m){let g=m.name,y=sr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let d=t.name,u=t.layers;for(let m of u)l(m);for(;!uP(s);)for(let m of u){let g=r[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let p=[],c=[],h=t.inputLayers;for(let m of h){let g=m[0],y=m[1],A=m[2];br(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];br(g in r);let x=r[g].inboundNodes[y].outputTensors;c.push(x[A])}return new e({inputs:p,outputs:c,name:d})}get stateful(){if(this._stateful)throw new H("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 Dz(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Yv(e,t){return Dz(e,t,"classWeight")}async function Jv(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=G(()=>{if(e.shape.length===1)return Za(e);if(e.shape.length===2){if(e.shape[1]>1)return ka(e,1);if(e.shape[1]===1)return V(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());Q(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Ht(i,"float32")}else return null}function $z(e,t){return L(e,t)}var Oz=32;function Qv(e,t){let n,a,r=t;n=r.xs,a=r.ys,w.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=e7("input",e.inputNames,n),i=e7("output",e.outputNames,a),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function e7(e,t,n){if(n instanceof je)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function _z(e){if(e.length===3)throw new Pe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Pz(e,t,n){let a=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(t7(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=_z(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),d;r?d=l.slice().concat(l.map(g=>"val_"+g)):d=l.slice();let u=Lv(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=Wv(u,p,n.epochs,null,null,zz(t,n),null,r,d);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await c.onEpochBegin(f);let y=0,A=0;for(a||(m=await t.iterator());a?y<n.batchesPerEpoch:!0;){let x=await m.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let d=[];for(let h=0;h<this.inputs.length;++h)d.push({key:this.inputs[h],value:n[h]});let u=new Ao(d),p=gp(this.outputs,u,{training:!0}),c;for(let h=0;h<this.lossFunctions.length;++h){let f=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(f=$z(f,r[h]));let m=Dt(f);t.push(m),h===0?c=f:c=le(c,f)}for(let h=0;h<this.metricsTensors.length;++h){let f;if(this.outputs.length>1&&h<this.outputs.length)f=t[h];else{let m=this.metricsTensors[h][0],g=this.metricsTensors[h][1];f=Dt(m(a[g],p[g]))}pn(f),s.push(f)}return c=Dt(c),this.calculateLosses().forEach(h=>{c=le(c,h)}),c},o=this.collectedTrainableWeights.map(d=>d.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>G(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new Ao(s),o=gp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let d=this.lossFunctions[l],u=Dt(d(r[l],o[l]));l===0?n=u:n=le(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let d=this.metricsTensors[l][0],u=this.metricsTensors[l][1],p=Dt(d(r[u],o[u]));t.push(p)}return t})}async fit(e,t,n={}){return Vz(this,e,t,n)}async fitDataset(e,t){return Pz(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Q(s),Jn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Dh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Dh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Br(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=>Br(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=Br(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Br(B0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Br(B0(e)));{let e={};for(let t in this.metrics)e[t]=Br(B0(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=mp(e.optimizer_config),n=sr(t),a;if(typeof e.loss=="string")a=ho(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>ho(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=ho(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>ho(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=ho(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Xn.getSaveHandlers(e);if(i.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new H(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Xn.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:qz,generatedBy:`TensorFlow.js tfjs-layers v${Ly}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Xn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Xn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;qv(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){qv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Vr.className="Model";ue.registerClass(Vr);var i7=class extends Vr{};i7.className="Functional";ue.registerClass(i7);async function Xz(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=mp(n),r=sr(a,t);if(e.weightsManifest!=null){let s=await Xn.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Q(s)}return r}async function Kz(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Xn.getLoadHandlers(e,t);if(n.length===0)n.push(Xn.browserHTTPRequest(e,t));else if(n.length>1)throw new H(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Zz(e,void 0,t)}async function Zz(e,t,n){if(n==null&&(n={}),e.load==null)throw new H("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=sr(mp(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new H("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:d,optimizerWeights:u}=Yz(a.weightData,a.weightSpecs);o.loadWeights(d,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Q(d),Q(u.map(p=>p.tensor))}return o}function Yz(e,t){let n=Xn.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var ku=class extends Vr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:M0("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new H(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof ku||e instanceof Vr,n;if(t){if(n=e,n.outputs.length!==1)throw new H("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new Vr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new tr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new tr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new tr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new tr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new H("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof ku))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=sr(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new H("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 H("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}}};ku.className="Sequential";ue.registerClass(ku);function Jz(e){return new Vr(e)}function Qz(e){return new ku(e)}function eL(e,t){return t==null&&(t={}),Kz(e,t)}function o7(e){return Dv(e)}function tL(e,t){Va.registerCallbackConstructor(e,t)}var ea=class extends ue.Serializable{getConfig(){return{}}},l7=class extends ea{apply(e,t=1){return NP(e,t)}};l7.className="elu";ue.registerClass(l7);var u7=class extends ea{apply(e){return Qh(e)}};u7.className="selu";ue.registerClass(u7);var d7=class extends ea{apply(e){return Ja(e)}};d7.className="relu";ue.registerClass(d7);var p7=class extends ea{apply(e){return G(()=>pu(6,Ja(e)))}};p7.className="relu6";ue.registerClass(p7);var c7=class extends ea{apply(e){return e}};c7.className="linear";ue.registerClass(c7);var h7=class extends ea{apply(e){return Kn(e)}};h7.className="sigmoid";ue.registerClass(h7);var f7=class extends ea{apply(e){return EP(e)}};f7.className="hardSigmoid";ue.registerClass(f7);var m7=class extends ea{apply(e){return so(e)}};m7.className="softplus";ue.registerClass(m7);var g7=class extends ea{apply(e){return CP(e)}};g7.className="softsign";ue.registerClass(g7);var y7=class extends ea{apply(e){return no(e)}};y7.className="tanh";ue.registerClass(y7);var Gy=class extends ea{apply(e,t=-1){return lo(e,t)}};Gy.className="softmax";ue.registerClass(Gy);var A7=class extends ea{apply(e,t=-1){return Hh(e,t)}};A7.className="logSoftmax";ue.registerClass(A7);var x7=class extends ea{apply(e,t=1){return G(()=>L(Kn(L(e,t)),e))}};x7.className="swish";ue.registerClass(x7);var b7=class extends ea{apply(e){return G(()=>L(e,no(so(e))))}};b7.className="mish";ue.registerClass(b7);function Ss(e){return e.getClassName()}function Hy(e,t={}){return lp(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Ns(e){if(e==null){let t={};return t.className="linear",t.config={},Hy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Hy(t)}else return e instanceof ea?e:Hy(e)}function qy(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var v7=class extends ue.Serializable{},Ap=class extends v7{constructor(e){super();qy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return G(()=>{let t=Gt([1]);return this.hasL1&&(t=le(t,Ie(L(this.l1,jt(e))))),this.hasL2&&(t=le(t,Ie(L(this.l2,cp(e))))),V(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ap.className="L1L2";ue.registerClass(Ap);function nL(e){return qy(e),new Ap({l1:e!=null?e.l1:null,l2:0})}function aL(e){return qy(e),new Ap({l2:e!=null?e.l2:null,l1:0})}var w7={l1l2:"L1L2"};function gt(e){return dy(e)}function k7(e,t={}){return lp(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Et(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in w7?w7[e]:e,config:{}};return k7(t)}else return e instanceof v7?e:k7(e)}var Xy=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Le(e);let n=Ja(e);return this.maxValue!=null&&(n=Zn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="ReLU";ue.registerClass(Xy);var Ky=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Le(e);return Kd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="LeakyReLU";ue.registerClass(Ky);var Zy=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ct(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Et(e.alphaRegularizer),this.alphaConstraint=nn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new H(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=pt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new qt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Le(e),tp(e,this.alpha.read())}getConfig(){let e={alphaInitializer:$t(this.alphaInitializer),alphaRegularizer:gt(this.alphaRegularizer),alphaConstraint:tn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Zy.className="PReLU";ue.registerClass(Zy);var Yy=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Pe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Le(e);return lu(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Yy.className="ELU";ue.registerClass(Yy);var Jy=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Le(e);return L(n,pe(Yn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="ThresholdedReLU";ue.registerClass(Jy);var Qy=class extends Qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Le(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}};Qy.className="Softmax";ue.registerClass(Qy);function Iu(e,t,n){if(typeof e=="number")return co(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!kP(r))throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function ir(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function kr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+Is([n-t,0]);else if(a==="same")e=e*t;else throw new H(`Unsupport padding mode: ${a}.`);return e}function eA(e,t){return G(()=>(Bt(t),t==="channelsFirst"?Ze(e,[0,2,3,1]):e))}function I7(e,t){return G(()=>(Bt(t),t==="channelsFirst"?Ze(e,[0,2,3,4,1]):e))}function rL(e,t,n,a=1,r="valid",s,i=1){return G(()=>{if(s==null&&(s=er()),Bt(s),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ze(e,[0,2,1])),r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Lh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ar(o,n)),o})}function T7(e,t,n,a=[1,1],r="valid",s,i,o=null){return G(()=>{if(s==null&&(s=er()),Bt(s),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=eA(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=vs.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ze(l,[0,3,1,2])),l})}function sL(e,t,n,a=[1,1,1],r="valid",s,i){return G(()=>{if(s==null&&(s=er()),Bt(s),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=I7(e,s);if(r==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Mg(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ar(o,n)),s==="channelsFirst"&&(o=Ze(o,[0,4,1,2,3])),o})}var tA=class extends Qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",tA.verifyArgs(t),this.rank=e,hn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Iu(t.kernelSize,e,"kernelSize"),this.strides=Iu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Sa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Bt(this.dataFormat),this.activation=Ns(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ct(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=nn(t.biasConstraint),this.biasRegularizer=Et(t.biasRegularizer),this.activityRegularizer=Et(t.activityRegularizer),this.dilationRate=Iu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!cy(e.kernelSize,"number",1,3))throw new H(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ss(this.activation),useBias:this.useBias,biasInitializer:$t(this.biasInitializer),biasRegularizer:gt(this.biasRegularizer),activityRegularizer:gt(this.activityRegularizer),biasConstraint:tn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},xp=class extends tA{constructor(e,t){super(e,t);this.kernel=null,xp.verifyArgs(t),this.filters=t.filters,hn(this.filters,"filters"),this.kernelInitializer=Ct(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=nn(t.kernelConstraint),this.kernelRegularizer=Et(t.kernelRegularizer)}build(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return G(()=>{e=Le(e);let n,a=this.bias==null?null:this.bias.read(),r=fv(this.activation.getClassName());if(r!=null&&this.rank===2)n=T7(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=rL(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=T7(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=sL(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=pt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=ir(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:$t(this.kernelInitializer),kernelRegularizer:gt(this.kernelRegularizer),kernelConstraint:tn(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},bp=class extends xp{constructor(e){super(2,e);bp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!cy(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};bp.className="Conv2D";ue.registerClass(bp);var vp=class extends xp{constructor(e){super(3,e);vp.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};vp.className="Conv3D";ue.registerClass(vp);var nA=class extends bp{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return G(()=>{let n=Le(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],d=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=kr(o,p,d,this.padding),f=kr(l,c,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,1]));let g=Wh(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ze(g,[0,3,1,2])),this.bias!=null&&(g=ar(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=pt(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=kr(t[a],o,s,this.padding),t[r]=kr(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};nA.className="Conv2DTranspose";ue.registerClass(nA);var aA=class extends vp{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return G(()=>{let n=Le(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=a[o],d=a[s],u=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=kr(l,f,p,this.padding),A=kr(d,m,c,this.padding),x=kr(u,g,h,this.padding),b=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,4,1]));let v=h3(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ze(v,[0,4,1,2,3])),this.bias!==null&&(v=ar(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=pt(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],d=this.strides[0],u=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=kr(t[a],d,i,this.padding),t[r]=kr(t[r],u,o,this.padding),t[s]=kr(t[s],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};aA.className="Conv3DTranspose";ue.registerClass(aA);var S7=class extends xp{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 H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ct(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Et(t.depthwiseRegularizer),this.depthwiseConstraint=nn(t.depthwiseConstraint),this.pointwiseInitializer=Ct(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Et(t.pointwiseRegularizer),this.pointwiseConstraint=nn(t.pointwiseConstraint)}build(e){if(e=pt(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return G(()=>{e=Le(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ze(e,[0,2,3,1])),n=Kg(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ze(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=$t(this.depthwiseInitializer),e.pointwiseInitializer=$t(this.pointwiseInitializer),e.depthwiseRegularizer=gt(this.depthwiseRegularizer),e.pointwiseRegularizer=gt(this.pointwiseRegularizer),e.depthwiseConstraint=tn(this.depthwiseConstraint),e.pointwiseConstraint=tn(this.pointwiseConstraint),e}};S7.className="SeparableConv";var rA=class extends S7{constructor(e){super(2,e)}};rA.className="SeparableConv2D";ue.registerClass(rA);var U0=class extends xp{constructor(e){super(1,e);U0.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"&&!cy(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};U0.className="Conv1D";ue.registerClass(U0);var sA=class extends Qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return G(()=>{if(e=Le(e),this.dataFormat==="channelsLast"){let n=v0(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return v0(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=v0(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return v0(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}};sA.className="Cropping2D";ue.registerClass(sA);var iA=class extends Qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,bP(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=Le(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ze(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?Me.resizeNearestNeighbor(n,[r,s]):Me.resizeBilinear(n,[r,s]);return Ze(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?Me.resizeNearestNeighbor(n,[r,s]):Me.resizeBilinear(n,[r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};iA.className="UpSampling2D";ue.registerClass(iA);function iL(e,t,n=[1,1],a="valid",r,s){return G(()=>{r==null&&(r=er()),Bt(r);let i=eA(e,r);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ou(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ze(i,[0,3,1,2])),i})}var oA=class extends tA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ct(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=nn(e.depthwiseConstraint),this.depthwiseRegularizer=Et(e.depthwiseRegularizer)}build(e){if(e=pt(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return G(()=>{e=Le(e);let n=iL(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=ir(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=$t(this.depthwiseInitializer),e.depthwiseRegularizer=gt(this.depthwiseRegularizer),e.depthwiseConstraint=tn(this.depthwiseRegularizer),e}};oA.className="DepthwiseConv2D";ue.registerClass(oA);function N7(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function C7(e,t,n,a=!1,r,s,i=!1,o=!1){return G(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let d=[1,0].concat(nr(2,l));if(t=Ze(t,d),s!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Wt(r,-1)),r=Ze(r,d)),a&&(t=pa(t,0),r!=null&&(r=pa(r,0)));let u=[],p,c=n,h=t.shape[0],f=Mn(t),m;r!=null&&(m=Mn(r));for(let y=0;y<h;++y){let A=f[y],x=G(()=>e(A,c));if(r==null)p=x[0],c=x[1];else{let b=G(()=>{let v=m[y],k=Ae(da(v),v),N=le(L(x[0],v),L(c[0],k)),C=c.map((E,O)=>le(L(x[1][O],v),L(E,k)));return{output:N,newStates:C}});p=b.output,c=b.newStates}o&&u.push(p)}let g;return o&&(g=xn(u,1)),[p,g,c]})}var Ir=class extends Qe{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new H0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 qt({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 nr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ey(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return G(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Pe("Constants support is not implemented in RNN yet.");Ey(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new qt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Pe("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new qt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){G(()=>{if(!this.stateful)throw new Wr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Gt([n,a])):this.states_=[Gt([n,this.cell.stateSize])];else if(e==null)Q(this.states_),this.keptStates!=null&&(Q(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>Gt([n,a])):this.states_[0]=Gt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):Q(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!w.arraysEqual(r.shape,i))throw new H(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>pn(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=N7(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof rr){let o=[e].concat(s),l=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=d,u}else return super.apply(e,t)}call(e,t){return G(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Le(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new H(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=C7((c,h)=>{let f=this.cell.call([c].concat(h),i);return[f[0],f.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],d=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?d:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return G(()=>{let t=Gt(e.shape);return t=Ie(t,[1,2]),t=pp(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?by(t,[1,n]):t):this.cell.stateSize>1?[by(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()===Ir.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=sr(a,n);return new e(Object.assign(t,{cell:r}))}};Ir.className="RNN";ue.registerClass(Ir);var wp=class extends Qe{},j0=class extends wp{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,hn(this.units,"units"),this.activation=Ns(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=nn(e.kernelConstraint),this.recurrentConstraint=nn(e.recurrentConstraint),this.biasConstraint=nn(e.biasConstraint),this.dropout=xu([1,Is([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xu([1,Is([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Cs({ones:()=>da(e),rate:this.dropout,training:a,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Cs({ones:()=>da(n),rate:this.recurrentDropout,training:a,dropoutFunc:this.dropoutFunc}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=vr(L(e,s),this.kernel.read()):r=vr(e,this.kernel.read()),this.bias!=null&&(r=ar(r,this.bias.read())),i!=null&&(n=L(n,i));let o=le(r,vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ss(this.activation),useBias:this.useBias,kernelInitializer:$t(this.kernelInitializer),recurrentInitializer:$t(this.recurrentInitializer),biasInitializer:$t(this.biasInitializer),kernelRegularizer:gt(this.kernelRegularizer),recurrentRegularizer:gt(this.recurrentRegularizer),biasRegularizer:gt(this.biasRegularizer),activityRegularizer:gt(this.activityRegularizer),kernelConstraint:tn(this.kernelConstraint),recurrentConstraint:tn(this.recurrentConstraint),biasConstraint:tn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};j0.className="SimpleRNNCell";ue.registerClass(j0);var lA=class extends Ir{constructor(e){e.cell=new j0(e);super(e)}call(e,t){return G(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};lA.className="SimpleRNN";ue.registerClass(lA);var G0=class extends wp{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 H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,hn(this.units,"units"),this.activation=Ns(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ns(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=nn(e.kernelConstraint),this.recurrentConstraint=nn(e.recurrentConstraint),this.biasConstraint=nn(e.biasConstraint),this.dropout=xu([1,Is([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xu([1,Is([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Cs({ones:()=>da(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Cs({ones:()=>da(a),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let d=vr(e,this.kernel.read());this.useBias&&(d=ar(d,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,c]=cn(u,[2*this.units,this.units],u.rank-1),h=vr(a,p),[f,m,g]=cn(d,3,d.rank-1),[y,A]=cn(h,2,h.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=vr(L(o,a),c);l=this.activation.apply(le(g,x));let b=le(L(i,a),L(le(1,Nt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ss(this.activation),recurrentActivation:Ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:$t(this.kernelInitializer),recurrentInitializer:$t(this.recurrentInitializer),biasInitializer:$t(this.biasInitializer),kernelRegularizer:gt(this.kernelRegularizer),recurrentRegularizer:gt(this.recurrentRegularizer),biasRegularizer:gt(this.biasRegularizer),activityRegularizer:gt(this.activityRegularizer),kernelConstraint:tn(this.kernelConstraint),recurrentConstraint:tn(this.recurrentConstraint),biasConstraint:tn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};G0.className="GRUCell";ue.registerClass(G0);var uA=class extends Ir{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 G0(e);super(e)}call(e,t){return G(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uA.className="GRU";ue.registerClass(uA);var kp=class extends wp{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,hn(this.units,"units"),this.activation=Ns(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ns(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=nn(e.kernelConstraint),this.recurrentConstraint=nn(e.recurrentConstraint),this.biasConstraint=nn(e.biasConstraint),this.dropout=xu([1,Is([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xu([1,Is([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=pt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends Ba{apply(i,o){let l=r.apply([s]),d=new k0().apply([s]),u=r.apply([s*2]);return kv(kv(l,d),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return G(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Cs({ones:()=>da(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Cs({ones:()=>da(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,d,u;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let p=vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=le(p,vr(a,this.recurrentKernel.read())),this.useBias&&(p=ar(p,this.bias.read()));let[c,h,f,m]=cn(p,4,p.rank-1);o=this.recurrentActivation.apply(c),l=this.recurrentActivation.apply(h),d=le(L(l,r),L(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(d));return[g,g,d]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ss(this.activation),recurrentActivation:Ss(this.recurrentActivation),useBias:this.useBias,kernelInitializer:$t(this.kernelInitializer),recurrentInitializer:$t(this.recurrentInitializer),biasInitializer:$t(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:gt(this.kernelRegularizer),recurrentRegularizer:gt(this.recurrentRegularizer),biasRegularizer:gt(this.biasRegularizer),activityRegularizer:gt(this.activityRegularizer),kernelConstraint:tn(this.kernelConstraint),recurrentConstraint:tn(this.recurrentConstraint),biasConstraint:tn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};kp.className="LSTMCell";ue.registerClass(kp);var dA=class extends Ir{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&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};dA.className="LSTM";ue.registerClass(dA);var H0=class extends wp{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),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Ey(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{mo(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(sr(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Ry(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}My(t)}};H0.className="StackedRNNCells";ue.registerClass(H0);function Cs(e){let{ones:t,rate:n,training:a=!1,count:r=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),n):Tv(t(),n),o=()=>hp(i,t,a);return!r||r<=1?pn(o().clone()):Array(r).fill(void 0).map(o).map(l=>pn(l.clone()))}var oL=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},E7=class extends Ir{constructor(e){if(e.unroll)throw new Pe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Pe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return G(()=>{if(this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return G(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=Gt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){G(()=>{if(!this.stateful)throw new Wr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new H("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(r)):this.states_=[Gt(r)];else if(e==null)Q(this.states_),this.keptStates!=null&&(Q(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_[0]=Gt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):Q(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!w.arraysEqual(i.shape,o))throw new H(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>pn(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],d=e[o?4:3],u=ir(l,a[0],r,s[0],i[0]),p=ir(d,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};E7.className="ConvRNN2D";var q0=class extends kp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,hn(this.filters,"filters"),this.kernelSize=Iu(n,2,"kernelSize"),this.kernelSize.forEach(o=>hn(o,"kernelSize")),this.strides=Iu(a||1,2,"strides"),this.strides.forEach(o=>hn(o,"strides")),this.padding=r||"valid",Sa(this.padding),this.dataFormat=s||"channelsLast",Bt(this.dataFormat),this.dilationRate=Iu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>hn(o,"dilationRate"))}build(e){var t;e=pt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,d=this.filters;o=new(t=class extends Ba{apply(u,p){let c=l.apply([d]),h=ua([d]),f=l.apply([d*2]);return xy([c,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return G(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Cs({ones:()=>da(a),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(Z,te,J)=>!te||!te[J]?Z:L(te[J],Z),d=l(a,o,0),u=l(a,o,1),p=l(a,o,2),c=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Cs({ones:()=>da(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),A=3,[x,b,v,k]=cn(this.kernel.read(),i,A),[N,C,E,O]=this.useBias?cn(this.bias.read(),i):[null,null,null,null];d=this.inputConv(d,x,N,this.padding),u=this.inputConv(u,b,C,this.padding),p=this.inputConv(p,v,E,this.padding),c=this.inputConv(c,k,O,this.padding);let[D,T,P,_]=cn(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,D),m=this.recurrentConv(m,T),g=this.recurrentConv(g,P),y=this.recurrentConv(y,_);let j=this.recurrentActivation.apply(le(d,f)),q=this.recurrentActivation.apply(le(u,m)),z=le(L(q,s),L(j,this.activation.apply(le(p,g)))),X=L(this.recurrentActivation.apply(le(c,y)),this.activation.apply(z));return[X,X,z]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=oL(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=_r(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ar(r,n,this.dataFormat):r}recurrentConv(e,t){return _r(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};q0.className="ConvLSTM2DCell";ue.registerClass(q0);var pA=class extends E7{constructor(e){let t=new q0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};pA.className="ConvLSTM2D";ue.registerClass(pA);var X0=class extends Qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return hp(()=>Tv(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};X0.className="Dropout";ue.registerClass(X0);var cA=class extends X0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};cA.className="SpatialDropout1D";ue.registerClass(cA);var hA=class extends Qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,hn(this.units,"units"),this.activation=Ns(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=nn(e.kernelConstraint),this.biasConstraint=nn(e.biasConstraint),this.kernelRegularizer=Et(e.kernelRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.activityRegularizer=Et(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=pt(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=pt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e),a=fv(this.activation.getClassName()),r;return a!=null?r=vr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=vr(n,this.kernel.read()),this.bias!=null&&(r=ar(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Ss(this.activation),useBias:this.useBias,kernelInitializer:$t(this.kernelInitializer),biasInitializer:$t(this.biasInitializer),kernelRegularizer:gt(this.kernelRegularizer),biasRegularizer:gt(this.biasRegularizer),activityRegularizer:gt(this.activityRegularizer),kernelConstraint:tn(this.kernelConstraint),biasConstraint:tn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};hA.className="Dense";ue.registerClass(hA);var fA=class extends Qe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=pt(e);for(let t of e.slice(1))if(t==null)throw new H(`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],ks(e,1)]}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=Ze(n,a)}return SP(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};fA.className="Flatten";ue.registerClass(fA);var mA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ns(e.activation)}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ss(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};mA.className="Activation";ue.registerClass(mA);var gA=class extends Qe{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=Le(e),IP(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};gA.className="RepeatVector";ue.registerClass(gA);var yA=class extends Qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new H("Can only specifiy one unknown dimension.");else r*=l}let i=ks(e);if(s!==null){if(r===0||i%r!=0)throw new H(n);a[s]=i/r}else if(i!==r)throw new H(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return V(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};yA.className="Reshape";ue.registerClass(yA);var AA=class extends Qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=nr(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=pt(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ze(Le(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};AA.className="Permute";ue.registerClass(AA);var xA=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Le(e),a=-1;return jd(oo(n,this.maskValue),a)}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e),a=-1,r=!0,s=jd(oo(n,this.maskValue),a,r);return L(n,pe(s,n.dtype))})}};xA.className="Masking";ue.registerClass(xA);var bA=class extends Qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(vt(e.inputLength))}this.inputDim=e.inputDim,hn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,hn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ct(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Et(e.embeddingsRegularizer),this.activityRegularizer=Et(e.activityRegularizer),this.embeddingsConstraint=nn(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=Le(e),oo(e,Ye(e))):null)}computeOutputShape(e){if(e=pt(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e);n.dtype!=="int32"&&(n=b0(n,"int32"));let a=Iv(this.embeddings.read(),V(n,[n.size]));return V(a,pt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:$t(this.embeddingsInitializer),embeddingsRegularizer:gt(this.embeddingsRegularizer),activityRegularizer:gt(this.activityRegularizer),embeddingsConstraint:tn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};bA.className="Embedding";ue.registerClass(bA);var bo=class extends Qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Pe}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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new H("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[pt(e)]),e=e,e.length<2)throw new H(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=ws(t),t.length>1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&ws(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return G(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=Is(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=pp(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let d=o.shape,u=d[0],p=d.slice(1).concat([u]),c=V(o,[u].concat(ks(d.slice(1))));c=Ze(c,[1,0]),c=V(c,p),n.push(c),r=!0}else if(l>1){let d=nr(1,l).concat([0]);n.push(Ze(o,d)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,d=o[l-1],u=[d].concat(o.slice(0,o.length-1));s=V(Ze(V(s,[-1,d]),[1,0]),u)}else if(i>1){let o=[i-1].concat(nr(0,i-1));s=Ze(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=ws(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return G(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Wt(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=La(n,t[a]);return n})}},vA=class extends bo{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=le(t,e[n]);return t})}};vA.className="Add";ue.registerClass(vA);var wA=class extends bo{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};wA.className="Multiply";ue.registerClass(wA);var kA=class extends bo{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=le(t,e[n]);return L(1/e.length,t)})}};kA.className="Average";ue.registerClass(kA);var IA=class extends bo{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ar(t,e[n]);return t})}};IA.className="Maximum";ue.registerClass(IA);var TA=class extends bo{constructor(e){super(e)}mergeFunction(e){return G(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=pu(t,e[n]);return t})}};TA.className="Minimum";ue.registerClass(TA);var SA=class extends bo{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 H("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(w.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return G(()=>xy(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new H("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new H("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new H(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return G(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(pe(da(e[s]),"bool")):t[s].rank<e[s].rank?a.push(Wt(t[s],-1)):a.push(t[s]);let r=mt(a,this.axis);return Ph(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};SA.className="Concatenate";ue.registerClass(SA);function Ip(e,t){for(;e<0;)e+=t;return e}function lL(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Pe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Pe("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return G(()=>{let i;if(a>r){i=a-r;let l=[];for(let d=0;d<i;++d)l.push(1);t=V(t,t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let d=0;d<i;++d)l.push(1);e=V(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=Ie(L(e,t),s[0]):o=Ie(L(Ze(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,d=s[1]===t.shape.length-1;o=Ve(e,t,l,d)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let d=[];for(let u=l;u<l+i;++u)d.push(u);o=st(o,d)}return o.shape.length===1&&(o=Wt(o,1)),o})}var NA=class extends bo{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Pe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new H(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Ip(r,e[s].shape.length)):a=[Ip(this.axes,t.shape.length),Ip(this.axes,n.shape.length)],this.normalize&&(t=O0(t,a[0]),n=O0(n,a[1])),lL(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ip(this.axes,e.length),Ip(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Pe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};NA.className="Dot";ue.registerClass(NA);var CA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e);return hp(()=>le(w0(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};CA.className="GaussianNoise";ue.registerClass(CA);var EA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return G(()=>{this.invokeCallHook(e,t);let n=Le(e);return this.rate>0&&this.rate<1?hp(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return L(n,w0(n.shape,1,a))},()=>n,t.training||!1):n})}};EA.className="GaussianDropout";ue.registerClass(EA);var RA=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Le(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 hp(()=>{let a=Le(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=xs(cu(n),this.rate);o=b0(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,d=-l*i*this.rate,u=le(L(a,o),L(le(o,-1),i));return le(L(u,l),d)},()=>Le(e),t.training||!1)}return e})}};RA.className="AlphaDropout";ue.registerClass(RA);function Tp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=s3(e,t,n,a,r,s);else if(e.rank===3)i=i3(e,t,n,a,r,s);else if(e.rank===4)i=o3(e,t,n,a,r,s);else throw new Pe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function uL(e,t,n,a,r=.001){return G(()=>{let s=Xh(e,a),i=s.mean,o=s.variance;return[Tp(e,i,o,n,t,r),i,o]})}function dL(e,t,n,a,r=.001){return G(()=>{let s=Xh(e,a),i=s.mean,o=s.variance,l=[];for(let h of nr(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let d=V(i,l),u=V(o,l),p=t==null?null:V(t,l),c=n==null?null:V(n,l);return[Tp(e,d,u,c,p,r),i,o]})}function pL(e,t,n,a,r=.001){return w.arraysEqual(a.slice().sort(),nr(0,e.rank-1))?uL(e,t,n,a,r):dL(e,t,n,a,r)}var MA=class extends Qe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ct(e.betaInitializer||"zeros"),this.gammaInitializer=Ct(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ct(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ct(e.movingVarianceInitializer||"ones"),this.betaConstraint=nn(e.betaConstraint),this.gammaConstraint=nn(e.gammaConstraint),this.betaRegularizer=Et(e.betaRegularizer),this.gammaRegularizer=Et(e.gammaRegularizer)}build(e){e=pt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return G(()=>{let n=t.training==null?!1:t.training,a=Le(e),r=a.shape,s=r.length,i=nr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=co(1,s);l[o]=r[o];let d=i.slice();d.sort();let u=!w.arraysEqual(d,nr(0,s).slice(0,s-1)),p=()=>{if(u){let g=V(this.movingMean.read(),l),y=V(this.movingVariance.read(),l),A=this.center?V(this.beta.read(),l):null,x=this.scale?V(this.gamma.read(),l):null;return Tp(a,g,y,A,x,this.epsilon)}else return Tp(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,f]=pL(a,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{G(()=>{let x=1-A,b=g.read(),v=L(Ae(b,y),x);g.write(Ae(b,v))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:$t(this.betaInitializer),gammaInitializer:$t(this.gammaInitializer),movingMeanInitializer:$t(this.movingMeanInitializer),movingVarianceInitializer:$t(this.movingVarianceInitializer),betaRegularizer:gt(this.betaRegularizer),gammaRegularizer:gt(this.gammaRegularizer),betaConstraint:tn(this.betaConstraint),gammaConstraint:tn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};MA.className="BatchNormalization";ue.registerClass(MA);var FA=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ct(e.betaInitializer||"zeros"),this.gammaInitializer=Ct(e.gammaInitializer||"ones"),this.betaRegularizer=Et(e.betaRegularizer),this.gammaRegularizer=Et(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=pt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ws(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Le(e),a=n.shape,r=a.length;return G(()=>{let s=!0,{mean:i,variance:o}=Xh(n,this.axis,s),l=co(1,r);for(let f of this.axis)l[f]=a[f];let d=f=>f!=null&&f.shape.length!==r?V(f,l):f,u=d(this.gamma.read()),p=d(this.beta.read()),c=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(c.push(a[f]),h.push(1)):(c.push(1),h.push(a[f]));return i=Ia(i,c),o=Ia(o,c),u=Ia(u,h),p=Ia(p,h),Tp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:$t(this.betaInitializer),gammaInitializer:$t(this.gammaInitializer),betaRegularizer:gt(this.betaRegularizer),gammaRegularizer:gt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};FA.className="LayerNormalization";ue.registerClass(FA);function cL(e,t,n){return G(()=>{if(e.rank!==4)throw new H(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=er()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],Ta(e,a)})}var DA=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?er():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=pt(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(()=>cL(Le(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};DA.className="ZeroPadding2D";ue.registerClass(DA);function K0(e,t,n,a,r,s){return G(()=>{Bt(r),Av(s),Sa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=er()),s==null&&(s="max"),e=eA(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Jd(e,t,n,o):i=Hd(e,t,n,o),r==="channelsFirst"&&(i=Ze(i,[0,3,1,2])),i})}function R7(e,t,n,a,r,s){return G(()=>{Bt(r),Av(s),Sa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=er()),s==null&&(s="max"),e=I7(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Ug(e,t,n,o):i=Ng(e,t,n,o),r==="channelsFirst"&&(i=Ze(i,[0,4,1,2,3])),i})}var M7=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(hn(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 H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);hn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Sa(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=pt(e);let t=ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return G(()=>{this.invokeCallHook(e,t),e=pp(Le(e),2);let n=this.poolingFunction(Le(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return st(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},$A=class extends M7{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Bt(r),Sa(a),K0(e,t,n,a,r,"max")}};$A.className="MaxPooling1D";ue.registerClass($A);var OA=class extends M7{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Bt(r),Sa(a),K0(e,t,n,a,r,"avg")}};OA.className="AveragePooling1D";ue.registerClass(OA);var F7=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new H(`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];hn(this.poolSize,"poolSize"),hn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),Sa(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ir(t,this.poolSize[0],this.padding,this.strides[0]),n=ir(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(Le(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}},_A=class extends F7{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Bt(r),Sa(a),K0(e,t,n,a,r,"max")}};_A.className="MaxPooling2D";ue.registerClass(_A);var PA=class extends F7{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Bt(r),Sa(a),K0(e,t,n,a,r,"avg")}};PA.className="AveragePooling2D";ue.registerClass(PA);var D7=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new H(`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];hn(this.poolSize,"poolSize"),hn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),Sa(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ir(t,this.poolSize[0],this.padding,this.strides[0]),n=ir(n,this.poolSize[1],this.padding,this.strides[1]),a=ir(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return G(()=>(this.invokeCallHook(e,t),this.poolingFunction(Le(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}},zA=class extends D7{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Bt(r),Sa(a),R7(e,t,n,a,r,"max")}};zA.className="MaxPooling3D";ue.registerClass(zA);var LA=class extends D7{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Bt(r),Sa(a),R7(e,t,n,a,r,"avg")}};LA.className="AveragePooling3D";ue.registerClass(LA);var $7=class extends Qe{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}},WA=class extends $7{constructor(e){super(e||{})}call(e,t){return G(()=>{let n=Le(e);return Dt(n,1)})}};WA.className="GlobalAveragePooling1D";ue.registerClass(WA);var BA=class extends $7{constructor(e){super(e||{})}call(e,t){return G(()=>{let n=Le(e);return Rn(n,1)})}};BA.className="GlobalMaxPooling1D";ue.registerClass(BA);var O7=class extends Qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Bt(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},VA=class extends O7{call(e,t){return G(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Dt(n,[1,2]):Dt(n,[2,3])})}};VA.className="GlobalAveragePooling2D";ue.registerClass(VA);var UA=class extends O7{call(e,t){return G(()=>{let n=Le(e);return this.dataFormat==="channelsLast"?Rn(n,[1,2]):Rn(n,[2,3])})}};UA.className="GlobalMaxPooling2D";ue.registerClass(UA);var _7=class extends Qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=sr(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},jA=class extends _7{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=pt(e),e.length<3)throw new H(`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=pt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return G(()=>(e=Le(e),C7((n,a)=>[Le(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};jA.className="TimeDistributed";ue.registerClass(jA);function hL(e){fo(xP,"BidirectionalMergeMode",e)}var fL="concat",GA=class extends _7{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=sr(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=sr(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fL:e.mergeMode,hL(this.mergeMode),e.weights)throw new Pe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Jn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=N7(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new H("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let d=n.map(u=>new qt({shape:u.shape}));this.forwardLayer.stateSpec=d.slice(0,l/2),this.backwardLayer.stateSpec=d.slice(l/2),i.push(...d)}if(a!=null)throw new Pe("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof rr;for(let l of s)if(l instanceof rr!==o)throw new H("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),d=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=d;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return G(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=pa(r,1));let i;return this.mergeMode==="concat"?i=xy([a,r]):this.mergeMode==="sum"?i=le(a,r):this.mergeMode==="ave"?i=L(.5,le(a,r)):this.mergeMode==="mul"?i=L(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){mo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),mo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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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),Ua(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,pn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let a=0;a<this.size();a++)e.push(a)}if(e.length===0)return Lt([],[0].concat(this.elementShape));let n=this.readMany(e);return Ua(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),xn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Lt([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return Ua(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),mt(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,Mn(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];G(()=>{t=V(t,[1,n,r]);for(let o=0;o<e.length;++o){let l=o===0?0:a[o-1],d=[0,l,0],u=[1,e[o],r];s[o]=V(De(t,d,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Np=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);Ua(t,r.shape,"TensorList shape mismatch: "),pn(r)}),this.idTensor=Ce(0),this.maxNumElements=a,pn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Np([...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.`);Ua(e,this.elementShape,"TensorList shape mismatch: ");let a=Sp(this.elementShape,this.tensors,e);return G(()=>{let r=this.tensors.map(s=>V(s,a));return xn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Sp(this.elementShape,this.tensors,e),a=this.tensors.pop();return Ua(a.shape,e,"TensorList shape mismatch: "),V(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Ua(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");pn(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.`);Ua(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=Sp(this.elementShape,this.tensors,t);return V(this.tensors[e],a)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Ua(this.elementShape,t.shape,"TensorList shape mismatch: "),pn(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}`);Ua(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=Sp(this.elementShape,this.tensors,n);return e.length===0?Lt([],[0].concat(a)):G(()=>{let r=e.map(s=>V(this.tensors[s],a));return xn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Ua(this.elementShape,t,"TensorList shape mismatch: ");let n=Sp(this.elementShape,this.tensors,t);return this.size()===0?Lt([],[0].concat(n)):G(()=>{let a=this.tensors.map(r=>V(r,n));return mt(a,0)})}};function AB(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);Ua(r,t,"TensorList shape mismatch: ");let s=Mn(e);return new Np(s,t,a)}function xB(e,t,n){return new Np([],e,t,n)}function bB(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new Np([],n,e.dtype,a),i=Mn(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function vB(e,t,n){let a=0,r=t.map(u=>(a+=u,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=o2(s,n),o=a===0?0:e.size/a,l=G(()=>{let u=[];e=V(e,[1,a,o]);for(let p=0;p<t.length;++p){let c=p===0?0:r[p-1],h=[0,c,0],f=[1,t[p],o];u[p]=V(De(e,h,f),i)}return e.dispose(),u}),d=new Np([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)d.setItem(u,l[u]);return d}var wB=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let d=s;for(;l[0];){let u=d;d=await 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a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=xB(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=AB(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let 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G(()=>IB(s,i,o));case"dynamic":return TB(s,i,o);case"evaluation":return G(()=>SB(s,i,o));case"image":return G(()=>RB(s,i,o));case"graph":return G(()=>NB(s,i,o));case"logical":return G(()=>MB(s,i,o));case"matrices":return G(()=>FB(s,i,o));case"normalization":return G(()=>DB(s,i,o));case"reduction":return G(()=>$B(s,i,o));case"slice_join":return G(()=>OB(s,i,o));case"sparse":return G(()=>_B(s,i,o));case"spectral":return G(()=>PB(s,i,o));case"string":return G(()=>zB(s,i,o));case"transformation":return G(()=>LB(s,i,o));case"hash_table":return EB(s,i,o,a);case"custom":let l=X7(s.op);if(l&&l.customExecutor)return l.customExecutor(new fB(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function ww(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,d=Object.keys(e).map(c=>ca(c)[0]),u=[];a!=null&&(u=a.map(c=>ca(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((kw(c)||jB(c)||GB(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&d.indexOf(c.name)===-1&&u.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function WB(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>ca(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.has(u.name)&&s.push(u)});let l=new Set,d=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||d.push(u),u.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>l.has(c.name))&&s.push(p)})}return d}var BB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],VB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],UB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function kw(e){return BB.indexOf(e.op)>=0}function jB(e){return VB.indexOf(e.op)>=0}function GB(e){return UB.indexOf(e.op)>=0}var u2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new u2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=ww(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return WB(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[ca(u)[0]]),r=t.map(u=>ca(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},d={};return G(()=>{let u=new vw(this.weightMap,l,d,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=ca(f),y=[];y[g]=e[f],p[m]=y});let c=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=bw(m,p,u,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let y=o.filter(A=>!kw(A)&&!Dn(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw u!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. 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d}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Ur(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Dn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Dn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=ca(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=ca(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=ca(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},HB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},qB="?tfjs-format=file",XB="model.json",Iw=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new HB}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Xn.browserHTTPRequest(e,this.loadOptions);else{let t=Xn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Xn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=Xn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new u2(fw.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=fw.Instance.transformGraph(e.modelInitializer);this.initializer=new u2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Xn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 je)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function et(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Q(e.value)}}},hV=class extends fn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Xa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Xa.getTensorsInContainer(n);for(let r of t)Xa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},fV=class extends fn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Mw=class 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${e}`);let a;return this.size===1/0||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),ha(async()=>(await n.iterator()).columnMajorBatch(e,t,bV),a)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ha(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ha(async()=>(await t.iterator()).filter(a=>G(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ha(async()=>(await t.iterator()).map(n=>G(()=>e(n))),this.size)}mapAsync(e){let t=this;return ha(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 ha(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ha(async()=>{let a=p2(async()=>({value:await t.iterator(),done:!1}));return rV(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ha(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=ZB.alea(t||w.now().toString());return ha(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,ha(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Su.MAX_BUFFER_SIZE=1e4;function ha(e,t=null){return new class extends Su{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function AV(e){return ha(async()=>Rw(e),e.length)}function xV(e){if(!Tu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return ha(async()=>{let n=await Cw(e,a=>{if(a instanceof Su)return{value:a.iterator(),recurse:!1};if(Tu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return sV(n,Es.SHORTEST)},t)}function bV(e){if(e===null)return null;let t=e[0];return eV(t)?{value:vV(e),recurse:!1}:{value:null,recurse:!0}}function vV(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof je?xn(e):Lt(e)}var $w=class extends Su{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},ef='"',Cp=Symbol("out"),Ow=Symbol("field"),tf=Symbol("quote"),h2=Symbol("quoteafterquote"),_w=Symbol("quoteinquote"),Pw=class extends Su{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 $w(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let d=Number(o);if(isNaN(d))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=d;else switch(i.dtype){case"float32":l=d;break;case"int32":l=Math.floor(d);break;case"bool":l=this.getBoolean(o);break;default:l=d}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=Cp;for(let i=0;i<r;i++)switch(s){case Cp:switch(e.charAt(i)){case ef:a=i+1,s=tf;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Cp;break;default:s=Ow,a=i;break}break;case Ow:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Cp,a=i+1;break;default:}break;case tf:switch(e.charAt(i)){case ef:s=h2;break;default:}break;case h2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Cp,a=i+1;break;case ef:s=tf;break;default:s=_w;break}break;case _w:switch(e.charAt(i)){case ef:s=tf;break;default:}break;default:}if(s===h2?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},zw=class extends fn{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(ne().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new zw(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),Lt(n,t)}},Lw=class extends fn{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=Ht([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Qa([s,r,o,i],[1,4])}else this.cropBox=Qa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(ne().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 Lw(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=za.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return G(()=>{let t=Wt(pe(e,"float32"),0),n;n=Me.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return V(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Ww=class{},Bw=class extends fn{split(e){return new wV(this,e)}},wV=class extends Bw{constructor(e,t){super();this.upstream=e,this.impl=new kV(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},kV=class extends c2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},IV=class extends fn{decodeUTF8(){return new TV(this)}},TV=class extends Bw{constructor(e){super();this.upstream=e,this.impl=new SV(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SV=class extends c2{constructor(e){super();if(this.upstream=e,ne().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=W5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ne().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Vw=class extends IV{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(ne().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function NV(e,t={},n){let a,r;typeof e=="string"?a=e:(a=e.url,r=CV(e));let s=await(n||w.fetch)(a,r);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new Vw(i,t)}else throw new Error(s.statusText)}var CV=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Uw(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var jw=class extends Ww{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Uw(this.input)&&ne().get("IS_NODE")){let e=Ho("fs");this.input=e.readFileSync(this.input.substr(7))}return new Vw(this.input,this.options)}},Gw=class extends Ww{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Uw(this.url)?new jw(this.url,this.fileOptions).iterator():NV(this.url,this.fileOptions)}};function EV(e,t={}){return new Pw(new Gw(e),t)}function RV(e){let t=p2(e);return ha(async()=>t)}function MV(e){return ha(async()=>{let t=await e();return p2(()=>t.next())})}async function FV(e,t){return Lw.create(e,t)}async function DV(e){return zw.create(e)}var $V="3.10.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var OV=xr.whereImpl,f2=class extends fd{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Oc(this,sa())}nextDataId(){return f2.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,ne().get("IS_NODE")&&R.warn(`
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============================
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Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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============================`));let a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(s=>w.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,a,r){this.data.set(e,{values:t,dtype:a,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(a,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>w.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ge(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return sa().makeTensorFromDataId(a,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Ne([e],"where");let t=this.readSync(e.dataId);return OV(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};f2.nextDataId=0;var Hw={};ze(Hw,{addImpl:()=>Xw,bincountImpl:()=>g2,bincountReduceImpl:()=>Kw,ceilImpl:()=>Zw,concatImpl:()=>y2,equalImpl:()=>Yw,expImpl:()=>Qw,expm1Impl:()=>t6,floorImpl:()=>n6,gatherNdImpl:()=>a6,gatherV2Impl:()=>r6,greaterEqualImpl:()=>i6,greaterImpl:()=>s6,lessEqualImpl:()=>l6,lessImpl:()=>o6,linSpaceImpl:()=>u6,logImpl:()=>d6,maxImpl:()=>p6,maximumImpl:()=>c6,minimumImpl:()=>h6,multiplyImpl:()=>A2,negImpl:()=>f6,notEqualImpl:()=>m6,prodImpl:()=>g6,rangeImpl:()=>b2,rsqrtImpl:()=>y6,sigmoidImpl:()=>kU,simpleAbsImpl:()=>qw,sliceImpl:()=>rf,sparseFillEmptyRowsImpl:()=>x6,sparseReshapeImpl:()=>b6,sparseSegmentReductionImpl:()=>v2,sqrtImpl:()=>SU,squaredDifferenceImpl:()=>v6,stridedSliceImpl:()=>w6,stringNGramsImpl:()=>k6,stringSplitImpl:()=>I6,stringToHashBucketFastImpl:()=>T6,subImpl:()=>S6,tileImpl:()=>N6,topKImpl:()=>E6,transposeImpl:()=>x2,uniqueImpl:()=>R6});function qw(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var _V=e=>{let{x:t}=e.inputs,n=e.backend;Ne(t,"abs");let a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=qw(r),n.makeOutput(a,t.shape,t.dtype)},PV={kernelName:Ko,backendName:"cpu",kernelFunc:_V};function Xt(e){return(t,n,a,r,s)=>{let i=R.assertAndGetBroadcastShape(t,n),o=i.length,l=w.computeStrides(i),d=w.sizeFromShape(i),u=w.getTypedArrayFromDType(s,d),p=t.length,c=n.length,h=w.computeStrides(t),f=w.computeStrides(n),m=R.getBroadcastDims(t,i),g=R.getBroadcastDims(n,i);if(m.length+g.length===0)for(let y=0;y<u.length;++y)u[y]=e(a[y%a.length],r[y%r.length]);else for(let y=0;y<u.length;++y){let A=w.indexToLoc(y,o,l),x=A.slice(-p);m.forEach(N=>x[N]=0);let b=w.locToIndex(x,p,h),v=A.slice(-c);g.forEach(N=>v[N]=0);let k=w.locToIndex(v,c,f);u[y]=e(a[b],r[k])}return[u,i]}}function fa(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=n.makeTensorInfo(a.shape,"complex64"),l=n.data.get(o.dataId);return 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i=nf(n,r.shape,r.dtype),o=Rs({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=fa({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=vo({inputs:{input:r},backend:n}),o=Rs({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=Tr({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(r.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(r.shape,"int32",o)}if(s==="bool"){let i=n.data.get(r.dataId).values,o=w.toTypedArray([0],r.dtype),[l,d]=Xt((u,p)=>u!==p?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(d,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var BV={kernelName:ei,backendName:"cpu",kernelFunc:Rs};function mn(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;Ne([i,o],e);let 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Yw=Xt((e,t)=>e===t?1:0),Jw=mn(dl,Yw,null,"bool"),HV={kernelName:dl,backendName:"cpu",kernelFunc:Jw},Qw=Ms(e=>Math.exp(e)),e6=Nu(di,Qw,"float32"),qV={kernelName:di,backendName:"cpu",kernelFunc:e6},t6=Ms(e=>Math.expm1(e)),XV=Nu(cl,t6),KV={kernelName:cl,backendName:"cpu",kernelFunc:XV},n6=Ms(e=>Math.floor(e)),ZV=Nu(pi,n6),YV={kernelName:pi,backendName:"cpu",kernelFunc:ZV};function a6(e,t,n,a,r,s,i,o,l){let d=Ge([a,s],n);for(let u=0;u<a;u++){let p=[],c=0;for(let h=0;h<r;h++){let f=e[u*r+h];c+=f*i[h],p.push(f)}if(c<0||c>=l/s)throw new Error(`Invalid indices: ${p} does not index into ${o}`);for(let h=0;h<s;h++)d.values[u*s+h]=t.get(...t.indexToLoc(c*s+h))}return d}function r6(e,t,n){let a=Ge(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let d=e.locToIndex(s);a.values[r]=e.values[d]}return a}var s6=Xt((e,t)=>e>t?1:0),JV=mn(gl,s6,null,"bool"),QV={kernelName:gl,backendName:"cpu",kernelFunc:JV},i6=Xt((e,t)=>e>=t?1:0),eU=mn(fi,i6,null,"bool"),tU={kernelName:fi,backendName:"cpu",kernelFunc:eU},o6=Xt((e,t)=>e<t?1:0),nU=mn(bl,o6,null,"bool"),aU={kernelName:bl,backendName:"cpu",kernelFunc:nU},l6=Xt((e,t)=>e<=t?1:0),rU=mn(vl,l6,null,"bool"),sU={kernelName:vl,backendName:"cpu",kernelFunc:rU};function u6(e,t,n){let a=(t-e)/(n-1),r=w.makeZerosTypedArray(n,"float32");r[0]=e;for(let s=1;s<r.length;s++)r[s]=r[s-1]+a;return r}var d6=Ms(e=>Math.log(e)),iU=Nu(yi,d6),oU={kernelName:yi,backendName:"cpu",kernelFunc:iU};function p6(e,t,n,a){let r=w.getTypedArrayFromDType(a,w.sizeFromShape(n));for(let s=0;s<r.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let d=e[i+l];(Number.isNaN(d)||d>o)&&(o=d)}r[s]=o}return r}var c6=Xt((e,t)=>Math.max(e,t)),lU=mn(xi,c6),uU={kernelName:xi,backendName:"cpu",kernelFunc:lU},h6=Xt((e,t)=>Math.min(e,t)),dU=mn(ki,h6),pU={kernelName:ki,backendName:"cpu",kernelFunc:dU},A2=Xt((e,t)=>e*t),cU=m2((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),af=mn(Ti,A2,cU),hU={kernelName:Ti,backendName:"cpu",kernelFunc:af};function f6(e,t,n){let a=w.createScalarValue(-1,n);return A2([],t,a,e,n)}function fU(e){let{inputs:t,backend:n}=e,{x:a}=t;Ne(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=f6(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var mU={kernelName:Tl,backendName:"cpu",kernelFunc:fU},m6=Xt((e,t)=>e!==t?1:0),gU=mn(Sl,m6,null,"bool"),yU={kernelName:Sl,backendName:"cpu",kernelFunc:gU};function x2(e,t,n,a,r){let s=t.length,i=w.sizeFromShape(t),o=w.computeStrides(t),l=w.computeStrides(r),d=w.getTypedArrayFromDType(n,w.sizeFromShape(r));for(let u=0;u<i;++u){let p=w.indexToLoc(u,s,o),c=new Array(p.length);for(let f=0;f<c.length;f++)c[f]=p[a[f]];let 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c=u.indexToLoc(p),h=c.map((f,m)=>f+t[m]);u.set(d.get(...h),...c)}return r==="string"?R.fromStringArrayToUint8(u.values):u.values}function wo(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;Ne(r,"slice");let[o,l]=En.parseSliceParams(r,s,i);En.assertParamsValid(r,o,l);let d=n.data.get(r.dataId).values,u=rf(d,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}var TU={kernelName:zl,backendName:"cpu",kernelFunc:wo};function x6(e,t,n,a,r,s,i){let o=t[0],l=s[0],d=new Array(l),u=new Array(o),p=t[1];if(l===0){if(o!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
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indices.shape[0] = ${o}`);let g=w.getArrayFromDType(n,0),y=w.getArrayFromDType(r,0);return[g,[0,p],y,d,u]}let c=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<o;++g){let y=e[g*p];if(y<0)throw new Error(`indices(${g}, 0) is invalid: ${y} < 0`);if(y>=l)throw new Error(`indices(${g}, 0) is invalid: ${y} >= ${l}`);++f[y],c=c&&y>=h,h=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;d[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&c){let g=e,y=a;for(let A=0;A<o;++A)u[A]=A;return[g,[o,p],y,d,u]}else{let g=f[l-1],y=w.getArrayFromDType(n,g*p),A=w.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let b=0;b<o;++b){let v=e[b*p],k=x[v],N=(v===0?0:f[v-1])+k;x[v]++;for(let C=0;C<p;++C)y[N*p+C]=e[b*p+C];A[N]=a[b],u[b]=N}for(let b=0;b<l;++b)if(x[b]===0){let v=b===0?0:f[b-1];y[v*p+0]=b;for(let k=1;k<p;++k)y[v*p+k]=0;A[v]=i}return[y,[g,p],A,d,u]}}function b6(e,t,n,a,r){let s=w.sizeFromShape(a),i=t[0],o=r.length,l=[],d=1,u=-1;for(let g=0;g<o;++g){let y=r[g];if(y===-1){if(u!==-1)throw new Error(`only one output dimension may be -1, not both ${u} and ${g}`);u=g,l.push(1)}else{if(y<0)throw new Error(`size ${g} must be non-negative, not ${y}`);d*=y,l.push(y)}}if(u!==-1){if(d<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(s/d);if(d*g!==s)throw new Error(`Input to reshape is a SparseTensor with ${s}
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dense values, but the requested shape requires a multiple of ${d}. inputShape=${a} outputShape= ${l}`);l[u]=g}let p=w.sizeFromShape(l);if(p!==s)throw new Error(`Input to reshape is a tensor with ${s} dense values, but the requested shape has ${p}. inputShape=${a} outputShape=${l}`);let c=a.length,h=[];if(c>0){h[c-1]=1;for(let g=c-2;g>=0;--g)h[g]=h[g+1]*a[g+1]}let f=[];if(o>0){f[o-1]=1;for(let g=o-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=w.getArrayFromDType(n,i*o);for(let g=0;g<i;++g){let y=0;for(let A=0;A<c;++A)y+=e[g*c+A]*h[A];for(let A=0;A<o;++A)m[g*o+A]=Math.trunc(y/f[A]),y%=f[A]}return[m,[i,o],l]}function v2(e,t,n,a,r,s=!1,i=0){let o=a.length;if(o!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],d=l[1],u=o>0?r[o-1]+1:0;if(u<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=u;let c=p.reduce((A,x)=>A*x,1),h=w.getArrayFromDType(n,c);if(o===0)return u>0&&h.fill(i),[h,p];if(u<=0)throw new Error("segment ids must be >= 0");let f=0,m=1,g=0,y=r[f];for(;;){let A=0;if(m<o){if(A=r[m],y===A){++m;continue}if(y>=A)throw new Error("segment ids are not increasing")}if(y<0||y>=u)throw new Error(`Segment id ${y} out of range [0, ${u}), possibly because segmentIds input is not sorted.`);y>g&&h.fill(i,g*d,y*d);for(let x=f;x<m;++x){let b=a[x];if(b<0||b>=l[0])throw new Error(`Bad: indices[${x}] == ${a[x]} out of range [0, ${l[0]})`);for(let v=0;v<d;v++)h[y*d+v]+=e[b*d+v]}if(s)for(let x=0;x<d;x++)h[y*d+x]/=m-f;if(f=m,++m,g=y+1,y=A,m>o)break}return g<u&&h.fill(i,g*d,u*d),[h,p]}var SU=Ms(e=>Math.sqrt(e)),NU=ct(zi,e=>Math.sqrt(e)),CU={kernelName:zi,backendName:"cpu",kernelFunc:NU},v6=Xt((e,t)=>{let n=e-t;return n*n}),EU=mn(Bi,v6),RU={kernelName:Bi,backendName:"cpu",kernelFunc:EU};function w6(e,t,n,a){let r=Ge(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let l=0;l<o.length;l++)o[l]=i[l]*n[l]+a[l];r.set(t.get(...o),...i)}return r}var MU=class{constructor(e,t,n,a,r,s){this.separator=w.encodeString(e),this.nGramWidths=t,this.leftPad=w.encodeString(n),this.rightPad=w.encodeString(a),this.padWidth=r,this.preserveShort=s}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,a,r,s){for(let i=0;i<r;++i){let o=this.getPadWidth(s),l=Math.max(0,o-i),d=Math.max(0,o-(r-(i+1))),u=s-(l+d),p=t+(l>0?0:i-o),c=0;c+=l*this.leftPad.length;for(let g=0;g<u;++g)c+=e[p+g].length;c+=d*this.rightPad.length,c+=(l+d+u-1)*this.separator.length,n[a+i]=new Uint8Array(c);let h=n[a+i],f=0,m=g=>g.forEach(y=>h[f++]=y);for(let g=0;g<l;++g)m(this.leftPad),m(this.separator);for(let g=0;g<u-1;++g)m(e[p+g]),m(this.separator);if(u>0){m(e[p+u-1]);for(let g=0;g<d;++g)m(this.separator),m(this.rightPad)}else{for(let g=0;g<d-1;++g)m(this.rightPad),m(this.separator);m(this.rightPad)}}}compute(e,t){let n=e.length,a=t.length;if(a>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let l=1;l<a;++l){let d=t[l]>=o;if(d=d&&t[l]<=n,!d)throw new Error(`Invalid split value ${t[l]}, must be in [${o}, ${n}]`);o=t[l]}if(o!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${o}`)}let r=a-1,s=w.getArrayFromDType("int32",a);if(n===0||a===0){let o=new Array(n);for(let l=0;l<=r;++l)s[l]=0;return[o,s]}s[0]=0;for(let o=1;o<=r;++o){let l=t[o]-t[o-1],d=0;this.nGramWidths.forEach(u=>{d+=this.getNumNGrams(l,u)}),this.preserveShort&&l>0&&d===0&&(d=1),s[o]=s[o-1]+d}let i=new Array(s[r]);for(let o=0;o<r;++o){let l=t[o],d=s[o];if(this.nGramWidths.forEach(u=>{let p=t[o+1]-t[o],c=this.getNumNGrams(p,u);this.createNGrams(e,l,i,d,c,u),d+=c}),this.preserveShort&&d===s[o]){let u=t[o+1]-t[o];if(u===0)continue;let p=u+2*this.padWidth,c=1;this.createNGrams(e,l,i,d,c,p)}}return[i,s]}};function k6(e,t,n,a,r,s,i,o){return new MU(n,a,r,s,i,o).compute(e,t)}function FU(e,t,n,a){if(!e.length)return;if(t.length===0){for(let s=0;s<e.length;++s)a.push(e.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=e.indexOf(s);for(;i!==-1;){let o=e.subarray(0,i);(!n||o.length!==0)&&a.push(o),e=e.subarray(i+1),i=e.indexOf(s)}(!n||e.length!==0)&&a.push(e);return}let r=0;for(let s=0;s<e.length+1;s++)if(s===e.length||t.indexOf(e[s])!==-1){let i=e.subarray(r,s);(!n||i.length!==0)&&a.push(i),r=s+1}}function I6(e,t,n){let a=e.length,r=[],s=0,i=0,o=new Array(a);for(let c=0;c<a;++c){let h=r.length;FU(e[c],t,n,r);let f=r.length-h;o[c]=f,s+=f,i=Math.max(i,f)}let l=w.getArrayFromDType("int32",s*2),d=new Array(s),u=[a,i],p=0;for(let c=0;c<a;++c)for(let h=0;h<o[c];++h)l[p*2]=c,l[p*2+1]=h,d[p]=r[p],++p;return[l,d,u]}function T6(e,t){let n=w.getArrayFromDType("int32",e.length);for(let a=0;a<e.length;++a)n[a]=w.fingerPrint64(e[a]).modulo(t).getLowBitsUnsigned();return n}var S6=Xt((e,t)=>e-t),DU=m2((e,t,n,a)=>({real:e-n,imag:t-a})),w2=mn(Vi,S6,DU),$U={kernelName:Vi,backendName:"cpu",kernelFunc:w2};function N6(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let a=Ge(n,e.dtype);for(let r=0;r<a.values.length;++r){let s=a.indexToLoc(r),i=new Array(e.rank);for(let l=0;l<i.length;l++)i[l]=s[l]%e.shape[l];let o=e.locToIndex(i);a.values[r]=e.values[o]}return a}var Rp=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function C6(e,t,n=0,a=e.length-1){for(;a>n;){if(a-n>600){let o=a-n+1,l=t-n+1,d=Math.log(o),u=.5*Math.exp(2*d/3),p=.5*Math.sqrt(d*u*(o-u)/o)*Math.sign(l-o/2),c=Math.max(n,Math.floor(t-l*u/o+p)),h=Math.min(a,Math.floor(t+(o-l)*u/o+p));C6(e,t,c,h)}let r=e[t],s=n,i=a;for(w.swap(e,n,t),Rp(e[a],r)>0&&w.swap(e,n,a);s<i;){for(w.swap(e,s,i),s++,i--;Rp(e[s],r)<0;)s=s+1;for(;Rp(e[i],r)>0;)i=i-1}Rp(e[n],r)===0?w.swap(e,n,i):(i=i+1,w.swap(e,i,a)),i<=t&&(n=i+1),t<=i&&(a=i-1)}}function E6(e,t,n,a,r){let s=t[t.length-1],[i,o]=[e.length/s,s],l=w.getTypedArrayFromDType(n,i*a),d=w.getTypedArrayFromDType("int32",i*a);for(let p=0;p<i;p++){let c=p*o,h=e.subarray(c,c+o),f=new Array(h.length);h.forEach((A,x)=>f[x]={value:A,index:x}),a<f.length&&(C6(f,a),f=f.slice(0,a)),r&&f.sort(Rp);let m=p*a,g=l.subarray(m,m+a),y=d.subarray(m,m+a);for(let A=0;A<a;A++)g[A]=f[A].value,y[A]=f[A].index}let u=t.slice();return u[u.length-1]=a,[Ge(u,n,l),Ge(u,"int32",d)]}function R6(e,t,n,a){let r=w.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let f=0;f<r;f++)s[0]*=n[f];s[1]=n[r];for(let f=r+1;f<n.length;f++)s[2]*=n[f];let i={},o=new Int32Array(n[r]),l=new Jt(s,a,e),d=[],u=s[0]===1&&s[2]===1;for(let f=0;f<n[r];f++){let m;if(u)m=e[f].toString();else{let g=[];for(let y=0;y<s[0];y++)for(let A=0;A<s[2];A++)g.push(l.get(y,f,A));m=g.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let g=Object.keys(i).length;i[m]=g,o[f]=g,d.push(f)}}let p=s.slice();p[1]=Object.keys(i).length;let c=new Jt(p,a);d.forEach((f,m)=>{for(let g=0;g<s[0];g++)for(let y=0;y<s[2];y++)c.set(l.get(g,f,y),g,m,y)});let h=n.slice();return h[r]=p[1],{outputValues:c.values,outputShape:h,indices:o}}au("cpu",()=>new f2,1);var M6=ct(ui,e=>e>=0?e:Math.exp(e)-1),OU={kernelName:ui,backendName:"cpu",kernelFunc:M6};function F6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a;Ne([r],"leakyRelu");let i=w.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,l=w.getTypedArrayFromDType("float32",i);for(let d=0;d<o.length;d++)l[d]=o[d]<0?s*o[d]:o[d];return n.makeTensorInfo(r.shape,"float32",l)}var _U={kernelName:gi,backendName:"cpu",kernelFunc:F6},PU=Xt((e,t)=>e<0?t*e:e);function D6(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t;Ne([a,r],"prelu");let s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,[o,l]=PU(a.shape,r.shape,s,i,"float32");return n.makeTensorInfo(l,"float32",o)}var zU={kernelName:Ei,backendName:"cpu",kernelFunc:D6},$6=ct(Ri,e=>Math.max(0,e)),LU={kernelName:Ri,backendName:"cpu",kernelFunc:$6},O6=ct(Fi,e=>Math.min(Math.max(0,e),6)),WU={kernelName:Fi,backendName:"cpu",kernelFunc:O6};function k2(e,t,n,a,r){if(n==="linear")return Tr({inputs:{x:t},backend:e});if(n==="relu")return $6({inputs:{x:t},backend:e});if(n==="elu")return M6({inputs:{x:t},backend:e});if(n==="relu6")return O6({inputs:{x:t},backend:e});if(n==="prelu")return D6({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return F6({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return A6({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function wt(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(s,i),l=w.sizeFromShape(o);w.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. 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dt=0;for(let at=Ee;at<Ue;at++){let xt=Math.min(be,g-1)*q,ft=Math.min(be,y-1)*J,It=T[xt+Xe*z+at*X],Mt=P[at*Z+ut*te+ft];dt+=It*Mt}re[be*ie+(Xe*O+ut)]+=dt}}return n.disposeIntermediateTensorInfo(k),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(x,Y.dtype,Y.values)}var VU={kernelName:Qs,backendName:"cpu",kernelFunc:_6};function UU(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a,c,h,f,m=[];c=_6({inputs:{a:r,b:s},attrs:{transposeA:l,transposeB:d},backend:n}),i&&(h=Ep({inputs:{a:c,b:i},backend:n}),m.push(c),c=h),u&&(f=k2(n,c,u,o,p),m.push(c),c=f);for(let g of m)n.disposeIntermediateTensorInfo(g);return c}var jU={kernelName:Hi,backendName:"cpu",kernelFunc:UU},GU=ct(Zo,e=>Math.acos(e)),HU={kernelName:Zo,backendName:"cpu",kernelFunc:GU},qU=ct(Yo,e=>Math.acosh(e)),XU={kernelName:Yo,backendName:"cpu",kernelFunc:qU};function KU(e){let{inputs:t,backend:n}=e,a=t;Ne(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Ge(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let l=r[o];for(let d=0;d<i.length;d++)i[d]+=l[d]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var ZU={kernelName:Zs,backendName:"cpu",kernelFunc:KU};function YU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ne(r,"all");let o=w.parseAxisParam(s,r.shape),l=o,d=R.getAxesPermutation(l,r.shape.length),u=r;d!=null&&(u=Na({inputs:{x:r},backend:n,attrs:{perm:d}}),l=R.getInnerMostAxes(l.length,r.shape.length)),R.assertAxesAreInnerMostDims("all",l,u.shape.length);let[p,c]=R.computeOutAndReduceShapes(u.shape,l),h=w.sizeFromShape(c),f=w.makeZerosTypedArray(w.sizeFromShape(p),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let A=y*h,x=m[A];for(let b=0;b<h;++b){let v=m[A+b];x=x&&v}f[y]=x}d!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(p,o),A=wt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var JU={kernelName:Jo,backendName:"cpu",kernelFunc:YU};function QU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;Ne(r,"any");let o=w.parseAxisParam(s,r.shape),l=o,d=R.getAxesPermutation(l,r.shape.length),u=r;d!=null&&(u=Na({inputs:{x:r},backend:n,attrs:{perm:d}}),l=R.getInnerMostAxes(l.length,r.shape.length)),R.assertAxesAreInnerMostDims("any",l,u.shape.length);let[p,c]=R.computeOutAndReduceShapes(u.shape,l),h=w.sizeFromShape(c),f=w.makeZerosTypedArray(w.sizeFromShape(p),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let A=y*h,x=m[A];for(let b=0;b<h;++b){let v=m[A+b];x=x||v}f[y]=x}d!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(p,u.dtype,f);if(i){let y=R.expandShapeToKeepDim(p,o),A=wt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var ej={kernelName:Qo,backendName:"cpu",kernelFunc:QU};function tj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;Ne(r,"argMax");let i=w.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=Na({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,p]=R.computeOutAndReduceShapes(l.shape,i),c=w.sizeFromShape(u),h=w.makeZerosTypedArray(c,"int32"),f=w.sizeFromShape(p),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let v=m[y+b];v>A&&(A=v,x=b)}h[g]=x}return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var nj={kernelName:Ys,backendName:"cpu",kernelFunc:tj};function aj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;Ne(r,"argMin");let i=w.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=Na({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,p]=R.computeOutAndReduceShapes(l.shape,i),c=w.sizeFromShape(u),h=w.makeZerosTypedArray(c,"int32"),f=w.sizeFromShape(p),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let v=m[y+b];v<A&&(A=v,x=b)}h[g]=x}return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var rj={kernelName:yd,backendName:"cpu",kernelFunc:aj},sj=ct(el,e=>Math.asin(e)),ij={kernelName:el,backendName:"cpu",kernelFunc:sj},oj=ct(tl,e=>Math.asinh(e)),lj={kernelName:tl,backendName:"cpu",kernelFunc:oj},uj=ct(nl,e=>Math.atan(e)),dj={kernelName:nl,backendName:"cpu",kernelFunc:uj},pj=Xt((e,t)=>Math.atan2(e,t)),cj=mn(rl,pj),hj={kernelName:rl,backendName:"cpu",kernelFunc:cj},fj=ct(al,e=>Math.atanh(e)),mj={kernelName:al,backendName:"cpu",kernelFunc:fj};function I2(e,t,n,a,r,s){let i=r.strideHeight,o=r.strideWidth,l=r.dilationHeight,d=r.dilationWidth,u=r.effectiveFilterHeight,p=r.effectiveFilterWidth,c=r.padInfo.top,h=r.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ge(r.outShape,n),g=m.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],A=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;b<r.batchSize;++b){let v=b*y,k=b*a[0];for(let N=0;N<r.inChannels;++N)for(let C=0;C<r.outHeight;++C){let 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j=_-N,q=m.get(g,T,_,y);q>O&&(O=q,r?D=s?((g*a.inHeight+T)*a.inWidth+_)*a.inChannels+y:(T*a.inWidth+_)*a.inChannels+y:D=P*c+j)}}i.set(D,g,A,k,y)}}return i}function z6(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,d=r.dilationDepth,u=r.dilationHeight,p=r.dilationWidth,c=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,A=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Ge(r.outShape,n),b=x.values,v=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],N=r.outShape[3]*r.outShape[4],C=r.outShape[4];for(let E=0;E<r.batchSize;++E){let O=E*v,D=E*a[0];for(let T=0;T<r.inChannels;++T)for(let P=0;P<r.outDepth;++P){let _=P*i-m,j=_;for(;j<0;)j+=d;let q=Math.min(r.inDepth,c+_),z=O+P*k;for(let X=0;X<r.outHeight;++X){let Z=X*o-g,te=Z;for(;te<0;)te+=u;let J=Math.min(r.inHeight,h+Z),ie=z+X*N;for(let Y=0;Y<r.outWidth;++Y){let 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u=R.computePool3DInfo(s.shape,i,o,1,l,d),p=u.strideDepth,c=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,A=u.dilationHeight,x=u.dilationWidth,b=u.effectiveFilterDepth,v=u.effectiveFilterHeight,k=u.effectiveFilterWidth,N=b-1-u.padInfo.front,C=k-1-u.padInfo.left,E=v-1-u.padInfo.top,O=Ge(s.shape,"float32"),D=1/(f*m*g),T=n.bufferSync(r);for(let P=0;P<u.batchSize;++P)for(let _=0;_<u.inChannels;++_)for(let j=0;j<u.inDepth;++j)for(let q=0;q<u.inHeight;++q)for(let z=0;z<u.inWidth;++z){let X=j-N,Z=q-E,te=z-C,J=0;for(let ie=0;ie<b;ie+=y){let Y=(X+ie)/p;if(!(Y<0||Y>=u.outDepth||Math.floor(Y)!==Y))for(let re=0;re<v;re+=A){let de=(Z+re)/c;if(!(de<0||de>=u.outHeight||Math.floor(de)!==de))for(let be=0;be<k;be+=x){let ve=(te+be)/h;ve<0||ve>=u.outWidth||Math.floor(ve)!==ve||(J+=T.get(P,Y,de,ve,_))}}}O.set(J*D,P,j,q,z,_)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var wj={kernelName:Bc,backendName:"cpu",kernelFunc:vj};function kj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Ne([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:d}=a,u=R.computePool2DInfo(i.shape,o,l,1,d),p=u.strideHeight,c=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,A=u.effectiveFilterWidth,x=A-1-u.padInfo.left,b=y-1-u.padInfo.top,v=Ge(i.shape,"float32"),k=1/(h*f),N=n.data.get(r.dataId).values,C=Ge(r.shape,"float32",N);for(let E=0;E<u.batchSize;++E)for(let O=0;O<u.inChannels;++O)for(let D=0;D<u.inHeight;++D)for(let T=0;T<u.inWidth;++T){let P=D-b,_=T-x,j=0;for(let q=0;q<y;q+=m){let z=(P+q)/p;if(!(z<0||z>=u.outHeight||Math.floor(z)!==z))for(let X=0;X<A;X+=g){let Z=(_+X)/c;Z<0||Z>=u.outWidth||Math.floor(Z)!==Z||(j+=C.get(E,z,Z,O))}}v.set(j*k,E,D,T,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var Ij={kernelName:Wc,backendName:"cpu",kernelFunc:kj};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var Sj={kernelName:hi,backendName:"cpu",kernelFunc:Tj};function Nj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;Ne([r],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=R.getReshaped(r.shape,s,o),d=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(u,i,s.length),h=wt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Na({inputs:{x:h},backend:n,attrs:{perm:d}}),m=wt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=wo({inputs:{x:m},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Cj={kernelName:sl,backendName:"cpu",kernelFunc:Nj};function Ej(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=g2(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}var Rj={kernelName:Vc,backendName:"cpu",kernelFunc:Ej};function Mj(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=R.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Fj={kernelName:Uc,backendName:"cpu",kernelFunc:Mj},Dj=ct(us,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),$j={kernelName:us,backendName:"cpu",kernelFunc:Dj},Oj=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let d=0;d<o.length;d++){let u=o[d],p=l[d];a[d]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},_j={kernelName:xd,backendName:"cpu",kernelFunc:Oj};function 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Wj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:d,filterShape:u}=a;Ne([r,s],"conv2dBackpropFilter");let p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,u,i,1,o,d,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=c,y=c.dataFormat==="channelsLast",A=new Jt(c.filterShape,"float32"),x=c.padInfo.left,b=c.padInfo.top,v=n.data.get(r.dataId).values,k=n.data.get(s.dataId).values,N=new Jt(r.shape,r.dtype,v),C=new Jt(s.shape,s.dtype,k);for(let E=0;E<m;++E){let O=Math.max(0,Math.ceil((b-E)/h)),D=Math.min(c.outHeight,(c.inHeight+b-E)/h);for(let T=0;T<g;++T){let P=Math.max(0,Math.ceil((x-T)/f)),_=Math.min(c.outWidth,(c.inWidth+x-T)/f);for(let j=0;j<c.inChannels;++j)for(let q=0;q<c.outChannels;++q){let z=0;for(let X=0;X<c.batchSize;++X)for(let Z=O;Z<D;++Z){let te=E+Z*h-b;for(let J=P;J<_;++J){let ie=T+J*f-x;y?z+=N.get(X,te,ie,j)*C.get(X,Z,J,q):z+=N.get(X,j,te,ie)*C.get(X,q,Z,J)}}A.set(z,E,T,j,q)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var Bj={kernelName:Gc,backendName:"cpu",kernelFunc:Wj};function Vj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:d,dimRoundingMode:u}=a;Ne([r,s],"conv2dBackpropInput");let p=w.computeStrides(s.shape),c=w.computeStrides(r.shape),h=R.convertConv2DDataFormat(d),f=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),m=new Jt(f.inShape,"float32"),g=m.values,y=n.data.get(r.dataId).values,A=n.data.get(s.dataId).values,[x,b,v]=p,{batchSize:k,filterHeight:N,filterWidth:C,inChannels:E,inHeight:O,inWidth:D,outChannels:T,outHeight:P,outWidth:_,strideHeight:j,strideWidth:q}=f;h=f.dataFormat;let z=N-1-f.padInfo.top,X=C-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],J=Z?m.strides[1]:m.strides[2],ie=Z?m.strides[2]:1,Y=Z?1:m.strides[1],re=c[0],de=Z?c[1]:c[2],be=Z?c[2]:1,ve=Z?1:c[1];for(let ke=0;ke<k;++ke)for(let Ee=0;Ee<E;++Ee)for(let _e=0;_e<O;++_e){let 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dG={kernelName:Kc,backendName:"cpu",kernelFunc:uG};function pG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,inputShape:u}=a;Ne([r,s],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),c=w.computeStrides(s.shape),h=R.computeConv2DInfo(u,s.shape,i,o,l,d,!0),f=new Jt(h.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=n.data.get(r.dataId).values,[b,v,k]=p,N=n.data.get(s.dataId).values,[C,E,O]=c,{batchSize:D,filterHeight:T,filterWidth:P,inChannels:_,inHeight:j,inWidth:q,outChannels:z,outHeight:X,outWidth:Z,strideHeight:te,strideWidth:J}=h,ie=T-1-h.padInfo.top,Y=P-1-h.padInfo.left,re=z/_;for(let de=0;de<D;++de)for(let be=0;be<_;++be)for(let ve=0;ve<j;++ve){let ke=ve-ie,Ee=Math.max(0,Math.ceil(ke/te)),_e=Math.min(X,(T+ke)/te);for(let We=0;We<q;++We){let Ue=We-Y,Xe=Math.max(0,Math.ceil(Ue/J)),ut=Math.min(Z,(P+Ue)/J),dt=0;for(let at=Ee;at<_e;++at){let xt=at*te-ke;for(let ft=Xe;ft<ut;++ft){let 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|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
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|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${i.shape}`);let o=n.data.get(a.dataId).values,l=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,u=n.data.get(i.dataId).values[0],[p,c,h,f,m]=x6(o,a.shape,a.dtype,l,r.dtype,d,u);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],a.dtype,new Int32Array(m))]}var qq={kernelName:fh,backendName:"cpu",kernelFunc:Hq};function Xq(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.data.get(r.dataId).values),o=n.data.get(a.dataId).values,l=Array.from(n.data.get(s.dataId).values),[d,u,p]=b6(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(u,a.dtype,d),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var Kq={kernelName:mh,backendName:"cpu",kernelFunc:Xq};function Zq(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${s.shape}`);let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,[d,u]=v2(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(u,a.dtype,d)}var Yq={kernelName:gh,backendName:"cpu",kernelFunc:Zq};function Jq(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${s.shape}`);let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,[d,u]=v2(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(u,a.dtype,d)}var Qq={kernelName:yh,backendName:"cpu",kernelFunc:Jq};function eX(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:d,sliceSize:u,strides:p,outputSize:c}=R.calculateShapes(s,r,o),h=!1,f=n.bufferSync(r),m=n.bufferSync(s),g=n.data.get(i.dataId).values[0],y=q6(f,m,o,c,u,d,l,p,g,h);return n.makeTensorInfo(o,y.dtype,y.values)}var tX={kernelName:Ah,backendName:"cpu",kernelFunc:eX};function nX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=R.prepareSplitSize(r,s,o),d=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(p=>{let c=[...u];c[o]=p;let h=wo({inputs:{x:r},backend:n,attrs:{begin:d,size:c}});return d[o]+=p,h})}var 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a=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,a,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function JX(e,t){let n=R2(e,t),a=e.createTexture();e.bindTexture(e.TEXTURE_2D,a);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,a,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(a),e.deleteFramebuffer(i),o}function b4(e){return e!==2?!1:Sr(e).fenceSync!=null}function Mu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var $e=ne();$e.registerFlag("HAS_WEBGL",()=>$e.getNumber("WEBGL_VERSION")>0);$e.registerFlag("WEBGL_VERSION",()=>D2(2)?2:D2(1)?1:0);$e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);$e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>$e.get("WEBGL_VERSION")===2);$e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);$e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);$e.registerFlag("WEBGL_PACK",()=>$e.getBool("HAS_WEBGL"));$e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_CLIP",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_REDUCE",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_LAZILY_UNPACK",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_CONV_IM2COL",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>m4($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>g4($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=$e.getNumber("WEBGL_VERSION");return e===0?0:y4(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Wd.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>A4($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>x4($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>b4($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>$e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);$e.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});$e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Wd.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});$e.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);$e.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);$e.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);$e.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function $n(){let e,t,n,a,r,s,i,o,l,d;return ne().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,l="",d=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,d=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:d}}function So(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function gf(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / outShapeStrides[${s}]`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function QX(e,t){let n=e.length,a=e.map(s=>`${t}[${s}]`),r=new Array(n-1);r[n-2]=a[n-1];for(let s=n-3;s>=0;--s)r[s]=`(${r[s+1]} * ${a[s+1]})`;return r}function eK(e,t,n="index"){let a=e.map((s,i)=>i),r=QX(a,t);return r.map((s,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,l=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${l};`}).join("")}function O2(e){let t=w.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function _2(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var v4=`
|
|
const float FLOAT_MAX = 1.70141184e38;
|
|
const float FLOAT_MIN = 1.17549435e-38;
|
|
|
|
lowp vec4 encode_float(highp float v) {
|
|
if (isnan(v)) {
|
|
return vec4(255, 255, 255, 255);
|
|
}
|
|
|
|
highp float av = abs(v);
|
|
|
|
if(av < FLOAT_MIN) {
|
|
return vec4(0.0, 0.0, 0.0, 0.0);
|
|
} else if(v > FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
|
|
} else if(v < -FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
|
|
}
|
|
|
|
highp vec4 c = vec4(0,0,0,0);
|
|
|
|
highp float e = floor(log2(av));
|
|
highp float m = exp2(fract(log2(av))) - 1.0;
|
|
|
|
c[2] = floor(128.0 * m);
|
|
m -= c[2] / 128.0;
|
|
c[1] = floor(32768.0 * m);
|
|
m -= c[1] / 32768.0;
|
|
c[0] = floor(8388608.0 * m);
|
|
|
|
highp float ebias = e + 127.0;
|
|
c[3] = floor(ebias / 2.0);
|
|
ebias -= c[3] * 2.0;
|
|
c[2] += floor(ebias) * 128.0;
|
|
|
|
c[3] += 128.0 * step(0.0, -v);
|
|
|
|
return c / 255.0;
|
|
}
|
|
`,{getBroadcastDims:w4}=R;function tK(e,t,n){let a=[];if(e.forEach(c=>{let h=w.sizeFromShape(c.shapeInfo.logicalShape);if(c.shapeInfo.isUniform?a.push(`uniform float ${c.name}${h>1?`[${h}]`:""};`):(a.push(`uniform sampler2D ${c.name};`),a.push(`uniform int offset${c.name};`)),n.enableShapeUniforms){let{uniformShape:f}=P2(n.packedInputs,c.shapeInfo.logicalShape,c.shapeInfo.texShape);switch(f.length){case 1:a.push(`uniform int ${c.name}Shape;`);break;case 2:a.push(`uniform ivec2 ${c.name}Shape;`);break;case 3:a.push(`uniform ivec3 ${c.name}Shape;`);break;case 4:a.push(`uniform ivec4 ${c.name}Shape;`);break;default:break}a.push(`uniform ivec2 ${c.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:a.push("uniform int outShape;");break;case 2:a.push("uniform ivec2 outShape;"),a.push("uniform int outShapeStrides;");break;case 3:a.push("uniform ivec3 outShape;"),a.push("uniform ivec2 outShapeStrides;");break;case 4:a.push("uniform ivec4 outShape;"),a.push("uniform ivec3 outShapeStrides;");break;default:break}a.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(c=>{a.push(`uniform ${c.type} ${c.name}${c.arrayIndex?`[${c.arrayIndex}]`:""};`)});let r=a.join(`
|
|
`),s=e.map(c=>nK(c,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),i=t.texShape,o=$n(),l=sK(o),d,u,p=lK(o);return t.isPacked?(d=aK(t.logicalShape,i,n.enableShapeUniforms),u=oK(o)):(d=rK(t.logicalShape,i,n.enableShapeUniforms),u=iK(o)),n.packedInputs&&(p+=cK),[p,l,u,r,d,s,n.userCode].join(`
|
|
`)}function Fu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return IK(e,t);case 1:return SK(e,t);case 2:return CK(e,t);case 3:return RK(e,t);case 4:return FK(e,t);case 5:return DK(e);case 6:return $K(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function k4(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return kK(e);case 1:return TK(e,t);case 2:return NK(e,t);case 3:return EK(e,t);default:return MK(e,t)}}function nK(e,t,n=!1,a){let r="";n?r+=k4(e,a):r+=Fu(e,a);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(n?r+=OK(e,t):r+=_K(e,t)),r}function aK(e,t,n){switch(e.length){case 0:return I4();case 1:return hK(e,t,n);case 2:return vK(e,t,n);case 3:return mK(e,t,n);default:return yK(e,t,n)}}function rK(e,t,n){switch(e.length){case 0:return I4();case 1:return fK(e,t,n);case 2:return wK(e,t,n);case 3:return gK(e,t,n);case 4:return AK(e,t,n);case 5:return xK(e,t);case 6:return bK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function sK(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function iK(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function oK(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function lK(e){return`${e.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${e.varyingFs} vec2 resultUV;
|
|
${e.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${e.defineSpecialNaN}
|
|
${e.defineSpecialInf}
|
|
${e.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${uK}
|
|
${dK}
|
|
${pK}
|
|
`}var uK=`
|
|
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);
|
|
}
|
|
`,dK=`
|
|
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);
|
|
}
|
|
`,pK=`
|
|
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);
|
|
}
|
|
`,cK=`
|
|
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 I4(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function hK(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return a[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${a[1]}.0);
|
|
}
|
|
`:a[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${a[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${a[0]}, ${a[1]}));
|
|
return 2 * (resTexRC.x * ${a[1]} + resTexRC.y);
|
|
}
|
|
`}function fK(e,t,n){return t[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function mK(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),s=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${a[0]}, ${a[1]}));
|
|
int index = resTexRC.x * ${a[1]} + resTexRC.y;
|
|
|
|
int b = index / ${s};
|
|
index -= b * ${s};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function gK(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${gf(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let a=So(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${a}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function yK(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),s=r*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let d=2;d<e.length-1;d++)i*=e[e.length-d-1],o=`
|
|
int b${d} = index / ${i};
|
|
index -= b${d} * ${i};
|
|
`+o,l=`b${d}, `+l;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${a[0]}, ${a[1]}));
|
|
int index = resTexRC.x * ${a[1]} + resTexRC.y;
|
|
|
|
${o}
|
|
|
|
int b = index / ${s};
|
|
index -= b * ${s};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec${e.length}(${l});
|
|
}
|
|
`}function AK(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${gf(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let a=So(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${a}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function xK(e,t){let n=So(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function bK(e,t){let n=So(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function vK(e,t,n){let a=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${a[0]}, ${a[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${a[0]}, ${a[1]}));
|
|
|
|
int index = resTexRC.x * ${a[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function wK(e,t,n){return w.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function No(e){return`offset${e}`}function kK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=$n();return`
|
|
vec4 ${n}() {
|
|
return ${a.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function IK(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${a}() {return ${n};}`;let[r,s]=e.shapeInfo.texShape;if(r===1&&s===1)return`
|
|
float ${a}() {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=No(n);if(t)return`
|
|
float ${a}() {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let[o,l]=e.shapeInfo.texShape;return`
|
|
float ${a}() {
|
|
vec2 uv = uvFromFlat(${o}, ${l}, ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function TK(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=$n();if(t)return`
|
|
vec4 ${a}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${s.texture2D}(${n}, uv);
|
|
}
|
|
`;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${a}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${i[0]}, ${i[1]}, index);
|
|
return ${s.texture2D}(${n}, uv);
|
|
}
|
|
`}function SK(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int index) {
|
|
${Du(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,s=r[0],i=r[1];if(i===1&&s===1)return`
|
|
float ${a}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let o=No(n);return i===1?t?`
|
|
float ${a}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:s===1?t?`
|
|
float ${a}(int index) {
|
|
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int index) {
|
|
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${a}(int index) {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int index) {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function NK(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=$n();if(s!=null&&w.arraysEqual(n,s))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${a}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
|
|
|
|
return ${l.texture2D}(${a}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${a}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${a}, uv);
|
|
}
|
|
`;let d=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],u=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${d[0]}, ${d[1]}, row, col);
|
|
return ${l.texture2D}(${a}, uv);
|
|
}
|
|
`}function CK(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(n,s)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;let c=s[0],h=s[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}let{newShape:i,keptDims:o}=w.squeezeShape(n),l=i;if(l.length<n.length){let c=$u(e,l),h=["row","col"];return`
|
|
${Fu(c,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${Ou(h,o)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${Du(e)}
|
|
}
|
|
`;let d=s[0],u=s[1],p=No(a);return u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${a}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${a}TexShape[0]));
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${d}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:d===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${a}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${a}TexShape[1]), 0.5);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a}Shape[1] + col + ${p};
|
|
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n[1]} + col + ${p};
|
|
vec2 uv = uvFromFlat(${d}, ${u}, index);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function EK(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(n[0]===1){let c=n.slice(1),h=[1,2],f=$u(e,c),m=["b","row","col"];return`
|
|
${k4(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${Ou(m,h)});
|
|
}
|
|
`}let o=$n();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${a}TexShape[0]) / 2.0), ceil(float(${a}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${a}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${a}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${o.texture2D}(${a}, uv);
|
|
}
|
|
`;let l=i[0],d=i[1],u=Math.ceil(n[2]/2),p=u*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${d}, ${p}, ${u}, b, row, col);
|
|
return ${o.texture2D}(${a}, uv);
|
|
}
|
|
`}function RK(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:o,keptDims:l}=w.squeezeShape(n),d=o;if(d.length<n.length){let m=$u(e,d),g=["row","col","depth"];return`
|
|
${Fu(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${Ou(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${s}, ${i}, 1)));
|
|
${Du(e)}
|
|
}
|
|
`;let u=e.shapeInfo.texShape,p=u[0],c=u[1],h=e.shapeInfo.flatOffset;if(c===s&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${a}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}TexShape[1], ${a}TexShape[0]);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${i}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;if(c===i&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${a}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}TexShape[1], ${a}TexShape[0]);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;let f=No(a);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${a}Shape[1] * ${a}Shape[2];
|
|
int stride1 = ${a}Shape[2];
|
|
int index = row * ${s} + col * ${i} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s} + col * ${i} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${p}, ${c}, index);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function MK(e,t){let n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=$n();if(t)return`
|
|
vec4 ${a}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${n}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],d=l[0],u=l[1],p=Math.ceil(s[i-1]/2),c=p*Math.ceil(s[i-2]/2),h="int b, int row, int col",f=`b * ${c} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,c*=s[i-m-1],f=`b${m} * ${c} + `+f;return`
|
|
vec4 ${a}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${u};
|
|
int texC = index - texR * ${u};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${d});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function FK(e,t){let n=e.shapeInfo.logicalShape,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[3],i=n[2]*s,o=n[1]*i,{newShape:l,keptDims:d}=w.squeezeShape(n);if(l.length<n.length){let A=$u(e,l),x=["row","col","depth","depth2"];return`
|
|
${Fu(A,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${Ou(x,d)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, 1)));
|
|
${Du(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1],f=`int stride2 = ${a}Shape[3];`,m=`int stride1 = ${a}Shape[2] * stride2;`,g=`int stride0 = ${a}Shape[1] * stride1;`;if(h===o&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}TexShape[1], ${a}TexShape[0]);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${i}, ${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;if(h===s&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${a}Shape[1] * ${a}Shape[2], ${a}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}TexShape[1], ${a}TexShape[0]);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n[1]*n[2]}, ${n[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`;let y=No(a);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${a}TexShape[0], ${a}TexShape[1], index + ${y});
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} +
|
|
depth * ${s} + depth2;
|
|
vec2 uv = uvFromFlat(${c}, ${h}, index + ${y});
|
|
return sampleTexture(${a}, uv);
|
|
}
|
|
`}function DK(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:d}=w.squeezeShape(t);if(l.length<t.length){let m=$u(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${Fu(m)}
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${a}(${Ou(g,d)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${r})) +
|
|
depth3;
|
|
${Du(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&u==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&u==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=No(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} + depth * ${s} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${c}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function $K(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=w.squeezeShape(t);if(r.length<t.length){let g=$u(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Fu(g)}
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${a}(${Ou(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,l=t[3]*o,d=t[2]*l,u=t[1]*d;if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${d}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Du(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],f=c[1];if(f===u&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${d}, ${l}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===i&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=No(n);return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${u} + col * ${d} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Du(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function OK(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=w4(e.shapeInfo.logicalShape,t.logicalShape),l=yt(i),d=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+d]} = 0;`).join(`
|
|
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+d]}`).join(", ");let h="return outputValue;",f=w.sizeFromShape(e.shapeInfo.logicalShape)===1,m=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(f&&!m)i===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${a}(${c});
|
|
${h}
|
|
}
|
|
`}function _K(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let d=yt(l),u=w4(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,c,h=["x","y","z","w","u","v"];o===0?c="":l<2&&u.length>=1?c="coords = 0;":c=u.map(m=>`coords.${h[m+p]} = 0;`).join(`
|
|
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
|
|
float ${r}() {
|
|
${d} coords = getOutputCoords();
|
|
${c}
|
|
return get${a}(${f});
|
|
}
|
|
`}function yt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function P2(e,t,n){let{newShape:a,keptDims:r}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):a,l=!e&&s>1&&!w.arraysEqual(t,n)&&a.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:r}}function $u(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Ou(e,t){return t.map(n=>e[n]).join(", ")}function PK(e,t,n,a){let r=n.map((x,b)=>{let v={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(v.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:v}}),s=r.map(x=>x.shapeInfo),i={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},o=tK(r,i,t),l=e.createProgram(o),d=null,u=e.getUniformLocation(l,"NAN",!1);ne().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(l,"INFINITY",!1));let p=!1,c={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];c[b]=e.getUniformLocation(l,b,p),c[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,p),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,p),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,p))}let m,g,y;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",p),y=e.getUniformLocation(l,"outShapeStrides",p),g=e.getUniformLocation(l,"outTexShape",p));let A=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{A[b]=e.getUniformLocation(l,x.name,p)}),{program:t,source:o,webGLProgram:l,uniformLocations:c,customUniformLocations:A,inShapeInfos:s,outShapeInfo:i,infLoc:d,nanLoc:u,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:y,outTexShapeLocation:g}}function T4(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function zK(e,t,n,a,r){t.program.enableShapeUniforms||(T4(t.inShapeInfos,n),T4([t.outShapeInfo],[a]));let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),ne().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,d)=>{let u=t.program.variableNames[d],p=t.uniformLocations[u],c=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=P2(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(p,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}l.texData.slice!=null&&c!=null&&e.gl.uniform1i(c,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,p,d)}});let o=t.outShapeLocation;if(o)switch(a.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(a.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(a.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(a.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(a.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(a.shape);switch(a.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,a.texData.texShape[0],a.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,d)=>{let u=t.customUniformLocations[d],p=r[d];if(l.type==="float")e.gl.uniform1fv(u,p);else if(l.type==="vec2")e.gl.uniform2fv(u,p);else if(l.type==="vec3")e.gl.uniform3fv(u,p);else if(l.type==="vec4")e.gl.uniform4fv(u,p);else if(l.type==="int")e.gl.uniform1iv(u,p);else if(l.type==="ivec2")e.gl.uniform2iv(u,p);else if(l.type==="ivec3")e.gl.uniform3iv(u,p);else if(l.type==="ivec4")e.gl.uniform4iv(u,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function LK(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:d,uniformShape:u,keptDims:p}=P2(e.packedInputs,i.shape,l),c="",h="",f="";if(u.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];c=`${v[0]>1}_${v[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let v=w.computeStrides(u);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=u.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=R.getBroadcastDims(i.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;a+=`${m}_${x}_${d?p:""}_${u.length}_${y}_${A}_${g}_${c}_${h}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${l}_${o}`}});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r+`${ne().getNumber("WEBGL_VERSION")}`,s}function Ra(e){return ne().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var WK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Dp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$n();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?gf(["r","c","d"],e):So(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},BK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Dp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$n();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?gf(["r","c","d"],e):So(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},VK=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Ca.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
|
|
${v4}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},UK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ca.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
|
|
${v4}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},jK=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=$n();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?_2():O2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
vec4 values = ${n.texture2D}(A, uv);
|
|
|
|
float result;
|
|
|
|
if(offset == 0) {
|
|
result = values[0];
|
|
} else if(offset == 1) {
|
|
result = values[1];
|
|
} else if(offset == 2) {
|
|
result = values[2];
|
|
} else {
|
|
result = values[3];
|
|
}
|
|
|
|
${n.output} = vec4(${a}, 0., 0., 0.);
|
|
}
|
|
`}},GK=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=$n();this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let a="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;a+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
|
|
localCoords[2] += ${i};
|
|
if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
|
|
localCoords[1] += ${s};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${o}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${o}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${o}] = values[2];
|
|
} else {
|
|
result[${o}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?_2():O2(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${a}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},S4={};ze(S4,{bindVertexProgramAttributeStreams:()=>O4,createBufferFromOutputTexture:()=>z4,createFloat16MatrixTexture:()=>M4,createFloat16PackedMatrixTexture:()=>$4,createFloat32MatrixTexture:()=>R4,createIndexBuffer:()=>E4,createPackedMatrixTexture:()=>D4,createUnsignedBytesMatrixTexture:()=>F4,createVertexBuffer:()=>C4,createVertexShader:()=>N4,downloadByteEncodedFloatMatrixFromOutputTexture:()=>W4,downloadFloat32MatrixFromBuffer:()=>L4,downloadMatrixFromPackedOutputTexture:()=>V4,downloadPackedMatrixFromBuffer:()=>B4,getInternalFormatForFloat16MatrixTexture:()=>L2,getInternalFormatForFloat16PackedMatrixTexture:()=>V2,getInternalFormatForFloat32MatrixTexture:()=>z2,getInternalFormatForPackedMatrixTexture:()=>B2,getInternalFormatForUnsignedBytesMatrixTexture:()=>W2,uploadDenseMatrixToTexture:()=>_4,uploadPixelDataToTexture:()=>P4});function N4(e){let t=$n(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return Q6(e,n)}function C4(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return a4(e,t)}function E4(e){let t=new Uint16Array([0,1,2,2,1,3]);return r4(e,t)}function zp(e,t,n,a,r,s){i4(t,n);let i=s4(e),o=e.TEXTURE_2D;return Te(e,()=>e.bindTexture(o,i)),Te(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Te(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Te(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function z2(e){return e.internalFormatFloat}function R4(e,t,n,a){let[r,s]=$p(t,n);return zp(e,r,s,z2(a),a.textureFormatFloat,e.FLOAT)}function L2(e){return e.internalFormatHalfFloat}function M4(e,t,n,a){let[r,s]=$p(t,n);return zp(e,r,s,L2(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function W2(e){return e.downloadTextureFormat}function F4(e,t,n,a){let[r,s]=$p(t,n);return zp(e,r,s,W2(a),e.RGBA,e.UNSIGNED_BYTE)}function B2(e){return e.internalFormatPackedFloat}function D4(e,t,n,a){let[r,s]=Ru(t,n);return zp(e,r,s,B2(a),e.RGBA,e.FLOAT)}function V2(e){return e.internalFormatPackedHalfFloat}function $4(e,t,n,a){let[r,s]=Ru(t,n);return zp(e,r,s,V2(a),e.RGBA,a.textureTypeHalfFloat)}function O4(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),M2(e,t,"clipSpacePos",n,3,s,a)&&M2(e,t,"uv",n,2,s,r)}function _4(e,t,n,a,r,s){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function P4(e,t,n){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function z4(e,t,n,a){let r=e.createBuffer();Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Te(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function L4(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function W4(e,t,n,a){let[r,s]=$p(t,n),i=4,o=new Uint8Array(WX(t*n,i));return Te(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function B4(e,t,n,a,r,s,i,o){let l=e,d=new Float32Array(BX(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,d),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),d}function V4(e,t,n){let a=new Float32Array(t*n*4);return Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var yf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ne().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,lf(t,e)):this.gl=Sr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ne().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Op(this.gl,r),Ea(this.gl,s))this.textureHalfFloatExtension=Op(this.gl,s);else if(ne().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),Ea(this.gl,a))this.colorBufferHalfFloatExtension=Op(this.gl,a);else if(ne().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",Ea(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ea(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=C4(this.gl),this.indexBuffer=E4(this.gl),this.framebuffer=o4(this.gl),this.textureConfig=R2(this.gl,this.textureHalfFloatExtension)}get debug(){return ne().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;Te(e,()=>e.finish()),Te(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Te(e,()=>e.deleteFramebuffer(this.framebuffer)),Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Te(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Te(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),R4(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),M4(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),F4(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),P4(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),_4(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),$4(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),D4(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(F2(this.gl,this.framebuffer),this.outputTexture=null),Te(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>W4(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return B4(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return L4(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=z4(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(ne().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>V4(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=e4(t,e);this.vertexShader==null&&(this.vertexShader=N4(t));let a=t4(t);return Te(t,()=>t.attachShader(a,this.vertexShader)),Te(t,()=>t.attachShader(a,n)),n4(t,a),this.debug&&df(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=O4(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Te(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&df(this.gl,this.program),Te(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?u4(this.gl,e,t):d4(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Te(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(),p4(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Ru(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&df(this.gl,this.program),_p(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Te(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Te(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Op(this.gl,ne().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(ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=HK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),pf(this.gl,e,this.framebuffer),this.debug&&_p(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(pf(this.gl,this.outputTexture,this.framebuffer),this.debug&&_p(this.gl)):F2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;pf(a,e,this.framebuffer),this.debug&&_p(a),this.outputTexture=e,Te(a,()=>a.viewport(0,0,t,n)),Te(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Te(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function HK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:qK,bincountImpl:U4,bincountReduceImpl:XK,ceilImpl:KK,concatImpl:ZK,equalImpl:YK,expImpl:JK,expm1Impl:QK,floorImpl:eZ,gatherNdImpl:tZ,gatherV2Impl:nZ,greaterImpl:aZ,greaterEqualImpl:rZ,lessImpl:sZ,lessEqualImpl:iZ,linSpaceImpl:oZ,logImpl:lZ,maxImpl:uZ,maximumImpl:dZ,minimumImpl:pZ,multiplyImpl:cZ,negImpl:hZ,notEqualImpl:fZ,prodImpl:mZ,rangeImpl:gZ,rsqrtImpl:yZ,sigmoidImpl:AZ,simpleAbsImpl:j4,sliceImpl:xZ,sparseFillEmptyRowsImpl:bZ,sparseReshapeImpl:vZ,sparseSegmentReductionImpl:G4,sqrtImpl:wZ,stridedSliceImpl:kZ,stringNGramsImpl:IZ,stringSplitImpl:TZ,stringToHashBucketFastImpl:SZ,subImpl:NZ,tileImpl:CZ,topKImpl:EZ,transposeImpl:U2,uniqueImpl:RZ}=Hw;function H4(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function On(e,t){return t===1?[e]:H4(e,t)}function MZ(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var FZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let n=On("rc",t),a=yt(t),r=$Z(t,e,n),s=OZ(t,e[e.length-1],e[e.length-2],n),i=_Z(e,n);this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function DZ(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function $Z(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function OZ(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
|
|
int r = ${r[0]};
|
|
int c = ${r[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function _Z(e,t){let n=e.length,a=DZ(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${a[0]}),
|
|
cEdge ? 0. : getA(${a[1]}),
|
|
rEdge ? 0. : getA(${a[2]}),
|
|
rEdge || cEdge ? 0. : getA(${a[3]})`}var q4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${a}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${a>0?"}":""}
|
|
`}this.userCode=`
|
|
${PZ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?_2():O2(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function PZ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?eK(["r","c","d"],"inputShape"):So(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var zZ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=K4(t,n),r=Z4(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=X4(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===bn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===bn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===bn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===bn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===bn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=K4(n,a),s=Z4(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=X4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=ne().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],d=l.indexOf(e);if(d<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(d,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function LZ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function X4(e,t,n,a,r){let s=WZ(t,a),i;if(r){let[l,d]=Ru(e[0],e[1]);i=l*d}else{let[l,d]=$p(e[0],e[1]);i=l*d}let o=LZ(n,s);return i*o}function WZ(e,t){switch(e){case bn.PACKED_2X2_FLOAT32:return B2(t);case bn.PACKED_2X2_FLOAT16:return V2(t);case bn.UNPACKED_FLOAT32:return z2(t);case bn.UNPACKED_FLOAT16:return L2(t);case bn.PACKED_4X1_UNSIGNED_BYTE:return W2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function BZ(e){return ne().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?bn.PACKED_2X2_FLOAT32:bn.UNPACKED_FLOAT32:e?bn.PACKED_2X2_FLOAT16:bn.UNPACKED_FLOAT16}function K4(e,t){if(e===Ca.UPLOAD)return bn.PACKED_2X2_FLOAT32;if(e===Ca.RENDER||e==null)return BZ(t);if(e===Ca.DOWNLOAD||e===Ca.PIXELS)return bn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Z4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Fs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},lr="if (isnan(x)) return x;",VZ="return x;",Y4="return abs(x);",UZ="return (x >= 0.0) ? x : (exp(x) - 1.0);",jZ=lr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,GZ=lr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Af="return x;",HZ="return 1.0 / (1.0 + exp(-1.0 * x));",qZ="return x;",XZ=`
|
|
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;
|
|
`,KZ=`
|
|
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;
|
|
`,ZZ=`
|
|
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;
|
|
`,YZ="return 1.0 / (1.0 + exp(-1.0 * x));",_u=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},JZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=On("rc",t),a=yt(t),r=MZ(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},QZ=xr.whereImpl,eY=1e-7,tY=1e-4,j2={};function nY(e){return e in j2||(j2[e]={}),j2[e]}var aY=ne().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),rY=600;function sY(){return ne().global.screen==null?1024:ne().global.screen.height*ne().global.screen.width*window.devicePixelRatio*rY/1024/1024}var Pu=class extends fd{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!ne().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Sr(ne().getNumber("WEBGL_VERSION"));this.binaryCache=nY(ne().getNumber("WEBGL_VERSION")),this.gpgpu=new yf(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 zZ(this.gpgpu),this.numMBBeforeWarning=sY(),this.texData=new Oc(this,sa())}nextDataId(){return Pu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((ne().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ne().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:Ca.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(ne().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:Ca.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new _u(i,Af):p=new Fs(i,Af);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let l=this.activeTimers!=null,d;l&&(d=w.now());let u;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);u=R.mergeRealAndImagArrays(p,c)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-d),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new _u(a,Af):h=new Fs(a,Af);let f=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!ne().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ne().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,d;if(s!=="complex64"&&ne().get("WEBGL_BUFFER_SUPPORTED")){d=this.decode(e);let h=this.texData.get(d.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...uf(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(d!=null&&this.disposeIntermediateTensorInfo(d),l!=null){let h=this.gpgpu.gl;Te(h,()=>h.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,u),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sa().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>w.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ge(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Y6(n))throw ne().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=w.sizeFromShape(t);if(ne().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...uf(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=ne().getBool("WEBGL_PACK")&&a===!0,i=s?cf(t):t,o=s?new UK(i):new VK(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),d=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,d)=>({name:s[d],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(ne().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let d=this.texData.get(e);d.texture=null,d.texShape=null,d.isPacked=!1,d.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=aY){return ne().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return QZ(e.shape,t)}packedUnaryOp(e,t,n){let a=new _u(e.shape,t),r=this.compileAndRun(a,[e],n);return sa().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=j4(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(ne().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Y4,e.dtype);let t=new Fs(e.shape,Y4),n=this.compileAndRun(t,[e]);return sa().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(s=>w.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return sa().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new JZ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new FZ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Io(e.shape),...To(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Io(t),...To(t)],s=new q4(r,n),i=!0,o=[n],l=this.runWebGLProgram(s,[a],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=cf(a),i,o=uf(s);n?i=new BK(s):i=new WK(s);let l=!0,d=[o],u=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,d,l);return{dtype:r,shape:a,dataId:u.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Dp.DENSE){let m=uf(e.outputShape);i.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(s.shape)===0)return i.values=w.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&w.sizeFromShape(m.shape)<=ne().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!Pp(g.shape,m.shape)){let y=m,A=m.shape;m.shape=g.shape,m=this.packedReshape(m,A),o.push(m),g=this.texData.get(m.dataId),y.shape=A}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(s.dataId);let d={shape:s.shape,texData:i,isUniform:!1},u=LK(e,l,d),p=this.getAndSaveBinary(u,()=>PK(this.gpgpu,e,l,d)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),zK(this.gpgpu,p,l,d,a),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=ne().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!ne().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(ne().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=G(()=>{if(!ne().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=ne().getBool("DEBUG");ne().set("DEBUG",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(ne().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?eY:tY}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,d;l&&(d=w.now());let u=t.texShape;if(u==null&&(u=f4(n,o),t.texShape=u),r!=null){let p=cf(n),c,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;o?([h,f]=Ru(u[0],u[1]),c=new GK(p,m)):c=new jK(p,m);let g=this.makeTensorInfo([f,h],a);m?this.texData.get(g.dataId).usage=Ca.PIXELS:this.texData.get(g.dataId).usage=Ca.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let y=[[f,h]],A=!0,x=this.runWebGLProgram(c,[g],a,y,A),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-d)}else{let p=this.acquireTexture(u,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=iY(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}};Pu.nextDataId=0;function iY(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var oY="3.10.0";function J4(){ne().set("WEBGL_FORCE_F16_TEXTURES",!0)}Wd.isBrowser()&&au("webgl",()=>new Pu,2);var lY={forceHalfFloat:J4},Q4=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,zu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Ra(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},xf=`
|
|
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;
|
|
`,Lp=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Ra(r);let s="";if(a)if(r===0||w.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${yt(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?s+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=On("coords",r);this.enableShapeUniforms?s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function ma(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var uY={kernelName:mi,backendName:"webgl",kernelFunc:ma};function Ds(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=ma({inputs:{x:a},backend:n}),l=ma({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var dY={kernelName:jc,backendName:"webgl",kernelFunc:Ds},ek="return (a < 0.) ? b * a : a;",tk=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function pY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=ne().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lp(tk,r.shape,i.shape):new zu(ek,r.shape,i.shape),l=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),l}var cY={kernelName:gi,backendName:"webgl",kernelFunc:pY},nk="return (a < 0.) ? b * a : a;",ak=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function hY(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=ne().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lp(ak,a.shape,r.shape):new zu(nk,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],"float32")}var fY={kernelName:Ei,backendName:"webgl",kernelFunc:hY},rk="if (isnan(x)) return x;",mY=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,gY=`
|
|
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 nt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,l=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,l);return o.makeTensorInfo(i.shape,l,c)}let d=ne().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return d?u=new _u(i.shape,t):u=new Fs(i.shape,e),o.runWebGLProgram(u,[i],l)}}function vn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:d}=i,u=o;if(a&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(d.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:v.dataId,dtype:v.dtype,shape:d.shape},C=new zu(e,l.shape,d.shape);return u.runWebGLProgram(C,[k,N],Pa(b.dtype,v.dtype))}),A=Ds({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),A}let p=s||Pa(l.dtype,d.dtype);if((l.dtype==="string"||d.dtype==="string"||u.shouldExecuteOnCPU([l,d]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(d.dataId).values,g=l.dtype==="string"?R.fromUint8ToStringArray(f):f,y=l.dtype==="string"?R.fromUint8ToStringArray(m):m,[A,x]=r(l.shape,d.shape,g,y,p),b=u.makeTensorInfo(x,p),v=u.texData.get(b.dataId);return v.values=A,b}let c=ne().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Lp(t,l.shape,d.shape,n):h=new zu(e,l.shape,d.shape),u.runWebGLProgram(h,[l,d],p)}}function bf(e,t=!1){if(e==="linear")return t?qZ:VZ;if(e==="relu")return t?KZ:jZ;if(e==="elu")return t?XZ:UZ;if(e==="relu6")return t?ZZ:GZ;if(e==="prelu")return t?ak:nk;if(e==="leakyrelu")return t?tk:ek;if(e==="sigmoid")return t?YZ:HZ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var sk=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Ra(this.outputShape.length);let d=a?e[1]:e[2],u=Math.ceil(d/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${u}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${u}; i++) {
|
|
int batchA = ${A};
|
|
int batchB = ${x};
|
|
vec4 a = getMatrixA(batchA, ${p});
|
|
vec4 b = getMatrixB(batchB, ${c});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${f[0]});
|
|
result += (${h[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},ik={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},ok=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},lk="return a * b;";function G2(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=R.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),d=new ok(ik.REAL,a.shape,r.shape),u=new ok(ik.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(d,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),f=Ds({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[d,u]=cZ(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),c=n.texData.get(p.dataId);return c.values=d,p}let i;return ne().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Lp(lk,a.shape,r.shape):i=new zu(lk,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var yY={kernelName:Ti,backendName:"webgl",kernelFunc:G2};function AY(e,t,n){let a=[Io(e.shape),...To(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[Io(t),...To(t)],i=new q4(s,a),o=!0,l=[a],d=n.runWebGLProgram(i,[r],e.dtype,l,o);return{dataId:d.dataId,shape:t,dtype:d.dtype}}function we(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),d=w.sizeFromShape(l);w.assert(o===d,()=>`The new shape (${l}) has ${d} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!Pp(r.shape,l)&&!(u.texture!==null&&Pp(u.shape,l))?AY(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var xY={kernelName:$l,backendName:"webgl",kernelFunc:we},uk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${w.isInt(u)?u.toPrecision(2):u}, ones);`}let d="";r%n>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},bY=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let d=Math.floor(n/4)*4,u=n%4,p=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,c="vec4";t==="all"?(i="1.0",p=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,c="bvec4"):t==="any"&&(i="0.0",p=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,c="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${d}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${d};
|
|
if (${u===1}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===2}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===3}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function vY(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Co(e,t,n,a){let r=vY(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:d}=r[i],u,p;n==="mean"?u=i===0?new uk({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d},o):new uk({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d}):u=new bY({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:d},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var wY=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=yt(this.rank),r=kY(t);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function kY(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var IY=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let d=0;d<n.length;d++)n[d]=e[t[d]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=yt(this.rank),r=H4("rc",this.rank),s=new Array(this.rank);for(let d=0;d<t.length;d++)s[t[d]]=r[d];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${o}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${o}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function vf(e,t,n){let a=ne().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IY(e.shape,t):new wY(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function TY(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=R.getAxesPermutation(o,s),d=l!=null,u=e;d&&(u=vf(e,l,a),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=R.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=R.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(c),m=w.sizeFromShape(e.shape)/f,g=we({inputs:{x:u},attrs:{shape:[m,f]},backend:a}),y=Eh(e.dtype),A=Co(g,y,"sum",a),x=we({inputs:{x:A},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(A),d&&a.disposeIntermediateTensorInfo(u),x}function wf(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return TY(r,s,i,n)}var SY={kernelName:Li,backendName:"webgl",kernelFunc:wf};function _n(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let d;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=U2(u,r.shape,r.dtype,s,l);d=i.makeTensorInfo(l,r.dtype);let c=i.texData.get(d.dataId);c.values=p}else d=vf(r,s,i);return d}var NY={kernelName:Gi,backendName:"webgl",kernelFunc:_n},dk=1e3;function kf({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let d=e.shape.length,u=t.shape.length,p=n?e.shape[d-2]:e.shape[d-1],c=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[d-1]:e.shape[d-2],f=a?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=y===A||y===1||A===1;w.assert(d>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let b=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);w.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let v=n?[y,p,h]:[y,h,p],k=a?[A,f,c]:[A,c,f],N=we({inputs:{x:e},backend:r,attrs:{shape:v}}),C=we({inputs:{x:t},backend:r,attrs:{shape:k}}),E=[N,C],O=Math.max(y,A),D=n?N.shape[1]:N.shape[2],T=s!=null,P=i!=null,_=l==="leakyrelu",j=l!=null?bf(l,!0):null,q=T||P||_||j!=null,z;if((h===1||f===1)&&D>dk&&q===!1){let Z=N,te=C;n&&(Z=_n({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z)),a&&(te=_n({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(te));let J=f!==1,ie=f===1,Y=Z;J&&(Y=we({inputs:{x:Z},backend:r,attrs:{shape:[O,D,1]}}),E.push(Y));let re=f===1?2:1,de=te;ie&&(de=we({inputs:{x:te},backend:r,attrs:{shape:[O,1,D]}}),E.push(de));let be=G2({inputs:{a:Y,b:de},backend:r});z=wf({inputs:{x:be},backend:r,attrs:{axis:re,keepDims:!0}}),E.push(be)}else{let Z=Pa(e.dtype,t.dtype),te=new sk(v,k,[O,h,f],n,a,T,j,P,_),J=[N,C];if(s!=null&&J.push(s),P&&J.push(i),_){let ie=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));J.push(ie),E.push(ie)}z=r.runWebGLProgram(te,J,Z)}let X=we({inputs:{x:z},backend:r,attrs:{shape:b}});E.push(z);for(let Z of E)r.disposeIntermediateTensorInfo(Z);return X}function CY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a;return kf({a:r,b:s,transposeA:l,transposeB:d,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var EY={kernelName:Hi,backendName:"webgl",kernelFunc:CY},pk="return abs(x);";function RY(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=j4(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ne().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new _u(a.shape,pk):r=new Fs(a.shape,pk),n.runWebGLProgram(r,[a],a.dtype)}var MY={kernelName:Ko,backendName:"webgl",kernelFunc:RY},FY=lr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,DY=nt({opSnippet:FY}),$Y={kernelName:Zo,backendName:"webgl",kernelFunc:DY},OY=lr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,_Y=nt({opSnippet:OY}),PY={kernelName:Yo,backendName:"webgl",kernelFunc:_Y},ck="return a + b;",zY=vn({opSnippet:ck,packedOpSnippet:ck,supportsComplex:!0,cpuKernelImpl:qK}),LY={kernelName:ls,backendName:"webgl",kernelFunc:zY},WY=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}},BY=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}};function If(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return ma({inputs:{x:a[0]},backend:n});if(a.length>ne().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=If({inputs:a.slice(0,o),backend:n}),d=If({inputs:a.slice(o),backend:n});return If({inputs:[l,d],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>Pa(o,l)),s=a.map(o=>o.shape),i=ne().getBool("WEBGL_PACK")?new BY(a[0].shape,s):new WY(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var VY={kernelName:Zs,backendName:"webgl",kernelFunc:If};function UY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),d=l,u=R.getAxesPermutation(d,o),p=r;u!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:u}}),d=R.getInnerMostAxes(d.length,o)),R.assertAxesAreInnerMostDims("all",d,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,d),f=w.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Co(m,m.dtype,"all",n),y;if(i){let A=R.expandShapeToKeepDim(c,l);y=we({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var jY={kernelName:Jo,backendName:"webgl",kernelFunc:UY};function GY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),d=l,u=R.getAxesPermutation(d,o),p=r;u!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:u}}),d=R.getInnerMostAxes(d.length,o)),R.assertAxesAreInnerMostDims("any",d,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,d),f=w.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Co(m,m.dtype,"any",n),y;if(i){let A=R.expandShapeToKeepDim(c,l);y=we({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var HY={kernelName:Qo,backendName:"webgl",kernelFunc:GY},qY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${a};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${a}; i++) {
|
|
int inIdx = ${o};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},XY=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=yt(o),d=On("coords",o),u,p;if(s===1){p=o+1;let N=yt(p);u=`
|
|
${N} sourceLocR = ${N}(${d.join()}, 0);
|
|
++${d[o-1]};
|
|
${N} sourceLocG = ${N}(${d.join()}, 0);
|
|
++${d[o-2]};
|
|
${N} sourceLocA = ${N}(${d.join()}, 0);
|
|
--${d[o-1]};
|
|
${N} sourceLocB = ${N}(${d.join()}, 0);
|
|
--${d[o-2]};`}else p=o,u=`
|
|
${l} sourceLocR = coords;
|
|
++${d[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${d[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${d[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${d[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],f=c.map(N=>"int "+N),m=On("sourceLocR",p-1).concat("inIdx.r"),g=On("sourceLocG",p-1).concat("inIdx.g"),y=On("sourceLocB",p-1).concat("inIdx.b"),A=On("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=a?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${A.join()})));`,v=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,k=a?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}
|
|
${k}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${d[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${d[o-2]} < ${i[o-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${v};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${v};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function hk(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new qY(o,n,a==null),d=[t];a!=null&&d.push(a);let u=e.runWebGLProgram(l,d,"int32");if(u.shape[1]===1)return u;let p=hk(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function fk(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=R.computeOptimalWindowSize(s),o=new XY(r,i,n,a==null),l=a==null?[t]:[t,a],d=e.runWebGLProgram(o,l,"int32");if(d.shape.length===t.shape.length){let u=fk(e,t,n,d);return e.disposeIntermediateTensorInfo(d),u}return d}function mk(e,t,n,a){let r=[n];if(R.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ne().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[d,u]=R.computeOutAndReduceShapes(l.shape,r),p=w.sizeFromShape(u),c=we({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});s.push(c);let h=hk(e,c,a);s.push(h);let f=we({inputs:{x:h},backend:e,attrs:{shape:d}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return fk(e,t,a)}function KY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=_n({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=mk(n,l,i[0],"max");return d.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var ZY={kernelName:Ys,backendName:"webgl",kernelFunc:KY};function YY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),l=r,d=[];o!=null&&(l=_n({inputs:{x:r},backend:n,attrs:{perm:o}}),d.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=mk(n,l,i[0],"min");return d.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var JY={kernelName:yd,backendName:"webgl",kernelFunc:YY},QY=lr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,eJ=nt({opSnippet:QY}),tJ={kernelName:el,backendName:"webgl",kernelFunc:eJ},nJ=lr+"return log(x + sqrt(x * x + 1.0));",aJ=nt({opSnippet:nJ}),rJ={kernelName:tl,backendName:"webgl",kernelFunc:aJ},sJ=lr+`
|
|
return atan(x);
|
|
`,iJ=nt({opSnippet:sJ}),oJ={kernelName:nl,backendName:"webgl",kernelFunc:iJ},lJ=mY+`
|
|
return atan(a, b);
|
|
`,uJ=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+gY+`
|
|
return result;
|
|
`,dJ=vn({opSnippet:lJ,packedOpSnippet:uJ}),pJ={kernelName:rl,backendName:"webgl",kernelFunc:dJ},cJ=lr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,hJ=nt({opSnippet:cJ}),fJ={kernelName:al,backendName:"webgl",kernelFunc:hJ},Wp=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,d=e.dilationWidth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${d}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${N} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?r?m:g:`wR * ${p} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(s/4)*4,v=s%4,k=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${A}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
const float initializationValue = ${y};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${d}, d),
|
|
getValue(batch, xR, xC + 2 * ${d}, d),
|
|
getValue(batch, xR, xC + 3 * ${d}, d)
|
|
);
|
|
|
|
${k}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${v===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${d}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${v===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${d}, d),
|
|
getValue(batch, xR, xC + 2 * ${d}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},H2=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,d=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${y});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${d}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${p}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xD, xR, xC, ch);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${E} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let k=Math.floor(s/4)*4,N=s%4,C=`
|
|
if (${A}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${y});
|
|
const float initializationValue = ${x};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${x});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${d}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${k}; wC += 4) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${N===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${N===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${N===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${v});
|
|
}
|
|
}
|
|
`}};function mJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Mu(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;w.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let u=R.computePool2DInfo(r.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return ma({inputs:{x:r},backend:n});let p=new Wp(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var gJ={kernelName:Js,backendName:"webgl",kernelFunc:mJ};function yJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:d}=a,u=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,u,o,l,d),c=new H2(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var AJ={kernelName:Ad,backendName:"webgl",kernelFunc:yJ},xJ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,d=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${d}, ${u});
|
|
const float avgMultiplier = float(${p});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${o};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},bJ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=p-1-e.padInfo.top,m=c-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${f}, ${m});
|
|
const float avgMultiplier = float(${g});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${u};
|
|
wD += ${o}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${d}) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function vJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:d,dimRoundingMode:u}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,d,u),h=new bJ(c);return n.runWebGLProgram(h,[r],i.dtype)}var wJ={kernelName:Bc,backendName:"webgl",kernelFunc:vJ};function kJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Mu([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:d}=a,u=R.computePool2DInfo(i.shape,o,l,1,d),p=new xJ(u);return n.runWebGLProgram(p,[r],i.dtype)}var IJ={kernelName:Wc,backendName:"webgl",kernelFunc:kJ};function TJ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return kf({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var SJ={kernelName:Qs,backendName:"webgl",kernelFunc:TJ},NJ=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${o};
|
|
float inv = scale * inversesqrt(variance + float(${s}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},CJ=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${o};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},EJ=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let d=[a,r,s],u=null;i!=null&&(u=i.shape,d.push(i));let p=null;o!=null&&(p=o.shape,d.push(o));let c=ne().getBool("WEBGL_PACK_NORMALIZATION")?new CJ(a.shape,r.shape,s.shape,u,p,l):new NJ(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(c,d,d[0].dtype)},RJ={kernelName:hi,backendName:"webgl",kernelFunc:EJ},MJ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=FJ(this.rank),a,r=e.map((s,i)=>`sourceLoc.${q2[i]} = start[${i}] + coords.${q2[i]};`);a=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${a}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},q2=["x","y","z","w","u","v"];function FJ(e){if(e===1)return"sourceLoc";if(e<=6)return q2.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var DJ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),n=On("coords",this.rank),a=On("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
|
|
result.x = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.y = ${s};
|
|
--${a[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${a[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((d,u)=>`start[${u}]`).join()});`:e.map((d,u)=>`${a[u]} = ${n[u]} + start[${u}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}};function $J(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=En.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function Lu(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=En.parseSliceParams(r,s,i);if(En.assertParamsValid(r,o,l),w.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=xZ(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}let{isPacked:d}=n.texData.get(r.dataId),u=En.isSliceContinous(r.shape,o,l);if(d||!u){let p=ne().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new DJ(l):new MJ(l),c=[o];return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),$J(r,o,l,n)}var OJ={kernelName:zl,backendName:"webgl",kernelFunc:Lu},_J=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=R.getReshaped(r.shape,s,o),d=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(u,i,s.length),h=[],f=we({inputs:{x:r},backend:n,attrs:{shape:l}}),m=_n({inputs:{x:f},backend:n,attrs:{perm:d}}),g=we({inputs:{x:m},backend:n,attrs:{shape:u}}),y=Lu({inputs:{x:g},backend:n,attrs:{begin:p,size:c}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},PJ={kernelName:sl,backendName:"webgl",kernelFunc:_J};function zJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),d=U4(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}var LJ={kernelName:Vc,backendName:"webgl",kernelFunc:zJ};function WJ(e){let{inputs:t,backend:n}=e,{s0:a,s1:r}=t,s=n.readSync(a.dataId),i=n.readSync(r.dataId),o=R.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var BJ={kernelName:Uc,backendName:"webgl",kernelFunc:WJ},VJ="return float(a != b);",gk=vn({opSnippet:VJ,cpuKernelImpl:fZ,dtype:"bool"}),UJ={kernelName:Sl,backendName:"webgl",kernelFunc:gk};function Bp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ma({inputs:{x:r.complexTensorInfos.real},backend:n})}var jJ={kernelName:ph,backendName:"webgl",kernelFunc:Bp},GJ="return float(int(x));";function HJ(e,t){let n=new Fs(e.shape,GJ),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function X2(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return ma({inputs:{x:r},backend:n});let i=Gt(r.shape),o=X2({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Ds({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Bp({inputs:{input:r},backend:n}),o=X2({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,s)){let i=ma({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return HJ(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=gk({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var qJ={kernelName:ei,backendName:"webgl",kernelFunc:X2},yk="return ceil(x);",XJ=nt({opSnippet:yk,packedOpSnippet:yk,cpuKernelImpl:KK}),KJ={kernelName:ti,backendName:"webgl",kernelFunc:XJ},ZJ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}},YJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function JJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ne().getBool("WEBGL_PACK_CLIP")?o=new YJ(r.shape):o=new ZJ(r.shape);let l=[[s],[i]];return n.runWebGLProgram(o,[r],r.dtype,l)}var QJ={kernelName:us,backendName:"webgl",kernelFunc:JJ},eQ=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 Ak(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function tQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new eQ(a.shape),i=[Ak(a,r.complexTensorInfos.real),Ak(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var nQ={kernelName:xd,backendName:"webgl",kernelFunc:tQ},aQ=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},rQ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=yt(a),s=On("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],d=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${d.join()}));
|
|
}`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`
|
|
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${Tf(i,l,m)}),
|
|
vec2(${Tf(d,l,m)}));
|
|
}`}let c=o.length,h=o[o.length-1];p+=`
|
|
return getChannel(
|
|
getT${c}(${Tf(i,l,h)}),
|
|
vec2(${Tf(d,l,h)}));`,this.userCode=`
|
|
float getValue(${i.map(f=>"int "+f)}) {
|
|
${p}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[a-1]} = ${s[a-1]} + 1;
|
|
if (${s[a-1]} < ${n[a-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[a-2]} = ${s[a-2]} + 1;
|
|
if (${s[a-2]} < ${n[a-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[a-1]} = ${s[a-1]} - 1;
|
|
if (${s[a-2]} < ${n[a-2]} &&
|
|
${s[a-1]} < ${n[a-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Tf(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Sf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return ma({inputs:{x:r.complexTensorInfos.imag},backend:n})}var sQ={kernelName:rh,backendName:"webgl",kernelFunc:Sf};function Wu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let u=e.map(m=>Bp({inputs:{input:m},backend:n})),p=e.map(m=>Sf({inputs:{input:m},backend:n})),c=Wu(u,t,n),h=Wu(p,t,n),f=Ds({inputs:{real:c,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let u=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return we({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),p=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),c=R.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=ZK(p,c,a,h),m=R.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,a,f);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>ne().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),p=Wu(e.slice(0,u),t,n),c=Wu(e.slice(u),t,n),h=Wu([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(ne().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new rQ(e.map(p=>p.shape),t);return n.runWebGLProgram(u,e,a)}let{tensors2D:s,outShape:i}=iQ(e,t,n),o=new aQ(s.map(u=>u.shape)),l=n.runWebGLProgram(o,s,a);s.forEach(u=>n.disposeIntermediateTensorInfo(u));let d=we({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),d}function iQ(e,t,n){let a=R.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>we({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function xk(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=R.computeOutShape(t.map(d=>d.shape),s);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(d=>w.sizeFromShape(d.shape)>0);if(o.length===1)return ma({inputs:{x:o[0]},backend:n});let l=o.map(d=>d.shape);return R.assertParamsConsistent(l,s),Wu(o,s,n)}var oQ={kernelName:il,backendName:"webgl",kernelFunc:xk},bk=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,d=e.dilationHeight,u=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";n&&(a?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${l});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${A}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${y}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${d};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${v}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},lQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${a});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d2 = coords.u;
|
|
|
|
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xFCorner = xFRCCorner.x;
|
|
int xRCorner = xFRCCorner.y;
|
|
int xCCorner = xFRCCorner.z;
|
|
|
|
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
|
|
// y(yF, yR, yC, d2). ? = to be determined. : = across all
|
|
// values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${u}; wF++) {
|
|
int xF = xFCorner + wF * ${o};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},uQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Ra(this.outputShape.length);let{dataFormat:n}=t,a=$n(),r=n==="channelsLast",s=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let d=0;d<=1;d++)for(let u=0;u<=1;u++)l+=`
|
|
blockIndex = rc.y + ${u};
|
|
pos = rc.x + ${d};
|
|
|
|
${o}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${s}] && d0 >= 0) {
|
|
// Use custom imod instead mod. On Intel GPU, mod may generate
|
|
// unexpected value.
|
|
// https://github.com/tensorflow/tfjs/issues/5447
|
|
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
|
|
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
|
|
inChannels);
|
|
|
|
if(d1 < inputShape[${i}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${d*2+u}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${d*2+u}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${l}
|
|
|
|
${a.output} = result;
|
|
}
|
|
`}};function vk({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,d=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],c=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((p===1||c===1)&&u>dk)&&d.isPacked&&h&&d.texture!=null&&l[2]%2!=0&&w.arraysEqual(d.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,n.inChannels],dtype:e.dtype},b=d.shape;d.shape=d.shape.slice(),d.shape[d.shape.length-2]++,w.assert(Pp(d.shape,x.shape),()=>`packed reshape ${d.shape} to ${x.shape} isn't free`);let v=we({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(v);let k=kf({a:x,b:v,backend:a,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),N=a.texData.get(k.dataId);w.assert(N.isPacked,()=>"batchMatMul result is expected to be packed"),d.shape=b,N.shape=n.outShape,g=ma({inputs:{x:k},backend:a}),g.shape=n.outShape,y.push(k)}else{let A=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=we({inputs:{x:e},backend:a,attrs:{shape:[1,A,n.inChannels]}}),b=we({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),v=kf({a:x,b,transposeA:f,transposeB:m,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=we({inputs:{x:v},backend:a,attrs:{shape:n.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)a.disposeIntermediateTensorInfo(A);return g}function wk({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:d,inChannels:u,outWidth:p,outHeight:c,dataFormat:h}=n,f=h==="channelsLast",m=l*d*u,g=c*p,y=[m,g],A=!0,x=!1,b=[],v=we({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),k=we({inputs:{x:t},backend:a,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(k);let N=new uQ(y,n),C=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=a.runWebGLProgram(N,[v],"float32",C),O=we({inputs:{x:E},backend:a,attrs:{shape:[1,y[0],y[1]]}});b.push(E),b.push(O);let D=r!=null,T=s!=null,P=o==="leakyrelu",_=o?bf(o,!0):null,j=new sk(O.shape,k.shape,[1,g,n.outChannels],A,x,D,_,T,P),q=[O,k];if(r&&q.push(r),T&&q.push(s),P){let te=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),b.push(te)}let z=a.runWebGLProgram(j,q,"float32"),X=f?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],Z=we({inputs:{x:z},backend:a,attrs:{shape:X}});b.push(z);for(let te of b)a.disposeIntermediateTensorInfo(te);return Z}function dQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:d,dimRoundingMode:u}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=vk({x:r,filter:s,convInfo:c,backend:n});else if(ne().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=wk({x:r,filter:s,convInfo:c,backend:n});else{let m=new bk(c);h=n.runWebGLProgram(m,[r,s],"float32")}let f=we({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),f}var pQ={kernelName:ni,backendName:"webgl",kernelFunc:dQ},cQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int d2 = coords.w;
|
|
|
|
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${s}) {
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
} else {
|
|
float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},hQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,d=s?2:3,u=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${u}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${d}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${s}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},fQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${a} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},mQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,d=a-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${d});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${a} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function gQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:d,filterShape:u}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(r.shape,u,i,1,o,d,!1,p),h=new cQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var yQ={kernelName:Gc,backendName:"webgl",kernelFunc:gQ};function AQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:d,dimRoundingMode:u}=a,p=R.convertConv2DDataFormat(d),c=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new hQ(c);return n.runWebGLProgram(h,[r,s],"float32")}var xQ={kernelName:ai,backendName:"webgl",kernelFunc:AQ};function bQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,d=R.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new lQ(d);return n.runWebGLProgram(u,[r,s],"float32")}var vQ={kernelName:bd,backendName:"webgl",kernelFunc:bQ};function wQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,d=R.computeConv3DInfo(r.shape,l,i,1,o),u=new fQ(d);return n.runWebGLProgram(u,[r,s],"float32")}var kQ={kernelName:Hc,backendName:"webgl",kernelFunc:wQ};function IQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,d=R.computeConv3DInfo(l,s.shape,o,1,i),u=new mQ(d);return n.runWebGLProgram(u,[r,s],"float32")}var TQ={kernelName:qc,backendName:"webgl",kernelFunc:IQ},SQ=rk+`
|
|
return cos(x);
|
|
`,NQ=nt({opSnippet:SQ}),CQ={kernelName:ri,backendName:"webgl",kernelFunc:NQ},EQ=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,RQ=nt({opSnippet:EQ}),MQ={kernelName:si,backendName:"webgl",kernelFunc:RQ},FQ=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[d]=t,[u,p]=n;this.outputShape=[d,u,p,l];let c=a==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,b]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${A});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${s}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${c} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},DQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:d}=a,u=new FQ(r.shape,s.shape,o,l,d);return n.runWebGLProgram(u,[r,s,i],"float32")},$Q={kernelName:ol,backendName:"webgl",kernelFunc:DQ},kk=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${Ik(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${yt(a)} coords = getOutputCoords();
|
|
int end = ${Tk(a,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${Tk(a,"coords")} = idx;
|
|
val += getX(${Ik(a,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function Ik(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Tk(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,d=R.getAxesPermutation([s],l),u=r;d!=null&&(u=_n({inputs:{x:r},backend:n,attrs:{perm:d}}));let p=R.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=u.shape[p],h=ma({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(c))-1;f++){let m=new kk(u.shape,!1,o),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let f=new kk(u.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(d!=null){let f=R.getUndoAxesPermutation(d),m=_n({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var _Q={kernelName:ii,backendName:"webgl",kernelFunc:OQ};function PQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),d=n.readSync(s.dataId),u=U4(l,d,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),d=n.bufferSync(s),u=XK(l,d,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var zQ={kernelName:Xc,backendName:"webgl",kernelFunc:PQ},LQ=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 WQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],d=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,c=d*s,h=u/(s*s),f=i==="NHWC"?[o,p,c,h]:[o,h,p,c],m=new LQ(f,s,i);return n.runWebGLProgram(m,[r],r.dtype)}var BQ={kernelName:ll,backendName:"webgl",kernelFunc:WQ},Sk=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ra(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",d="";n&&(a?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,d="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${l}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${o};
|
|
int q = d2 - d1 * ${o};
|
|
|
|
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 < ${s}; wR++) {
|
|
int xR = xRCorner + wR * dilations[0];
|
|
|
|
if (xR < 0 || xR >= inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${i}; wC++) {
|
|
int xC = xCCorner + wC * dilations[1];
|
|
|
|
if (xC < 0 || xC >= inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${u}
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}},Nk=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ra(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,d=e.filterHeight,u=e.filterWidth,p=u,c=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)c+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;c+=`
|
|
for (int r = 0; r < ${d}; r++) {
|
|
`;for(let g=0;g<u;g++)c+=`
|
|
xTexelC${g*2} = vec4(0.0);
|
|
xTexelC${g*2}Ready = 0;
|
|
xTexelC${g*2+1} = vec4(0.0);
|
|
xTexelC${g*2+1}Ready = 0;
|
|
xC${g} = vec4(0.0);`;c+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let g=0;g<(p+1)/2;g++){let y=g*2;if(c+=`
|
|
xC = xCCorner + ${y*l};
|
|
`,o===1){if(y<u&&(i%2==1?(c+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`,l===1&&y>0?c+=`
|
|
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
|
|
`:c+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
|
|
} else {
|
|
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
|
|
}
|
|
`):c+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xC${y} = xTexelC${y};
|
|
`,y+1<u)){let A=i%2==0?w.nearestLargerEven(l):l;l%2==0&&i%2==1||l%2!=0&&i%2!=1?(c+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${A};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
`,l>1&&(c+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`),c+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
|
|
`):A===1?c+=`
|
|
xC${y+1} = xTexelC${y};
|
|
`:c+=`
|
|
xCOffset = xC + ${A};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y+1} = xTexelC${y+1};
|
|
`}}else y<u&&(i%2==1?(c+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`,y+1<u&&(c+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
|
|
`)):(c+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(
|
|
xTexelC${y}.xy, xTexelC${y+1}.xy);
|
|
`,y+1<u&&(c+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`)));y<u&&(c+=`
|
|
wTexel = getW(r, ${y}, d1, q);
|
|
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
|
|
`,y+1<u&&(c+=`
|
|
wTexel = getW(r, ${y+1}, d1, q);
|
|
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}c+=`
|
|
}
|
|
`,c+=`
|
|
}
|
|
`;let h="",f="";n&&(a?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:h=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${h}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${s};
|
|
int q = d2 - d1 * ${s};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${c}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${m}
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};function VQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:d}=a,u=l;u==null&&(u=[1,1]),w.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=R.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!0),c;ne().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new Nk(p):c=new Sk(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(c,[r,s],"float32",h)}var UQ={kernelName:oi,backendName:"webgl",kernelFunc:VQ},jQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int dm = coords.w;
|
|
int d2 = d1 * ${s} + dm;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
// TO DO: Vec4 over the batch size
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},GQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
ivec2 dyCorner = coords.yz - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${o}; dm++) {
|
|
int d2 = d1 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function HQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,filterShape:u}=a,p=R.computeConv2DInfo(r.shape,u,i,o,l,d,!0),c=new jQ(p);return n.runWebGLProgram(c,[r,s],"float32")}var qQ={kernelName:Kc,backendName:"webgl",kernelFunc:HQ};function XQ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:d,inputShape:u}=a,p=R.computeConv2DInfo(u,s.shape,i,o,l,d,!0),c=new GQ(p);return n.runWebGLProgram(c,[r,s],"float32")}var KQ={kernelName:Zc,backendName:"webgl",kernelFunc:XQ},ZQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function YQ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=we({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new ZQ(s),l=n.runWebGLProgram(o,[i],i.dtype),d=we({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),d}var JQ={kernelName:Yc,backendName:"webgl",kernelFunc:YQ},QQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:d}=e,{top:u,left:p}=a;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${s});
|
|
const ivec2 pads = ivec2(${u}, ${p});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${i}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${o}; w++) {
|
|
int wIn = wBeg + w * ${d};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function eee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,d=R.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new QQ(d);u=n.runWebGLProgram(p,[r,s],"float32");let c=we({inputs:{x:u},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(u),c}var tee={kernelName:vd,backendName:"webgl",kernelFunc:eee};function nee(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:l}=R.decodeEinsumEquation(r,s.length);R.checkEinsumDimSizes(i.length,l,s);let{path:d,steps:u}=R.getEinsumComputePath(o,l),p=u.length,c=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=R.getEinsumPermutation(h,l[g]),x;R.isIdentityPermutation(y)?x=s[g]:(x=_n({inputs:{x:s[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=we({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),c===null?c=x:(c=G2({inputs:{a:x,b:c},backend:n}),f.push(c))}m<p-1&&(d[m]>=0&&(c=wf({inputs:{x:c},backend:n,attrs:{axis:d[m]-(i.length-h),keepDims:!1}}),f.push(c)),h--)}for(let m of f)m!==c&&n.disposeIntermediateTensorInfo(m);return c}var aee={kernelName:eh,backendName:"webgl",kernelFunc:nee},ree="return (x >= 0.0) ? x : (exp(x) - 1.0);",see=`
|
|
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;
|
|
`,iee=nt({opSnippet:ree,packedOpSnippet:see}),oee={kernelName:ui,backendName:"webgl",kernelFunc:iee},lee="return (b >= 1.0) ? a : a * (b + 1.0);",uee=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,dee=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ne().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Lp(uee,a.shape,r.shape):new zu(lee,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},pee={kernelName:th,backendName:"webgl",kernelFunc:dee},cee=`
|
|
return vec4(equal(a, b));
|
|
`,hee="return float(a == b);",fee=vn({opSnippet:hee,packedOpSnippet:cee,dtype:"bool",cpuKernelImpl:YK}),mee={kernelName:dl,backendName:"webgl",kernelFunc:fee},gee=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${R.ERF_P};
|
|
float a1 = ${R.ERF_A1};
|
|
float a2 = ${R.ERF_A2};
|
|
float a3 = ${R.ERF_A3};
|
|
float a4 = ${R.ERF_A4};
|
|
float a5 = ${R.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,yee=nt({opSnippet:gee}),Aee={kernelName:ul,backendName:"webgl",kernelFunc:yee},Ck="return exp(x);",Ek=nt({opSnippet:Ck,packedOpSnippet:Ck,cpuKernelImpl:JK,dtype:"float32"}),xee={kernelName:di,backendName:"webgl",kernelFunc:Ek};function K2(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),we({inputs:{x:s},backend:a,attrs:{shape:o}})}var bee={kernelName:pl,backendName:"webgl",kernelFunc:K2},Rk="return exp(x) - 1.0;",vee=nt({opSnippet:Rk,packedOpSnippet:Rk,cpuKernelImpl:QK}),wee={kernelName:cl,backendName:"webgl",kernelFunc:vee},Mk=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${a});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${a}; i++) {
|
|
// x = (-2|2 * PI / N) * index * i;
|
|
float x = exponentMultiplierTimesIndexRatio * float(i);
|
|
float expR = cos(x);
|
|
float expI = sin(x);
|
|
float real = getReal(batch, i);
|
|
float imag = getImag(batch, i);
|
|
|
|
result +=
|
|
unaryOpComplex(real, expR, imag, expI) / ${s};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function Fk(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=we({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,d=new Mk("real",l,t),u=new Mk("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],c=n.runWebGLProgram(d,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),f=Ds({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let m=we({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function kee(e){let{inputs:t,backend:n}=e,{input:a}=t;return Fk(a,!1,n)}var Iee={kernelName:nh,backendName:"webgl",kernelFunc:kee},Tee=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}};function Vp(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new Tee(a,r),o=[[r]];return t.runWebGLProgram(i,[],s,o)}}var See={kernelName:wd,backendName:"webgl",kernelFunc:Vp},Nee=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${t} - x - 1;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Cee={kernelName:hl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new Nee(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Dk="return floor(x);",Eee=nt({opSnippet:Dk,packedOpSnippet:Dk,cpuKernelImpl:eZ}),Ree={kernelName:pi,backendName:"webgl",kernelFunc:Eee},Mee=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,Fee=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Dee=vn({opSnippet:Mee,packedOpSnippet:Fee,dtype:"int32"}),$ee={kernelName:ci,backendName:"webgl",kernelFunc:Dee},Oee=class{constructor(e){this.variableNames=["A"];let t=$n(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},_ee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=$n(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}.0, ${n}.0);
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},Pee={kernelName:kh,backendName:"webgl",kernelFunc:zee},Bu;function zee(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,d]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[d,l],p=[d,l,s];(o||i)&&(Bu==null&&(Bu=document.createElement("canvas").getContext("2d")),Bu.canvas.width=l,Bu.canvas.height=d,Bu.drawImage(r,0,0,l,d),r=Bu.canvas);let c=n.makeTensorInfo(u,"int32");n.texData.get(c.dataId).usage=Ca.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=ne().getBool("WEBGL_PACK")?new _ee(p):new Oee(p),f=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),f}function Lee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dataFormat:u,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:f}=a,m=R.convertConv2DDataFormat(u),g=R.computeConv2DInfo(r.shape,s.shape,l,p,d,c,!1,m),y,A=[];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"))y=vk({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(ne().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=wk({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,k=h==="leakyrelu",N=h?bf(h,!1):null,C=new bk(g,b,N,v,k),E=[r,s];if(i&&E.push(i),o&&E.push(o),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));E.push(O),A.push(O)}y=n.runWebGLProgram(C,E,"float32")}let x=we({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Wee={kernelName:qi,backendName:"webgl",kernelFunc:Lee};function Bee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:d,dilations:u,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,f=[],m=u;m==null&&(m=[1,1]),w.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=R.computeConv2DInfo(r.shape,s.shape,l,m,d,p,!0),y=ne().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=c?bf(c,y):null,x=[r,s],b=i!=null,v=o!=null,k=c==="leakyrelu";if(b&&x.push(i),v&&x.push(o),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(O),f.push(O)}let N;y?N=new Nk(g,b,A,v,k):N=new Sk(g,b,A,v,k);let C=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],E=n.runWebGLProgram(N,x,"float32",C);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),E}var Vee={kernelName:Xi,backendName:"webgl",kernelFunc:Bee},Uee=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=yt(t.length),r=yt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${a} strides = ${a}(${this.strides});
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${s};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function jee(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=w.sizeFromShape(a.shape),[l,d,u,p]=R.prepareAndValidate(a,r),c=we({inputs:{x:r},backend:n,attrs:{shape:[d,i]}}),h=we({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/u,u]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),A=n.bufferSync(a),x=tZ(y,A,a.dtype,d,i,u,p,a.shape,o);return n.makeTensorInfo(l,a.dtype,x.values)}let f=new Uee(i,p,[d,u]),m=n.runWebGLProgram(f,[h,c],h.dtype),g=we({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Gee={kernelName:ml,backendName:"webgl",kernelFunc:jee},Hee=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=yt(this.rank),a=qee(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function qee(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function $k(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],d=n.readSync(s.dataId),u=r.shape[l];for(let b=0;b<d.length;++b){let v=d[b];w.assert(v<=u-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${u-1}]`)}let p=R.segment_util.collectGatherOpShapeInfo(r,s,l,o),c=w.sizeFromShape(s.shape),h=[],f=we({inputs:{x:r},backend:n,attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]}}),m=we({inputs:{x:s},backend:n,attrs:{shape:[p.batchSize,c/p.batchSize]}});h.push(f),h.push(m);let g=[p.batchSize,p.outerSize,c/p.batchSize,p.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(m),v=n.bufferSync(f),k=nZ(v,b,g);return h.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(p.outputShape,k.dtype,k.values)}let y=new Hee(f.shape,g),A=n.runWebGLProgram(y,[f,m],f.dtype);h.push(A);let x=we({inputs:{x:A},backend:n,attrs:{shape:p.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Xee={kernelName:fl,backendName:"webgl",kernelFunc:$k},Kee="return float(a > b);",Zee=`
|
|
return vec4(greaterThan(a, b));
|
|
`,Yee=vn({opSnippet:Kee,packedOpSnippet:Zee,cpuKernelImpl:aZ,dtype:"bool"}),Jee={kernelName:gl,backendName:"webgl",kernelFunc:Yee},Qee="return float(a >= b);",ete=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,tte=vn({opSnippet:Qee,packedOpSnippet:ete,dtype:"bool",cpuKernelImpl:rZ}),nte={kernelName:fi,backendName:"webgl",kernelFunc:tte};function ate(e){let{inputs:t,backend:n}=e,{input:a}=t;return Fk(a,!0,n)}var rte={kernelName:ah,backendName:"webgl",kernelFunc:ate},ste="return float(!isnan(x) && !isinf(x));",ite=nt({opSnippet:ste,dtype:"bool"}),ote={kernelName:yl,backendName:"webgl",kernelFunc:ite},lte="return float(isinf(x));",ute=nt({opSnippet:lte,dtype:"bool"}),dte={kernelName:Al,backendName:"webgl",kernelFunc:ute},pte="return float(isnan(x));",cte=nt({opSnippet:pte,dtype:"bool"}),hte={kernelName:xl,backendName:"webgl",kernelFunc:cte},fte="return float(a < b);",mte=`
|
|
return vec4(lessThan(a, b));
|
|
`,gte=vn({opSnippet:fte,packedOpSnippet:mte,cpuKernelImpl:sZ,dtype:"bool"}),yte={kernelName:bl,backendName:"webgl",kernelFunc:gte},Ate="return float(a <= b);",xte=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,bte=vn({opSnippet:Ate,packedOpSnippet:xte,cpuKernelImpl:iZ,dtype:"bool"}),vte={kernelName:vl,backendName:"webgl",kernelFunc:bte};function wte(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=oZ(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var kte={kernelName:sh,backendName:"webgl",kernelFunc:wte},Ite=`if (x < 0.0) return NAN;
|
|
return log(x);`,Tte=`
|
|
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;
|
|
`,Ste=nt({opSnippet:Ite,packedOpSnippet:Tte,cpuKernelImpl:lZ}),Nte={kernelName:yi,backendName:"webgl",kernelFunc:Ste},Cte="return log(1.0 + x);",Ete=nt({opSnippet:Cte}),Rte={kernelName:wl,backendName:"webgl",kernelFunc:Ete},Mte="return float(a >= 1.0 && b >= 1.0);",Fte=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Dte=vn({opSnippet:Mte,packedOpSnippet:Fte,dtype:"bool"}),$te={kernelName:kl,backendName:"webgl",kernelFunc:Dte},Ote="return float(!(x >= 1.0));",_te=nt({opSnippet:Ote}),Pte={kernelName:kd,backendName:"webgl",kernelFunc:_te},zte="return float(a >= 1.0 || b >= 1.0);",Lte=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,Wte=vn({opSnippet:zte,packedOpSnippet:Lte,dtype:"bool"}),Bte={kernelName:Id,backendName:"webgl",kernelFunc:Wte},Vte=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${s}; j <= ${s}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},Ute=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${s};
|
|
vec2 cache = vec2(0.);
|
|
if(firstChannel >= 0){
|
|
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
|
|
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
|
|
if(hasNextRow){
|
|
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
|
|
}
|
|
}
|
|
|
|
ivec2 depth = ivec2(d, d + 1);
|
|
for (int j = - ${s}; j <= ${s}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
|
|
|
|
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
|
|
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
|
|
|
|
if(depthInRange || depthPlusOneInRange){
|
|
vec4 z = vec4(0.);
|
|
vec4 xFragAtCurrentDepth;
|
|
z.xz = cache.xy;
|
|
if(depthPlusOneInRange && hasNextCol){
|
|
xFragAtCurrentDepth = idx.y != d ?
|
|
getX(b, r, c, idx.y) : xFragAtOutputCoords;
|
|
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
|
|
if(hasNextRow){
|
|
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
|
|
}
|
|
}
|
|
cache.xy = z.yw;
|
|
sum += z * z;
|
|
}
|
|
}
|
|
vec4 result = xAtOutputCoords * ${o};
|
|
setOutput(result);
|
|
}
|
|
`}},jte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,d=ne().getBool("WEBGL_PACK_NORMALIZATION")?new Ute(r.shape,s,i,o,l):new Vte(r.shape,s,i,o,l);return n.runWebGLProgram(d,[r],r.dtype)},Gte={kernelName:Td,backendName:"webgl",kernelFunc:jte},Hte=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${t})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${t} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${a}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${a})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},qte=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:d,beta:u}=a,p=new Hte(r.shape,o,l,d,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},Xte={kernelName:ih,backendName:"webgl",kernelFunc:qte};function Kte(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=we({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,e.dtype,"max",a),l=we({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function Ok(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),d=l,u=R.getAxesPermutation(d,o),p=u!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let A=n.texData.get(h.dataId).values,x=new Array(o);for(let k=0;k<x.length;k++)x[k]=r.shape[u[k]];let b=U2(A,r.shape,r.dtype,u,x);h=n.makeTensorInfo(x,r.dtype);let v=n.texData.get(h.dataId);v.values=b}else h=vf(r,u,n);d=R.getInnerMostAxes(d.length,o)}R.assertAxesAreInnerMostDims("max",d,o);let[f,m]=R.computeOutAndReduceShapes(h.shape,d),g=f;i&&(g=R.expandShapeToKeepDim(f,l));let y;if(c){let A=n.texData.get(h.dataId).values,x=uZ(A,w.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let b=n.texData.get(y.dataId);b.values=x}else y=Kte(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var Zte={kernelName:Ai,backendName:"webgl",kernelFunc:Ok},Yte=Q4+`
|
|
return max(a, b);
|
|
`,Jte=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+xf+`
|
|
return result;
|
|
`,Qte=vn({opSnippet:Yte,packedOpSnippet:Jte,cpuKernelImpl:dZ}),ene={kernelName:xi,backendName:"webgl",kernelFunc:Qte};function tne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Mu(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,d=1;w.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let u=R.computePool2DInfo(r.shape,s,i,d,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return ma({inputs:{x:r},backend:n});let p=new Wp(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var nne={kernelName:bi,backendName:"webgl",kernelFunc:tne};function ane(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:d}=a,u=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,u,o,d,l),c=new H2(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var rne={kernelName:Sd,backendName:"webgl",kernelFunc:ane},sne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${r};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${t}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},ine=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,d=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,c=d-1-e.padInfo.left,h=o*l*d-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${p}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${o};
|
|
wD += ${r}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${d} +
|
|
wR * ${d} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function one(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:d,dimRoundingMode:u}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,l,p,d,u),h=new H2(c,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new ine(c),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var lne={kernelName:lh,backendName:"webgl",kernelFunc:one};function une(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Mu([s,i],"maxPoolGrad");let{filterSize:l,strides:d,pad:u,dimRoundingMode:p}=a,c=R.computePool2DInfo(o.shape,l,d,1,u,p),h=!0,f=new Wp(c,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new sne(c),y=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var dne={kernelName:oh,backendName:"webgl",kernelFunc:une};function pne(e,t,n,a){let r=new Wp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Wp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var cne={kernelName:uh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let d=[1,1];w.assert(R.eitherStridesOrDilationsAreOne(s,d),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${d}'`);let u=R.computePool2DInfo(a.shape,r,s,d,i),[p,c]=pne(a,o,u,l);return[p,c]}};function hne(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=we({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Co(i,"float32","mean",a),l=we({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var fne={kernelName:vi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),d=l,u=R.getAxesPermutation(d,o),p=u!=null,c=i.shouldExecuteOnCPU([a]),h=[],f=a;if(p){if(c){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=a.shape[u[N]];let v=U2(x,a.shape,a.dtype,u,b);f=i.makeTensorInfo(b,a.dtype);let k=i.texData.get(f.dataId);k.values=v}else f=vf(a,u,i);h.push(f),d=R.getInnerMostAxes(d.length,o)}R.assertAxesAreInnerMostDims("sum",d,o);let[m,g]=R.computeOutAndReduceShapes(f.shape,d),y=m;r&&(y=R.expandShapeToKeepDim(m,l));let A=hne(f,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return A}};function mne(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),d=l,u=R.getAxesPermutation(d,o),p=r;u!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:u}}),d=R.getInnerMostAxes(d.length,r.shape.length)),R.assertAxesAreInnerMostDims("min",d,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,d),f=w.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Co(m,m.dtype,"min",n),y;if(i){let A=R.expandShapeToKeepDim(c,l);y=we({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var gne={kernelName:wi,backendName:"webgl",kernelFunc:mne},yne=Q4+`
|
|
return min(a, b);
|
|
`,Ane=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+xf+`
|
|
return result;
|
|
`,xne=vn({opSnippet:yne,packedOpSnippet:Ane,cpuKernelImpl:pZ}),bne={kernelName:ki,backendName:"webgl",kernelFunc:xne},vne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((d,u)=>d[0]+e[u]+d[1]);let a=e.length,r=yt(a),s=t.map(d=>d[0]).join(","),i=t.map((d,u)=>d[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${a}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},wne=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let a=e.length,r=yt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=On("rc",a),l=On("source",a),d=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${p};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${p};
|
|
}
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${o[a-1]} += 1;
|
|
if(${d}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${p}) +
|
|
gte * ((end - 1) * 2 - source + ${p});
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${o[a-1]} += 1;
|
|
if(${d}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${o[a-1]} += 1;
|
|
if(${d}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},kne=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ne().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wne(a.shape,r,s):new vne(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},Ine={kernelName:Ii,backendName:"webgl",kernelFunc:kne},Tne=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Sne=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+xf+`
|
|
return result;
|
|
`,Nne=vn({opSnippet:Tne,packedOpSnippet:Sne}),Cne={kernelName:Il,backendName:"webgl",kernelFunc:Nne},Ene=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}},Rne=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Mne=`
|
|
// 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;
|
|
`,_k=vn({opSnippet:Rne,packedOpSnippet:Mne,checkOutOfBounds:!0}),Fne={kernelName:li,backendName:"webgl",kernelFunc:_k},Pk="return a - b;",zk=vn({opSnippet:Pk,packedOpSnippet:Pk,supportsComplex:!0,cpuKernelImpl:NZ}),Dne={kernelName:Vi,backendName:"webgl",kernelFunc:zk};function Lk(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=Ok({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),d=we({inputs:{x:o},backend:n,attrs:{shape:l}}),u=zk({inputs:{a:r,b:d},backend:n}),p=Ek({inputs:{x:u},backend:n}),c=wf({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=we({inputs:{x:c},backend:n,attrs:{shape:l}}),f=_k({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),f}var $ne={kernelName:Wi,backendName:"webgl",kernelFunc:Lk};function One(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:Lk({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),d=l.shape[0],u=l.shape[1],p=new Ene(d,u,s),c=[[i]],h=n.runWebGLProgram(p,[l],"int32",c);return o||n.disposeIntermediateTensorInfo(l),h}var _ne={kernelName:dh,backendName:"webgl",kernelFunc:One},Wk="return -x;";function Pne(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=hZ(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ne().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new _u(a.shape,Wk):r=new Fs(a.shape,Wk),n.runWebGLProgram(r,[a],a.dtype)}var zne={kernelName:Tl,backendName:"webgl",kernelFunc:Pne},Lne=xr.nonMaxSuppressionV3Impl;function Wne(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,d=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=Lne(d,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Bne={kernelName:Nl,backendName:"webgl",kernelFunc:Wne},Vne=xr.nonMaxSuppressionV4Impl;function Une(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:d}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=Vne(u,p,i,o,l,d);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var jne={kernelName:Cl,backendName:"webgl",kernelFunc:Une},Gne=xr.nonMaxSuppressionV5Impl;function Hne(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:d}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,f=l,m=d,{selectedIndices:g,selectedScores:y}=Gne(u,p,c,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var qne={kernelName:El,backendName:"webgl",kernelFunc:Hne},Xne=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${a}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},Kne=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=w.sizeFromShape(r.shape),d=new Xne(l,s,i,o),u=we({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(d,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let c=[...r.shape,s],h=we({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},Zne={kernelName:Si,backendName:"webgl",kernelFunc:Kne};function Nf(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Bp({inputs:{input:a},backend:n}),s=Nf({inputs:{x:r},backend:n}),i=Sf({inputs:{input:a},backend:n}),o=Nf({inputs:{x:i},backend:n}),l=Ds({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Vp({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var Yne={kernelName:Xl,backendName:"webgl",kernelFunc:Nf};function Bk(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Bp({inputs:{input:a},backend:n}),s=Bk({inputs:{x:r},backend:n}),i=Sf({inputs:{input:a},backend:n}),o=Nf({inputs:{x:i},backend:n}),l=Ds({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Vp({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var Jne={kernelName:Rl,backendName:"webgl",kernelFunc:Bk};function Qne(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return K2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=K2({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),d=xk({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),d}var eae={kernelName:Ml,backendName:"webgl",kernelFunc:Qne},tae=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,d)=>l[0]+e[d]+l[1]);let a=e.length,r=yt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,d)=>l[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}},nae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let a=e.length,r=yt(a),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=On("rc",a),l=On("source",a),d=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
|
|
if(${d}) {
|
|
`,a===1?"":`}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
|
|
if(${d}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=a===1?2:4;f<m;f++)h+=`
|
|
${p[f]}
|
|
if (${c}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`;h+=a===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},Vk=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a;if(w.sizeFromShape(r.shape)===0){let d=s.map((u,p)=>u[0]+r.shape[p]+u[1]);return Vp({backend:n,attrs:{shape:d,value:i,dtype:r.dtype}})}let o=ne().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nae(r.shape,s,i):new tae(r.shape,s,i),l=[[i]];return n.runWebGLProgram(o,[r],r.dtype,l)},aae={kernelName:Ni,backendName:"webgl",kernelFunc:Vk},rae=`
|
|
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);
|
|
`,sae=`
|
|
// 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));
|
|
`+xf+`
|
|
return result;
|
|
`,iae=vn({opSnippet:rae,packedOpSnippet:sae}),oae={kernelName:Ci,backendName:"webgl",kernelFunc:iae};function lae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],d=w.parseAxisParam(s,r.shape),u=d,p=R.getAxesPermutation(u,o),c=r;p!=null&&(c=_n({inputs:{x:r},backend:n,attrs:{perm:p}}),u=R.getInnerMostAxes(u.length,o),l.push(c)),R.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([c])){let f=n.texData.get(c.dataId).values,{outVals:m,outShape:g,outDtype:y}=mZ(c.shape,c.dtype,f,u);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(c.shape,u),g=w.sizeFromShape(m),y=we({inputs:{x:c},backend:n,attrs:{shape:[-1,g]}}),A=Eh(r.dtype),x=Co(y,A,"prod",n);h=we({inputs:{x},backend:n,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(h);let f=R.expandShapeToKeepDim(h.shape,d);h=we({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var uae={kernelName:Fl,backendName:"webgl",kernelFunc:lae},Uk=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=gZ(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},dae={kernelName:Nd,backendName:"webgl",kernelFunc:Uk},pae="return 1.0 / x;",cae=nt({opSnippet:pae}),hae={kernelName:Dl,backendName:"webgl",kernelFunc:cae},fae=lr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,mae=`
|
|
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;
|
|
`,gae=nt({opSnippet:fae,packedOpSnippet:mae}),yae={kernelName:Ri,backendName:"webgl",kernelFunc:gae},Aae=lr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,xae=`
|
|
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;
|
|
`,bae=nt({opSnippet:Aae,packedOpSnippet:xae}),vae={kernelName:Fi,backendName:"webgl",kernelFunc:bae},wae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},kae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]},
|
|
${d[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the four integer indices.
|
|
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
|
|
ivec3 sourceCeilRC = ivec3(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Iae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=ne().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new kae(r.shape,l,d,s,i):new wae(r.shape,l,d,s,i);return n.runWebGLProgram(u,[r],"float32")}var Tae={kernelName:Mi,backendName:"webgl",kernelFunc:Iae},Sae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],d=o[0]/l[0],u=o[1]/l[1],p=1/d,c=1/u,h=Math.ceil(p)*2+2,f=Math.ceil(c)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${d});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Nae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Sae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Cae={kernelName:hh,backendName:"webgl",kernelFunc:Nae},Eae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},Rae=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let d=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${d[0]/u[0]},
|
|
${d[1]/u[1]},
|
|
${d[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Mae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,d]=o,u=ne().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Rae(r.shape,l,d,s,i):new Eae(r.shape,l,d,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var Fae={kernelName:Cd,backendName:"webgl",kernelFunc:Mae},Dae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],d=o[0]/l[0],u=o[1]/l[1],p=1/d,c=1/u,h=Math.ceil(p)*2+2,f=Math.ceil(c)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${d});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${o[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function $ae(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Dae(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Oae={kernelName:ch,backendName:"webgl",kernelFunc:$ae},_ae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=yt(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},Pae=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=On("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=yt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${o(a.slice())};
|
|
if(${r}){
|
|
result.g = ${l(a.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${d(a.slice())};
|
|
if(${r}) {
|
|
result.a = ${u(a.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function d(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((y,A)=>c(A,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function c(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function zae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return ma({inputs:{x:r},backend:n});let l=ne().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pae(r.shape,o):new _ae(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var Lae={kernelName:Di,backendName:"webgl",kernelFunc:zae},Wae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${t.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Bae={kernelName:Kl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new Wae(a.shape,s),[d,u]=R.getImageCenter(i,a.shape[1],a.shape[2]),p=[[d,u,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[a],a.dtype,p)}},Vae=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Uae=nt({opSnippet:Vae}),jae={kernelName:$i,backendName:"webgl",kernelFunc:Uae},Gae="return inversesqrt(x);",Hae=nt({opSnippet:Gae,cpuKernelImpl:yZ}),qae={kernelName:Oi,backendName:"webgl",kernelFunc:Hae},jk=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=yt(r.length),l=yt(s.length),d="";n===1?d="i":n===2&&(d="i, j");let u=`getIndices(${d})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${r});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${u});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${c};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function Xae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:d,strides:u,outputSize:p}=R.calculateShapes(s,r,i),c=[p/d,d];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=we({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),f=we({inputs:{x:s},backend:n,attrs:{shape:[l,d]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new jk(l,o,h.shape.length,f.shape.length,u,c),y=n.runWebGLProgram(g,[f,h,m],f.dtype),A=we({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),A}var Kae={kernelName:Ol,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let d=0;d<t.length;d++)l.push(`${i[d]}`),d<e&&o.push(`${i[d]}`);a=o.join(),r=l.join()}let s=yt(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${a});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function Yae(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new Zae(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],Pa(r.dtype,s.dtype))}var Jae={kernelName:_l,backendName:"webgl",kernelFunc:Yae},Qae=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${R.SELU_SCALEALPHA};
|
|
float scale = ${R.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,ere=nt({opSnippet:Qae}),tre={kernelName:Pl,backendName:"webgl",kernelFunc:ere},Gk="return 1.0 / (1.0 + exp(-1.0 * x));",nre=nt({opSnippet:Gk,packedOpSnippet:Gk,cpuKernelImpl:AZ}),are={kernelName:Pi,backendName:"webgl",kernelFunc:nre},rre=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,sre=nt({opSnippet:rre}),ire={kernelName:Wl,backendName:"webgl",kernelFunc:sre},ore=rk+`
|
|
return sin(x);
|
|
`,lre=nt({opSnippet:ore}),ure={kernelName:_i,backendName:"webgl",kernelFunc:lre},dre=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,pre=nt({opSnippet:dre}),cre={kernelName:Ll,backendName:"webgl",kernelFunc:pre},hre=`
|
|
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;
|
|
`,fre=nt({opSnippet:hre}),mre={kernelName:Bl,backendName:"webgl",kernelFunc:fre},gre=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let d=[],u=Vk({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=R.getReshaped(u.shape,s,o,!1),c=R.getPermuted(p.length,s.length,!1),h=R.getReshapedPermuted(u.shape,s,o,!1),f=we({inputs:{x:u},backend:n,attrs:{shape:p}}),m=_n({inputs:{x:f},backend:n,attrs:{perm:c}}),g=we({inputs:{x:m},backend:n,attrs:{shape:h}});return d.push(u),d.push(f),d.push(m),d.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},yre={kernelName:Vl,backendName:"webgl",kernelFunc:gre};function Are(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let o=n.readSync(a.dataId),l=n.readSync(r.dataId),d=n.readSync(s.dataId),u=n.readSync(i.dataId)[0],[p,c,h,f,m]=bZ(o,a.shape,a.dtype,l,r.dtype,d,u);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],a.dtype,new Int32Array(m))]}var xre={kernelName:fh,backendName:"webgl",kernelFunc:Are};function bre(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),l=Array.from(n.readSync(s.dataId)),[d,u,p]=vZ(o,a.shape,a.dtype,i,l);return[n.makeTensorInfo(u,a.dtype,d),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var vre={kernelName:mh,backendName:"webgl",kernelFunc:bre};function wre(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[d,u]=G4(i,a.shape,a.dtype,o,l,!0);return n.makeTensorInfo(u,a.dtype,d)}var kre={kernelName:gh,backendName:"webgl",kernelFunc:wre};function Ire(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),l=n.readSync(s.dataId),[d,u]=G4(i,a.shape,a.dtype,o,l);return n.makeTensorInfo(u,a.dtype,d)}var Tre={kernelName:yh,backendName:"webgl",kernelFunc:Ire};function Sre(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:d,strides:u,outputSize:p}=R.calculateShapes(s,r,o),c=!1,h=new jk(d,l,r.shape.length,s.shape.length,u,[p,1],c),f=n.runWebGLProgram(h,[s,r,i],s.dtype),m=we({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var Nre={kernelName:Ah,backendName:"webgl",kernelFunc:Sre};function Cre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=R.prepareSplitSize(r,s,o),d=r.shape.length,u=new Array(d).fill(0),p=r.shape.slice();return l.map(c=>{let h=[...p];h[o]=c;let f=Lu({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=c,f})}var Ere={kernelName:Ul,backendName:"webgl",kernelFunc:Cre},Hk="return sqrt(x);",Rre=nt({opSnippet:Hk,packedOpSnippet:Hk,cpuKernelImpl:wZ}),Mre={kernelName:zi,backendName:"webgl",kernelFunc:Rre},Fre="return x * x;",Dre=nt({opSnippet:Fre}),$re={kernelName:Ed,backendName:"webgl",kernelFunc:Dre},qk="return (a - b) * (a - b);",Ore=vn({opSnippet:qk,packedOpSnippet:qk}),_re={kernelName:Bi,backendName:"webgl",kernelFunc:Ore};function Pre({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=lr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Fs(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var zre={kernelName:ps,backendName:"webgl",kernelFunc:Pre},Lre=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=yt(n.length),s=yt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,d)=>(o++,n.length===1?`coords * strides[${d}] + begin[${d}]`:`coords[${o-1}] * strides[${d}] + begin[${d}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function Wre(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:d,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=En.sliceInfo(r.shape,s,i,o,l,d,u,p,c),x=we({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=Lu({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=we({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(k)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let k=n.texData.get(x.dataId).values,N=Ge(x.shape,x.dtype,k),C=kZ(A,N,m,f);b=n.makeTensorInfo(A,x.dtype,C.values)}else{let k=new Lre(f,m,A);b=n.runWebGLProgram(k,[x],x.dtype)}let v=we({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var Bre={kernelName:jl,backendName:"webgl",kernelFunc:Wre};function Vre(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:d}=a,{data:u,dataSplits:p}=t,c=n.readSync(u.dataId),h=n.readSync(p.dataId),[f,m]=IZ(c,h,r,s,i,o,l,d);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Ure={kernelName:xh,backendName:"webgl",kernelFunc:Vre};function jre(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),l=n.readSync(i.dataId)[0],[d,u,p]=TZ(o,l,r),c=u.length;return[n.makeTensorInfo([c,2],"int32",d),n.makeTensorInfo([c],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Gre={kernelName:bh,backendName:"webgl",kernelFunc:jre};function Hre(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=SZ(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var qre={kernelName:vh,backendName:"webgl",kernelFunc:Hre},Xre="return tan(x);",Kre=nt({opSnippet:Xre}),Zre={kernelName:Ui,backendName:"webgl",kernelFunc:Kre},Yre=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Jre=nt({opSnippet:Yre}),Qre={kernelName:ji,backendName:"webgl",kernelFunc:Jre},ese=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=yt(this.rank),r=tse(e);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function tse(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function Xk(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),l=r.dtype==="string"?o.map(p=>w.decodeString(p)):o,d=Ge(r.shape,r.dtype,l),u=CZ(d,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new ese(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var nse={kernelName:ds,backendName:"webgl",kernelFunc:Xk},ase=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced above,
|
|
// Figure5(a) shows that element[1] is in the
|
|
// second half of the group when group size is 2, but it is in the
|
|
// first half of the group when group size is 4.
|
|
|
|
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
|
|
int i = isFirstInPair ? elemIdx : elemIdx - inc;
|
|
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
|
|
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
|
|
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
|
|
|
|
// Denotes which direction indices are in (ascending or descending).
|
|
bool reverse = imod(elemIdx, 2 * dir) >= dir;
|
|
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) { // Elements in opposite order of direction
|
|
int iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutput(float(i0));
|
|
} else {
|
|
setOutput(float(i1));
|
|
}
|
|
}
|
|
`}},rse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
|
|
// we only need to output the indices at positions |, the indices at
|
|
// positions _ can be thrown away, see Figure5(b) After Phase 2
|
|
// (Merge phase) in the Bitonic Top K paper referenced above.
|
|
// For example, the paper shows we only need to output the orange bars.
|
|
// The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back
|
|
// to the previous sequence to find the corresponding value,
|
|
// we need to double the index. When we double the index,
|
|
// we basically interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
|
|
// of each 2k positions by - elemIdx % k. E.g. for output at
|
|
// index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
|
|
|
|
float x0 = getX(batch, i0);
|
|
float x1 = i1 < n ? getX(batch, i1) : x0;
|
|
|
|
setOutput(x0 >= x1 ? float(i0) : float(i1));
|
|
}
|
|
`}};function Eo(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Kk(e){let t=1;for(;t<e;)t*=2;return t}function sse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=ne().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=ne().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),d=r.shape,u=d[d.length-1];if(n.shouldExecuteOnCPU([r])||u<o||s>l){let E=n.readSync(r.dataId),[O,D]=EZ(E,d,r.dtype,s,i);return[n.makeTensorInfo(O.shape,O.dtype,O.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(s===0)return d[d.length-1]=0,[n.makeTensorInfo(d,r.dtype,[]),n.makeTensorInfo(d,"int32",[])];if(u===1)return[r,Vp({attrs:{shape:d,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),c=p!==null&&p.isPacked,h=c?n.unpackTensor(r):r,f=w.sizeFromShape(d)/u,m=we({inputs:{x:h},attrs:{shape:[f,u]},backend:n});c&&Eo(n,h);let g=Kk(s),y=Kk(u),A=null,x=()=>A===null?[m,m]:[m,A],b=(E,O,D)=>{let T=x(),P=new ase(D),_=[[u],[A===null?1:0],[Number.NEGATIVE_INFINITY],[E],[O]],j=A;A=n.runWebGLProgram(P,T,"int32",_),Eo(n,j)};for(let E=1;E<g;E*=2){let O=E*2;for(let D=E;D>=1;D/=2)b(O,D,[f,y])}for(let E=y;E>g;E/=2){let O=x(),D=new rse([f,E/2]),T=[[u],[A===null?1:0],[g]],P=A;A=n.runWebGLProgram(D,O,"int32",T),Eo(n,P);let _=g/2,j=_*2;for(let q=_;q>=1;q/=2)b(j,q,A.shape)}let v=A;A=Lu({inputs:{x:A},backend:n,attrs:{begin:0,size:[f,s]}}),Eo(n,v);let k=$k({inputs:{x:m,indices:A},backend:n,attrs:{axis:1,batchDims:1}});Eo(n,m);let N=d.slice(0,-1);N.push(s),v=A,A=we({inputs:{x:A},attrs:{shape:N},backend:n}),Eo(n,v);let C=k;return k=we({inputs:{x:k},attrs:{shape:N},backend:n}),Eo(n,C),[k,A]}var ise={kernelName:Gl,backendName:"webgl",kernelFunc:sse},ose=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${o} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
} else {
|
|
return outCoord;
|
|
}
|
|
}
|
|
|
|
float readWithFillValue(int batch, int coordY, int coordX,
|
|
int channel) {
|
|
float outputValue;
|
|
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
float outputValue;
|
|
int batch = coords[0];
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
int channel = coords[3];
|
|
float xf = float(x);
|
|
float yf = float(y);
|
|
float a1 = getTransforms(batch, 0);
|
|
float a2 = getTransforms(batch, 1);
|
|
float a3 = getTransforms(batch, 2);
|
|
float b1 = getTransforms(batch, 3);
|
|
float b2 = getTransforms(batch, 4);
|
|
float b3 = getTransforms(batch, 5);
|
|
float c1 = getTransforms(batch, 6);
|
|
float c2 = getTransforms(batch, 7);
|
|
float projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = float(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${i} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function lse(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:d}=a,[u,p,c,h]=r.shape,[f,m]=d!=null?d:[p,c],g=[u,f,m,h],y=new ose(p,c,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var use={kernelName:Hl,backendName:"webgl",kernelFunc:lse};function dse(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Mu(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:d}=RZ(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([d.length],"int32",d)]}var pse={kernelName:wh,backendName:"webgl",kernelFunc:dse};function cse(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],d=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(d[u++]=i.shape[m]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){c[s]=m;let g=Lu({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=we({inputs:{x:g},backend:n,attrs:{shape:d}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var hse={kernelName:ql,backendName:"webgl",kernelFunc:cse},fse=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",d=Math.floor(n/4)*4,u=n%4,p=`
|
|
sumValue += dot(values, segFilter);
|
|
`,c="";r%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${s})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${s})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${d}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${d};
|
|
if (${u===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${p}
|
|
} else if (${u===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
0
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function mse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],d=0,u=R.getAxesPermutation([d],o),p=r;u!=null&&(p=_n({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),d=R.getInnerMostAxes(1,o)[0]);let c=R.segment_util.computeOutShape(p.shape,d,i),h=w.sizeFromShape([p.shape[d]]),f=we({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Eh(r.dtype),g=(b,v,k,N,C)=>{let E=b.shape[0],O=b.shape[1],D=R.segment_util.segOpComputeOptimalWindowSize(O,C),T={windowSize:D,inSize:O,batchSize:E,numSegments:C},P=new fse(T,v),_=n.compileAndRun(P,[b,k],N);if(l.push(_),_.shape[1]===C)return _;let j=Uk({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),q=Xk({inputs:{x:j},backend:n,attrs:{reps:[O/D]}});return l.push(j),l.push(q),g(_,v,q,N,C)},y=g(f,"unsortedSegmentSum",s,m,i),A=we({inputs:{x:y},backend:n,attrs:{shape:c}}),x=A;if(u!=null){l.push(A);let b=R.getUndoAxesPermutation(u);x=_n({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var gse={kernelName:Rd,backendName:"webgl",kernelFunc:mse},yse=[Gte,Xte,EY,MY,$Y,PY,LY,VY,jY,HY,ZY,JY,tJ,rJ,pJ,oJ,fJ,AJ,gJ,wJ,IJ,SJ,RJ,PJ,LJ,BJ,qJ,KJ,QJ,nQ,dY,oQ,yQ,xQ,pQ,kQ,TQ,vQ,CQ,MQ,$Q,_Q,zQ,BQ,qQ,KQ,UQ,JQ,tee,aee,oee,pee,mee,Aee,xee,bee,wee,Iee,See,Cee,Ree,$ee,Pee,Wee,Vee,Gee,Xee,Jee,nte,uY,rte,sQ,ote,dte,hte,cY,yte,vte,kte,Rte,Nte,$te,Pte,Bte,Zte,rne,nne,lne,dne,cne,ene,fne,gne,bne,Ine,Cne,_ne,yY,zne,Bne,jne,qne,UJ,Zne,Jne,eae,aae,oae,fY,uae,dae,jJ,Fne,hae,vae,yae,xY,Tae,Cae,Fae,Oae,Lae,Bae,jae,qae,Kae,Jae,tre,are,ire,ure,cre,OJ,$ne,mre,yre,xre,vre,kre,Tre,Nre,Ere,Mre,$re,_re,zre,Bre,Ure,Gre,qre,Dne,SY,Zre,Qre,nse,ise,use,NY,pse,hse,gse,Yne];for(let e of yse)cs(e);var Kt;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Kt||(Kt={}));var Up;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Up||(Up={}));var Zk;function Ase(e){Zk=e.wasm.cwrap(Hi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function xse(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:d,activation:u,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let C=n.dataIdMap.get(i.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);f=C.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Up[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],A=d?s.shape[1]:s.shape[2],x=r.shape[0],b=n.makeOutput([x,y,A],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return Zk(c,k,r.shape.length,h,N,s.shape.length,l,d,g,f,m,p||0,v),b}var bse={kernelName:Hi,backendName:"wasm",setupFunc:Ase,kernelFunc:xse};function wn(e,t){let n;function a(s){n=s.wasm.cwrap(e,null,["number","number","number"])}function r(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,d=i.makeOutput(o.shape,t||o.dtype),u=i.dataIdMap.get(d.dataId).id;return w.sizeFromShape(d.shape)===0||n(l,Kt[o.dtype],u),d}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:r}}var vse=wn(Ko);function Pn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:d,b:u}=l,p=o.dataIdMap.get(d.dataId).id,c=o.dataIdMap.get(u.dataId).id,h=n!=null?n:d.dtype,f=R.assertAndGetBroadcastShape(d.shape,u.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(d.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),A=o.dataIdMap.get(m.dataId).id,x=()=>a(p,g,d.shape.length,c,y,u.shape.length,Kt[d.dtype],A);if(t&&d.dtype==="float32")return x(),m;let b=R.getBroadcastDims(d.shape,f),v=R.getBroadcastDims(u.shape,f),k=b.every((C,E)=>C===E),N=v.every((C,E)=>C===E);if(k&&N)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${d.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var wse=!0,kse=Pn(ls,wse),Yk;function Ise(e){Yk=e.wasm.cwrap(Zs,null,["array","number","number","number"])}function Tse(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return Yk(s,r.length,Kt[a.dtype],i),a}var Sse={kernelName:Zs,backendName:"wasm",setupFunc:Ise,kernelFunc:Tse};function Cf(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Nse={kernelName:mi,backendName:"wasm",kernelFunc:Cf},Jk;function Cse(e){Jk=e.wasm.cwrap(Gi,null,["number","array","number","number","number","array","number"])}function Vu(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Rse(t.x.shape,a.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=Ese(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=Cf({inputs:t,backend:n});return f.shape=o,f}let d=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(d.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return Jk(u,h,l.shape.length,Kt[l.dtype],p,c,s.length),d}function Ese(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Rse(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Mse={kernelName:Gi,backendName:"wasm",kernelFunc:Vu,setupFunc:Cse};function $s(e,t,n){let a=e.shape,r=e.shape.length,s=w.parseAxisParam(t,a),i=s,o=R.getAxesPermutation(i,r),l=null,d=!1;if(o!=null){let u=new Array(r);for(let c=0;c<u.length;c++)u[c]=a[o[c]];i=R.getInnerMostAxes(i.length,r),l=Vu({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(d=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:d}}var Qk;function Fse(e){Qk=e.wasm.cwrap(Jo,null,["number, number, number"])}function Dse(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:c}=$s(i,r,t);if(c){let A=t.dataIdMap.get(d.dataId).id;l=d,o=A}let h=l.shape.length;R.assertAxesAreInnerMostDims("all",u,h);let[f,m]=R.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;Qk(o,g,A)}if(c&&t.disposeData(d.dataId),s){let A=R.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var $se={kernelName:Jo,backendName:"wasm",setupFunc:Fse,kernelFunc:Dse},e8;function Ose(e){e8=e.wasm.cwrap(Qo,null,["number, number, number"])}function _se(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:d,axes:u,originalAxes:p,inputWasTransposed:c}=$s(i,r,t);if(c){let A=t.dataIdMap.get(d.dataId).id;l=d,o=A}let h=l.shape.length;R.assertAxesAreInnerMostDims("any",u,h);let[f,m]=R.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;e8(o,g,A)}if(c&&t.disposeData(d.dataId),s){let A=R.expandShapeToKeepDim(y.shape,p);y.shape=A}return y}var Pse={kernelName:Qo,backendName:"wasm",setupFunc:Ose,kernelFunc:_se},t8;function zse(e){t8=e.wasm.cwrap(Ys,null,["number","number","number","number","number"])}function Lse(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:d,axes:u,inputWasTransposed:p}=$s(s,r,t);if(p){let y=t.dataIdMap.get(d.dataId).id;y!==i&&(l=d,o=y)}let c=l.shape.slice(0,-1),h=t.makeOutput(c,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=l.shape[u[0]];return t8(o,Kt[l.dtype],m,g,f),p&&t.disposeData(d.dataId),h}var Wse={kernelName:Ys,backendName:"wasm",kernelFunc:Lse,setupFunc:zse},n8;function Bse(e){n8=e.wasm.cwrap(Js,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vse(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:d}=n,u=R.computePool2DInfo(r.shape,i,o,1,l,d),p=u.filterHeight,c=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,A=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=a.makeOutput(u.outShape,"float32"),v=a.dataIdMap.get(b.dataId).id;return n8(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,f,m,g,y,A,x,v),b}var Use={kernelName:Js,backendName:"wasm",setupFunc:Bse,kernelFunc:Vse};function ta(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=w.sizeFromShape(a.shape),i=w.inferFromImplicitShape(r,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var jse={kernelName:$l,backendName:"wasm",kernelFunc:ta},a8;function Gse(e){a8=e.wasm.cwrap(Qs,null,["number","array","number","number","array","number","number","number","number"])}function Hse(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,d=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[d-1]:s.shape[d-2],c=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[d-2]:s.shape[d-1],f=r.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=g===y||g===1||y===1;w.assert(l>=2&&d>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);w.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[g,u,c]:[g,c,u],v=o?[y,h,p]:[y,p,h],k=ta({inputs:{x:r},backend:n,attrs:{shape:b}}),N=ta({inputs:{x:s},backend:n,attrs:{shape:v}}),C=n.dataIdMap.get(k.dataId).id,E=n.dataIdMap.get(N.dataId).id,O=i?k.shape[2]:k.shape[1],D=o?N.shape[1]:N.shape[2],T=Math.max(g,y),P=n.makeOutput([T,O,D],k.dtype),_=n.dataIdMap.get(P.dataId).id,j=new Uint8Array(new Int32Array(k.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return a8(C,j,k.shape.length,E,q,N.shape.length,i,o,_),n.disposeData(k.dataId),n.disposeData(N.dataId),P.shape=x,P}var qse={kernelName:Qs,backendName:"wasm",setupFunc:Gse,kernelFunc:Hse};function jp(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=En.parseSliceParams(t,n,a),o=En.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),d=r.makeOutput(i,t.dtype),u=w.computeStrides(t.shape),p=r.dataIdMap.get(d.dataId);if(o){let f=En.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(d).set(l.subarray(f,f+w.sizeFromShape(i))),d}if(t.dtype==="string"){let f=rf(l,s,i,t.shape,t.dtype);return p.stringBytes=f,d}let c=r.typedArrayFromHeap(d),h=t.shape.length;if(h===2)Xse(l,u[0],c,s,i);else if(h===3)Kse(l,u[0],u[1],c,s,i);else if(h===4)Zse(l,u[0],u[1],u[2],c,s,i);else{let f=rf(l,s,i,t.shape,t.dtype);c.set(f)}return d}function Xse(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let d=i;d<l;d++){let u=d*t+o;n.set(e.subarray(u,u+r[1]),s),s+=r[1]}}function Kse(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],d=r[2],u=o+s[0],p=l+s[1];for(let c=o;c<u;c++)for(let h=l;h<p;h++){let f=c*t+h*n+d;a.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function Zse(e,t,n,a,r,s,i){let o=0,l=s[0],d=s[1],u=s[2],p=l+i[0],c=d+i[1],h=u+i[2],f=s[3];for(let m=l;m<p;m++)for(let g=d;g<c;g++)for(let y=u;y<h;y++){let A=m*t+g*n+y*a+f;r.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Yse={kernelName:zl,backendName:"wasm",kernelFunc:jp};function Jse(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a,o=s.reduce((y,A)=>y*A),l=R.getReshaped(r.shape,s,o),d=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(u,i,s.length),h=ta({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Vu({inputs:{x:h},backend:n,attrs:{perm:d}}),m=ta({inputs:{x:f},backend:n,attrs:{shape:u}}),g=jp({inputs:{x:m},backend:n,attrs:{begin:p,size:c}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Qse={kernelName:sl,backendName:"wasm",kernelFunc:Jse};function Gp(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var eie={kernelName:ei,backendName:"wasm",kernelFunc:Gp},tie=wn(ti),r8;function nie(e){r8=e.wasm.cwrap(us,null,["number","number","number","number"])}function aie(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),d=n.dataIdMap.get(l.dataId).id;return r8(o,s,i,d),l}var rie={kernelName:us,backendName:"wasm",setupFunc:nie,kernelFunc:aie};function s8(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=R.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>w.sizeFromShape(h.shape)>0);if(s.length===1)return Cf({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(R.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let b=w.sizeFromShape(x.shape.slice(a));return ta({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=R.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=y2(f,r,t[0].dtype,m),y=R.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let A=n.dataIdMap.get(i.dataId);return A.stringBytes=R.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,a)),d=0,u=s.map(h=>{let f=w.sizeFromShape(h.shape.slice(a));return d+=f,f}),p=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let f=h*d;for(let m=0;m<p.length;m++){let g=u[m],y=h*g,A=p[m].subarray(y,y+g);c.set(A,f),f+=g}}return i}var sie={kernelName:il,backendName:"wasm",kernelFunc:s8},i8;function iie(e){i8=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oie(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:d,pad:u,dimRoundingMode:p,dataFormat:c}=n,h=R.convertConv2DDataFormat(c),f=R.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,N=f.strideHeight,C=f.strideWidth,E=f.inChannels,O=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Soe={kernelName:wi,backendName:"wasm",setupFunc:Ioe,kernelFunc:Toe},Noe=!1,Coe=Pn(ki,Noe),J2;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(J2||(J2={}));var I8;function Eoe(e){I8=e.wasm.cwrap(Ii,null,["number","array","number","number","array","array","number","number"])}function Roe(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(f=>f[0]),p=a.map(f=>f[1]),c=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return I8(i,d,t.shape.length,Kt[t.dtype],c,h,J2[r],l),o}var Moe={kernelName:Ii,backendName:"wasm",kernelFunc:Roe,setupFunc:Eoe},Foe=!0,Doe=Pn(Ti,Foe),$oe=wn(Tl);function Q2(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return 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x=R.expandShapeToKeepDim(A.shape,c);A.shape=x}return A}var aue={kernelName:Li,backendName:"wasm",setupFunc:tue,kernelFunc:nue},rue=wn(Ui),sue=wn(ji),U8;function iue(e){U8=e.wasm.cwrap(ds,null,["number","array","number","array","number","number"])}function oue(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,s=n.dataIdMap.get(r.dataId).id,{reps:i}=a,o=new Array(r.shape.length);for(let c=0;c<o.length;c++)o[c]=r.shape[c]*i[c];let l=new Uint8Array(new Int32Array(r.shape).buffer),d=new Uint8Array(new Int32Array(o).buffer),u=n.makeOutput(o,r.dtype),p=n.dataIdMap.get(u.dataId).id;return U8(s,l,r.shape.length,d,o.length,Kt[u.dtype],p),u}var lue={kernelName:ds,backendName:"wasm",setupFunc:iue,kernelFunc:oue},j8;function uue(e){j8=e.wasm.cwrap(Gl,null,["number","array","number","number","number","bool","number","number"])}var due=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{k:r,sorted:s}=n,i=t.dataIdMap.get(a.dataId).id,o=new Uint8Array(new Int32Array(a.shape).buffer),l=a.shape.slice();l[l.length-1]=r;let d=t.makeOutput(l,a.dtype),u=t.dataIdMap.get(d.dataId).id,p=t.makeOutput(l,"int32"),c=t.dataIdMap.get(p.dataId).id;return j8(i,o,a.shape.length,Kt[a.dtype],r,s,u,c),[d,p]},pue={kernelName:Gl,backendName:"wasm",setupFunc:uue,kernelFunc:due},G8;function cue(e){G8=e.wasm.cwrap(Hl,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function hue(e){let{backend:t,inputs:n,attrs:a}=e,{image:r,transforms:s}=n,{interpolation:i,fillMode:o,fillValue:l,outputShape:d}=a,[u,p,c,h]=r.shape,[f,m]=d!=null?d:[p,c],g=[u,f,m,h],y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),A=t.makeOutput(g,r.dtype),x=t.dataIdMap.get(A.dataId).id,b=t.dataIdMap.get(r.dataId).id,v=t.dataIdMap.get(s.dataId).id,k=i==="nearest"?1:2,N;switch(o){case"constant":N=1;break;case"reflect":N=2;break;case"wrap":N=3;break;case"nearest":N=4;break;default:N=1;break}return G8(b,v,s.shape[0]>1,u,f,m,h,c,p,y,r.shape.length-1,k,N,l,x),A}var fue={kernelName:Hl,backendName:"wasm",setupFunc:cue,kernelFunc:hue};function mue(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r.shape[s],o=r.shape.length,l=new Array(o-1),d=0;for(let h=0;h<o;h++)h!==s&&(l[d++]=r.shape[h]);let u=new Array(i),p=new Array(o).fill(0),c=r.shape.slice();c[s]=1;for(let h=0;h<u.length;h++)p[s]=h,u[h]=jp({inputs:{x:r},attrs:{begin:p,size:c},backend:n});return u.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var gue={kernelName:ql,backendName:"wasm",kernelFunc:mue};function yue(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(0),a}var Aue={kernelName:Xl,backendName:"wasm",kernelFunc:yue},xue=[vse,kse,Sse,$se,Pse,Wse,Use,qse,Qse,eie,tie,rie,sie,lie,pie,cie,hie,gie,xie,wie,Tie,Sie,Cie,Eie,Rie,Mie,$ie,Oie,Pie,bse,Wie,Uie,Hie,Kie,Jie,eoe,noe,Nse,soe,ooe,uoe,doe,coe,moe,yoe,boe,koe,Soe,Coe,Moe,Doe,$oe,Poe,Woe,Uoe,Goe,Xoe,Zoe,Joe,R8,nle,sle,lle,dle,cle,hle,fle,jse,yle,ble,kle,Tle,Ile,Cle,Mle,$le,Ole,Yse,zle,Wle,Vle,Ule,jle,Hle,Kle,Jle,eue,aue,rue,sue,lue,pue,fue,Mse,gue,Aue];for(let e of xue)cs(e);var ex=ne();ex.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ex.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ex.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var H8=qs(AS()),bue='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',vue=qs(xS()),q8=class extends fd{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(Z8),nx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Oc(this,sa())}write(e,t,n){let a={id:this.dataIdNextNumber++};return this.move(a,e,t,n,1),a}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}move(e,t,n,a,r){let s=this.dataIdNextNumber++;if(a==="string"){let d=t;this.dataIdMap.set(e,{id:s,stringBytes:d,shape:n,dtype:a,memoryOffset:null,refCount:r});return}let i=w.sizeFromShape(n),o=i*w.bytesPerElement(a),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:a,refCount:r}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:a,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let s=this.wasm.HEAPU8.slice(t,t+w.sizeFromShape(a)*w.bytesPerElement(n));return Iue(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let a;if(n==null)a=this.write(null,e,t);else{let r=this.dataIdNextNumber++;a={id:r},this.dataIdMap.set(a,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=w.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,s,n)}return{dataId:a,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let a=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),s=w.sizeFromShape(e);switch(t){case"float32":return new Float32Array(a,r,s);case"int32":return new Int32Array(a,r,s);case"bool":return new Uint8Array(a,r,s);default:throw new Error(`Unknown dtype ${t}`)}}};function wue(e){return(t,n)=>(w.fetch(e,{credentials:"same-origin"}).then(a=>{a.ok||t.env.a(`failed to load wasm binary file at '${e}'`),a.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(s=>{n(s.instance,s.module)})})}),{})}function X8(e,t,n){if(Ef!=null)return Ef;let a="tfjs-backend-wasm.wasm";return e&&t?a="tfjs-backend-wasm-threaded-simd.wasm":e&&(a="tfjs-backend-wasm-simd.wasm"),qp!=null&&qp[a]!=null?qp[a]:n+a}async function kue(){let[e,t]=await Promise.all([ne().getAsync("WASM_HAS_SIMD_SUPPORT"),ne().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,a)=>{let r={};r.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let d=bue,u=new Blob([d],{type:"application/javascript"});return URL.createObjectURL(u)}return o.endsWith(".wasm")?X8(e,t,Hp!=null?Hp:l):l+o},tx&&(r.instantiateWasm=wue(X8(e,t,Hp!=null?Hp:"")));let s=!1;r.onAbort=()=>{s||Xp||(Xp=!0,a({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Ef=e,tx=t}function K8(e,t=!1){if(Xp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Hp=e;else{qp=e;let n=Tue.filter(a=>qp[a]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}tx=t}var Z8=-1,nx=-1;function Nue(e){Z8=e}function Cue(){if(nx===-1)throw new Error("WASM backend not initialized.");return nx}var Eue="3.10.0",Rue=2;au("wasm",async()=>{let{wasm:e}=await kue();return new q8(e)},Rue);var Mue="3.10.0",Fue="3.10.0",Due="3.10.0",$ue="3.10.0",Oue="3.10.0",_ue="3.10.0",Pue="3.10.0",zue="3.10.0",Lue={tfjs:Mue,"tfjs-core":Fue,"tfjs-data":Due,"tfjs-layers":$ue,"tfjs-converter":Oue,"tfjs-backend-cpu":_ue,"tfjs-backend-webgl":Pue,"tfjs-backend-wasm":zue};var Y8=`
|
|
precision highp float;
|
|
attribute vec2 pos;
|
|
attribute vec2 uv;
|
|
varying vec2 vUv;
|
|
uniform float flipY;
|
|
void main(void) {
|
|
vUv = uv;
|
|
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
|
|
}
|
|
`;var J8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform float m[20];
|
|
void main(void) {
|
|
vec4 c = texture2D(texture, vUv);
|
|
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
|
|
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
|
|
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
|
|
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
|
|
}
|
|
`,Q8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform float m[20];
|
|
void main(void) {
|
|
vec4 c = texture2D(texture, vUv);
|
|
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
|
|
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
|
|
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
|
|
gl_FragColor.a = c.a;
|
|
}
|
|
`,e9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform vec2 size;
|
|
uniform sampler2D texture;
|
|
vec2 pixelate(vec2 coord, vec2 size) {
|
|
return floor( coord / size ) * size;
|
|
}
|
|
void main(void) {
|
|
gl_FragColor = vec4(0.0);
|
|
vec2 coord = pixelate(vUv, size);
|
|
gl_FragColor += texture2D(texture, coord);
|
|
}
|
|
`,t9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform vec2 px;
|
|
void main(void) {
|
|
gl_FragColor = vec4(0.0);
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
|
|
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
|
|
}
|
|
`,n9=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform vec2 px;
|
|
uniform float m[9];
|
|
void main(void) {
|
|
vec4 c11 = texture2D(texture, vUv - px); // top left
|
|
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
|
|
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
|
|
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
|
|
vec4 c22 = texture2D(texture, vUv); // mid center
|
|
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
|
|
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
|
|
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
|
|
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
|
|
gl_FragColor =
|
|
c11 * m[0] + c12 * m[1] + c22 * m[2] +
|
|
c21 * m[3] + c22 * m[4] + c23 * m[5] +
|
|
c31 * m[6] + c32 * m[7] + c33 * m[8];
|
|
gl_FragColor.a = c22.a;
|
|
}
|
|
`;var ax=(e,t,n)=>{let a=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(a,(r,s)=>(n[s]=0,r))},a9=class{constructor(t,n,a){ce(this,"uniform",{});ce(this,"attribute",{});ce(this,"gl");ce(this,"id");ce(this,"compile",(t,n)=>{let a=this.gl.createShader(n);if(this.gl.shaderSource(a,t),this.gl.compileShader(a),!this.gl.getShaderParameter(a,this.gl.COMPILE_STATUS))throw new Error(`filter: gl compile failed: ${this.gl.getShaderInfoLog(a)}`);return a});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),s=this.compile(a,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS))throw new Error(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);this.gl.useProgram(this.id),ax(n,"attribute",this.attribute);for(let i in 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t={model:"",flags:[]};if(this.node&&((n=this.platform)==null?void 0:n.startsWith("linux"))){let a=rs("fs");try{let r=a.readFileSync("/proc/cpuinfo").toString();for(let s of r.split(`
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ade=["angry","disgust","fear","happy","sad","surprise","neutral"],on,zf=[],E9=0,R9=0,Fx=Number.MAX_SAFE_INTEGER,Dx=[.2989,.587,.114];async function M9(e){var t,n;return ye.initial&&(on=null),on?e.debug&&se("cached model:",on.modelUrl):(on=await et(tt(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!on||!on.modelUrl?se("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&se("load model:",on.modelUrl)),on}async function $x(e,t,n,a){var i,o;if(!on)return null;let r=Fx<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>he()-R9;return t.skipAllowed&&s&&r&&E9===a&&zf[n]&&zf[n].length>0?(Fx++,zf[n]):(Fx=0,new Promise(async l=>{var u,p;let d=[];if((u=t.face.emotion)==null?void 0:u.enabled){let c=Me.resizeBilinear(e,[(on==null?void 0:on.inputs[0].shape)?on.inputs[0].shape[2]:0,(on==null?void 0:on.inputs[0].shape)?on.inputs[0].shape[1]:0],!1),[h,f,m]=cn(c,3,3);Q(c);let g=L(h,Dx[0]),y=L(f,Dx[1]),A=L(m,Dx[2]);Q(h),Q(f),Q(m);let x=_h([g,y,A]);Q(g),Q(y),Q(A);let b=G(()=>L(Ae(x,.5),2));Q(x);let v=await(on==null?void 0:on.predict(b));R9=he();let k=await v.data();Q(v);for(let N=0;N<k.length;N++)k[N]>(((p=t.face.emotion)==null?void 0:p.minConfidence)||0)&&d.push({score:Math.min(.99,Math.trunc(100*k[N])/100),emotion:ade[N]});d.sort((N,C)=>C.score-N.score),Q(b)}zf[n]=d,E9=a,l(d)}))}var Ga,Ls=0,rde=2.3,Ox=Nr.leftEyeLower0,_x=Nr.rightEyeLower0,Hu={leftBounds:[Ox[0],Ox[Ox.length-1]],rightBounds:[_x[0],_x[_x.length-1]]},qu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function F9(e){var t,n;return ye.initial&&(Ga=null),Ga?e.debug&&se("cached model:",Ga.modelUrl):(Ga=await et(tt(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!Ga||!Ga.modelUrl?se("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&se("load model:",Ga.modelUrl)),Ls=Ga.inputs[0].shape?Ga.inputs[0].shape[2]:0,Ls===-1&&(Ls=64),Ga}function Lf(e,t,n,a){for(let r=0;r<cx.length;r++){let{key:s,indices:i}=cx[r],o=Nr[`${n}${s}`];if(!a||a.includes(s))for(let l=0;l<i.length;l++){let d=i[l];e[o[l]]=[t[d][0],t[d][1],(t[d][2]+e[o[l]][2])/2]}}}var sde=e=>{let t=e[Hu.leftBounds[0]][2],n=e[Hu.rightBounds[0]][2];return t-n},D9=(e,t,n,a,r=!1,s)=>{let i=Qp(Jp($f([e[n],e[a]]),rde)),o=Yp(i),l=Me.cropAndResize(t,[[i.startPoint[1]/s,i.startPoint[0]/s,i.endPoint[1]/s,i.endPoint[0]/s]],[0],[Ls,Ls]);if(r&&ye.kernels.includes("flipleftright")){let d=Me.flipLeftRight(l);Q(l),l=d}return{box:i,boxSize:o,crop:l}},$9=(e,t,n,a=!1)=>{let r=[];for(let s=0;s<qu.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];r.push([(a?1-i/Ls:i/Ls)*n[0]+t.startPoint[0],o/Ls*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(qu.index)}},O9=(e,t,n)=>{let a=e[Nr[`${n}EyeUpper0`][qu.upperCenter]][2],r=e[Nr[`${n}EyeLower0`][qu.lowerCenter]][2],s=(a+r)/2;return t.map((i,o)=>{let l=s;return o===2?l=a:o===4&&(l=r),[i[0],i[1],l]})};async function 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A.boxes){let b=await x.box.startPoint.data(),v=await x.box.endPoint.data(),k=await x.landmarks.array();Cr.push({startPoint:b,endPoint:v,landmarks:k,confidence:x.confidence})}A.boxes.forEach(x=>Q([x.box.startPoint,x.box.endPoint,x.landmarks]));for(let x=0;x<Cr.length;x++){let b=p9({startPoint:Cr[x].startPoint,endPoint:Cr[x].endPoint},A.scaleFactor),v=Jp(b),k=Qp(v);Cr[x]={...k,confidence:Cr[x].confidence,landmarks:Cr[x].landmarks}}Px=0}else Px++;let r=[],s=[],i=0;for(let A of Cr){let x=0,b,v={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if(((d=t.face.detector)==null?void 0:d.rotation)&&((u=t.face.mesh)==null?void 0:u.enabled)&&ye.kernels.includes("rotatewithoffset"))[x,b,v.tensor]=gx(A,e,ur);else{b=Of;let k=mx({startPoint:A.startPoint,endPoint:A.endPoint},e,((p=t.face.mesh)==null?void 0:p.enabled)?[ur,ur]:[ec(),ec()]);v.tensor=fe(k,255),Q(k)}if(v.boxScore=Math.round(100*A.confidence)/100,(c=t.face.mesh)==null?void 0:c.enabled)if(!Ha)t.debug&&se("face mesh detection requested, but model is not loaded");else{let[k,N,C]=Ha.execute(v.tensor);Q(k);let E=(await N.data())[0];Q(N);let O=V(C,[-1,3]),D=await O.array();if(Q(C),Q(O),E<(((h=t.face.detector)==null?void 0:h.minConfidence)||1))A.confidence=E;else{((f=t.face.iris)==null?void 0:f.enabled)&&(D=await _9(D,v.tensor,t,ur)),v.mesh=g9(D,A,x,b,ur),v.meshRaw=v.mesh.map(T=>[T[0]/(e.shape[2]||0),T[1]/(e.shape[1]||0),(T[2]||0)/ur]),A={...Jp($f(v.mesh),1.5),confidence:A.confidence};for(let T of Object.keys(Nr))v.annotations[T]=Nr[T].map(P=>v.mesh[P]);((m=t.face.detector)==null?void 0:m.rotation)&&t.face.mesh.enabled&&((g=t.face.description)==null?void 0:g.enabled)&&ye.kernels.includes("rotatewithoffset")&&(Q(v.tensor),[x,b,v.tensor]=gx(A,e,ur)),v.box=hx(A,e),v.boxRaw=fx(A,e),v.score=Math.round(100*E||100*A.confidence||0)/100,v.faceScore=Math.round(100*E)/100,A={...Qp(A),confidence:A.confidence,faceConfidence:E}}}else{v.box=hx(A,e),v.boxRaw=fx(A,e),v.score=Math.round(100*A.confidence||0)/100,v.mesh=A.landmarks.map(k=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*k[0]/ec(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*k[1]/ec()]),v.meshRaw=v.mesh.map(k=>[k[0]/(e.shape[2]||0),k[1]/(e.shape[1]||0),(k[2]||0)/ur]);for(let k of Object.keys(Kp))v.annotations[k]=[v.mesh[Kp[k]]]}r.push(v),s.push(A)}return((y=t.face.mesh)==null?void 0:y.enabled)&&(Cr=s.filter(A=>{var x;return A.confidence>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)})),z9=r.length,r}async function W9(e){var t,n;return ye.initial&&(Ha=null),Ha?e.debug&&se("cached model:",Ha.modelUrl):(Ha=await et(tt(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!Ha||!Ha.modelUrl?se("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&se("load model:",Ha.modelUrl)),ur=Ha.inputs[0].shape?Ha.inputs[0].shape[2]:0,ur===-1&&(ur=64),Ha}var B9=Mo,V9=Zp;var Wn,Wf=[],U9=0,j9=0,zx=Number.MAX_SAFE_INTEGER;async function G9(e){var n,a;let t=tt(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return ye.initial&&(Wn=null),Wn?e.debug&&se("cached model:",t):(Wn=await et(t),Wn?e.debug&&se("load model:",t):se("load model failed:",((a=e.face.description)==null?void 0:a.modelPath)||"")),Wn}function Lx(e){return G(()=>{let n=e.image||e.tensor||e;if(!(n instanceof je))return null;let a=[[.05,.15,.85,.85]];if(!(Wn==null?void 0:Wn.inputs[0].shape))return null;let r=n.shape.length===3?Me.cropAndResize(Wt(n,0),a,[0],[Wn.inputs[0].shape[2],Wn.inputs[0].shape[1]]):Me.cropAndResize(n,a,[0],[Wn.inputs[0].shape[2],Wn.inputs[0].shape[1]]);return L(r,255)})}async function Wx(e,t,n,a){var i,o,l,d;if(!Wn)return null;let r=zx<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>he()-U9;return t.skipAllowed&&r&&s&&j9===a&&((l=Wf[n])==null?void 0:l.age)&&((d=Wf[n])==null?void 0:d.age)>0?(zx++,Wf[n]):(zx=0,new Promise(async u=>{var c,h;let p={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((c=t.face.description)==null?void 0:c.enabled){let f=Lx(e),m=await(Wn==null?void 0:Wn.predict(f));U9=he(),Q(f);let y=await(await m.find(E=>E.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(p.gender=y[0]<=.5?"female":"male",p.genderScore=Math.min(.99,A));let x=ka(m.find(E=>E.shape[1]===100),1),b=(await x.data())[0];Q(x);let k=await m.find(E=>E.shape[1]===100).data();p.age=Math.round(k[b-1]>k[b+1]?10*b-100*k[b-1]:10*b+100*k[b+1])/10;let N=m.find(E=>E.shape[1]===1024),C=N?await N.data():[];p.descriptor=Array.from(C),m.forEach(E=>Q(E))}Wf[n]=p,j9=a,u(p)}))}function Bf(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function tc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function H9(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return Me.cropAndResize(t,s,[0],n)}function q9(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:a,palmLandmarks:r,confidence:e.confidence}}function Vf(e,t=1.5){let n=tc(e),a=Bf(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function Uf(e){let t=tc(e),n=Bf(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function ide(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function X9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return ide(n)}var K9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ws(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function ode(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function Z9(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(Ws(e[r],ode(t,s)))}return n}function Bx(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=K9(t[0],t[1]),i=Z9(s,r),o=K9(-t[0],-t[1]);return Z9(i,o)}function Y9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-Ws(t[0],n),-Ws(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function Vx(e,t){return[Ws(e,t[0]),Ws(e,t[1])]}var 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a={};a.batched=this.model.predict(t),a.predictions=st(a.batched),a.scores=G(()=>st(Kn(De(a.predictions,[0,0],[-1,1]))));let r=await a.scores.data();a.boxes=De(a.predictions,[0,1],[-1,4]),a.norm=this.normalizeBoxes(a.boxes),a.nms=await Me.nonMaxSuppressionAsync(a.norm,a.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let s=await a.nms.array(),i=[];for(let o of s){let l=De(a.norm,[o,0],[1,-1]),d=G(()=>V(this.normalizeLandmarks(De(a.predictions,[o,5],[1,14]),o),[-1,2]));i.push({box:l,palmLandmarks:d,confidence:r[o]})}for(let o of Object.keys(a))Q(a[o]);return i}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=G(()=>Ae(fe(Me.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),i=await this.getBoxes(s,n);Q(s);let o=[];if(!i||i.length===0)return o;for(let l of i){let d=await l.box.data(),u=d.slice(0,2),p=d.slice(2,4),c=await l.palmLandmarks.array();Q(l.box),Q(l.palmLandmarks),o.push(q9({startPoint:u,endPoint:p,palmLandmarks:c,confidence:l.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};var lde=5,Q9=1.65,eI=[0,5,9,13,17,1,2],ude=0,dde=2,tI=0,jx=class{constructor(t,n){ce(this,"handDetector");ce(this,"handPoseModel");ce(this,"inputSize");ce(this,"storedBoxes");ce(this,"skipped");ce(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>Vx([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return Vf(Uf(r),lde)}getBoxForHandLandmarks(t){let 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d=this.storedBoxes[l];if(!!d)if(n.hand.landmarks){let u=n.hand.rotation?X9(d.palmLandmarks[ude],d.palmLandmarks[dde]):0,p=tc(d),c=[p[0]/t.shape[2],p[1]/t.shape[1]],h=n.hand.rotation&&ye.kernels.includes("rotatewithoffset")?Me.rotateWithOffset(t,u,0,c):t.clone(),f=Bx(-u,p),m=a?this.getBoxForPalmLandmarks(d.palmLandmarks,f):d,g=H9(m,h,[this.inputSize,this.inputSize]),y=fe(g,255);Q(g),Q(h);let[A,x]=await this.handPoseModel.predict(y);tI=he(),Q(y);let b=(await A.data())[0];if(Q(A),b>=n.hand.minConfidence/4){let v=V(x,[-1,3]),k=await v.array();Q(x),Q(v);let N=this.transformRawCoords(k,m,u,f),C=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...C,confidence:b};let E={landmarks:N,confidence:b,boxConfidence:d.confidence,fingerConfidence:b,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};o.push(E)}else this.storedBoxes[l]=null;Q(x)}else{let u=Vf(Uf(d),Q9),p={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};o.push(p)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>n.hand.maxDetected&&(o.length=n.hand.maxDetected),o}};var qe={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>qe.nameMapping[e],getPoints:e=>qe.pointsMapping[e]},na={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>na.nameMapping[e]},He={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>He.nameMapping[e]},jf=class{constructor(t){ce(this,"name");ce(this,"curls");ce(this,"directions");ce(this,"weights");ce(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}addCurl(t,n,a){typeof 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of[qe.index,qe.middle,qe.ring,qe.pinky])Bs.addCurl(e,na.full,1),Bs.addDirection(e,He.horizontalLeft,1),Bs.addDirection(e,He.horizontalRight,1);var Zt=new jf("victory");Zt.addCurl(qe.thumb,na.half,.5);Zt.addCurl(qe.thumb,na.none,.5);Zt.addDirection(qe.thumb,He.verticalUp,1);Zt.addDirection(qe.thumb,He.diagonalUpLeft,1);Zt.addCurl(qe.index,na.none,1);Zt.addDirection(qe.index,He.verticalUp,.75);Zt.addDirection(qe.index,He.diagonalUpLeft,1);Zt.addCurl(qe.middle,na.none,1);Zt.addDirection(qe.middle,He.verticalUp,1);Zt.addDirection(qe.middle,He.diagonalUpLeft,.75);Zt.addCurl(qe.ring,na.full,1);Zt.addDirection(qe.ring,He.verticalUp,.2);Zt.addDirection(qe.ring,He.diagonalUpLeft,1);Zt.addDirection(qe.ring,He.horizontalLeft,.2);Zt.addCurl(qe.pinky,na.full,1);Zt.addDirection(qe.pinky,He.verticalUp,.2);Zt.addDirection(qe.pinky,He.diagonalUpLeft,1);Zt.addDirection(qe.pinky,He.horizontalLeft,.2);Zt.setWeight(qe.index,2);Zt.setWeight(qe.middle,2);var nI=[Bs,Zt];var pde=.7,$o={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function aI(e,t,n,a){let r=(t-a)/(e-n),s=Math.atan(r)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function rI(e,t){if(!e||!t)return[0,0];let n=aI(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let a=aI(e[1],e[2],t[1],t[2]);return[n,a]}function sI(e,t=1){let n=0,a=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?a=1*t:r=1*t,[n,a,r]}function cde(e,t,n){let a=e[0]-t[0],r=e[0]-n[0],s=t[0]-n[0],i=e[1]-t[1],o=e[1]-n[1],l=t[1]-n[1],d=e[2]-t[2],u=e[2]-n[2],p=t[2]-n[2],c=Math.sqrt(a*a+i*i+d*d),h=Math.sqrt(r*r+o*o+u*u),f=Math.sqrt(s*s+l*l+p*p),m=(f*f+c*c-h*h)/(2*f*c);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>$o.NO_CURL_START_LIMIT?y=na.none:g>$o.HALF_CURL_START_LIMIT?y=na.half:y=na.full,y}function iI(e,t,n,a){let r;return a===Math.abs(e)?e>0?r=He.horizontalLeft:r=He.horizontalRight:a===Math.abs(t)?t>0?r=He.horizontalLeft:r=He.horizontalRight:n>0?r=He.horizontalLeft:r=He.horizontalRight,r}function oI(e,t,n,a){let r;return a===Math.abs(e)?e<0?r=He.verticalDown:r=He.verticalUp:a===Math.abs(t)?t<0?r=He.verticalDown:r=He.verticalUp:n<0?r=He.verticalDown:r=He.verticalUp,r}function hde(e,t,n,a,r,s,i,o){let l,d=oI(e,t,n,a),u=iI(r,s,i,o);return d===He.verticalUp?u===He.horizontalLeft?l=He.diagonalUpLeft:l=He.diagonalUpRight:u===He.horizontalLeft?l=He.diagonalDownLeft:l=He.diagonalDownRight,l}function fde(e,t,n,a){let r=e[0]-t[0],s=e[0]-n[0],i=t[0]-n[0],o=e[1]-t[1],l=e[1]-n[1],d=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(s),Math.abs(i)),p=Math.max(Math.abs(o),Math.abs(l),Math.abs(d)),c=0,h=0,f=0,m=p/(u+1e-5);m>1.5?c+=$o.DISTANCE_VOTE_POWER:m>.66?h+=$o.DISTANCE_VOTE_POWER:f+=$o.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+d*d),x=Math.max(g,y,A),b=e[0],v=e[1],k=n[0],N=n[1];x===g?(k=n[0],N=n[1]):x===A&&(b=t[0],v=t[1]);let O=rI([b,v],[k,N]),D=sI(O,$o.TOTAL_ANGLE_VOTE_POWER);c+=D[0],h+=D[1],f+=D[2];for(let P of a){let _=sI(P,$o.SINGLE_ANGLE_VOTE_POWER);c+=_[0],h+=_[1],f+=_[2]}let T;return c===Math.max(c,h,f)?T=oI(l,o,d,p):f===Math.max(h,f)?T=iI(s,r,i,u):T=hde(l,o,d,p,s,r,i,u),T}function lI(e){let t=[],n=[],a=[],r=[];if(!e)return{curls:a,directions:r};for(let s of qe.all){let i=qe.getPoints(s),o=[],l=[];for(let d of i){let u=e[d[0]],p=e[d[1]],c=rI(u,p),h=c[0],f=c[1];o.push(h),l.push(f)}t.push(o),n.push(l)}for(let s of qe.all){let i=s===qe.thumb?1:0,o=qe.getPoints(s),l=e[o[i][0]],d=e[o[i+1][1]],u=e[o[3][1]],p=cde(l,d,u),c=fde(l,d,u,t[s].slice(i));a[s]=p,r[s]=c}return{curls:a,directions:r}}function Gf(e){if(!e||e.length===0)return null;let t=lI(e),n={};for(let a of 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o=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let d=Gf(i);a.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:d})}return a}async function Hx(e){var n,a,r,s,i,o;ye.initial&&(Hr=null,qr=null),!Hr||!qr?([Hr,qr]=await Promise.all([e.hand.enabled?et(tt(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((a=e.hand.detector)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?et(tt(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((s=e.hand.skeleton)==null?void 0:s.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!Hr||!Hr.modelUrl?se("load model failed:",((i=e.hand.detector)==null?void 0:i.modelPath)||""):e.debug&&se("load model:",Hr.modelUrl),!qr||!qr.modelUrl?se("load model failed:",((o=e.hand.skeleton)==null?void 0:o.modelPath)||""):e.debug&&se("load model:",qr.modelUrl))):(e.debug&&se("cached model:",Hr.modelUrl),e.debug&&se("cached model:",qr.modelUrl));let t=new Ux(Hr);return pI=new jx(t,qr),[Hr,qr]}function Oo(e,t=[1,1]){let n=[e.map(o=>o[0]),e.map(o=>o[1])],a=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[a[0],a[1],r[0]-a[0],r[1]-a[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function cI(e,t=[1,1]){let n=[e.map(d=>d[0]),e.map(d=>d[1])],a=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(a[0]+r[0])/2,(a[1]+r[1])/2],i=Math.max(s[0]-a[0],s[1]-a[1],-s[0]+r[0],-s[1]+r[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function Hf(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function qx(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var kt=[null,null],mde=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Vs=[[0,0],[0,0]],gde=["hand","fist","pinch","point","face","tip","pinchtip"],hI=4,fI=1.6,yde=512,Ade=1.4,qf=Number.MAX_SAFE_INTEGER,Xx=0,Xr=[0,0],Ut={boxes:[],hands:[]},mI={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function gI(e){var t,n;if(ye.initial&&(kt[0]=null),kt[0])e.debug&&se("cached model:",kt[0].modelUrl);else{Gu(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),kt[0]=await et(tt(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let a=Object.values(kt[0].modelSignature.inputs);Vs[0][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Vs[0][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0,!kt[0]||!kt[0].modelUrl?se("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&se("load model:",kt[0].modelUrl)}return kt[0]}async function yI(e){var t,n;if(ye.initial&&(kt[1]=null),kt[1])e.debug&&se("cached model:",kt[1].modelUrl);else{kt[1]=await et(tt(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let a=Object.values(kt[1].modelSignature.inputs);Vs[1][0]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[1].size):0,Vs[1][1]=Array.isArray(a)?parseInt(a[0].tensorShape.dim[2].size):0,!kt[1]||!kt[1].modelUrl?se("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&se("load model:",kt[1].modelUrl)}return kt[1]}async function xde(e,t){let n=[];if(!e||!kt[0])return n;let a={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,yde),i=Math.round(s*r/8)*8;a.resize=Me.resizeBilinear(e,[s,i]),a.cast=pe(a.resize,"int32"),[a.rawScores,a.rawBoxes]=await kt[0].executeAsync(a.cast,mde),a.boxes=st(a.rawBoxes,[0,2]),a.scores=st(a.rawScores,[0]);let o=Mn(a.scores,1);Q(o[hI]),o.splice(hI,1),a.filtered=xn(o,1),Q(o),a.max=Rn(a.filtered,1),a.argmax=ka(a.filtered,1);let l=0;a.nms=await Me.nonMaxSuppressionAsync(a.boxes,a.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let d=await a.nms.data(),u=await a.max.data(),p=await a.argmax.data();for(let c of Array.from(d)){let h=De(a.boxes,c,1),f=await h.data();Q(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=Hf(m,Ade),y=qx(g),A=[Math.trunc(m[0]*Xr[0]),Math.trunc(m[1]*Xr[1]),Math.trunc(m[2]*Xr[0]),Math.trunc(m[3]*Xr[1])],x=u[c],b=gde[p[c]],v={id:l++,score:x,box:A,boxRaw:g,boxCrop:y,label:b};n.push(v)}return Object.keys(a).forEach(c=>Q(a[c])),n.sort((c,h)=>h.score-c.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Kx(e,t,n){let a={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&kt[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=Me.cropAndResize(e,[t.boxCrop],[0],[Vs[1][0],Vs[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=fe(r.cast,255),[r.score,r.keypoints]=kt[1].execute(r.div);let s=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(i>=(n.hand.minConfidence||0)){a.fingerScore=i,r.reshaped=V(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(u=>[u[0]/Vs[1][1],u[1]/Vs[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);a.keypoints=d.map(u=>[Xr[0]*(u[0]+t.boxRaw[0]),Xr[1]*(u[1]+t.boxRaw[1]),u[2]||0]),a.landmarks=Gf(a.keypoints);for(let u of Object.keys(mI))a.annotations[u]=mI[u].map(p=>a.landmarks&&a.keypoints[p]?a.keypoints[p]:null)}Object.keys(r).forEach(o=>Q(r[o]))}return a}async function Zx(e,t){var r,s;if(!kt[0]||!kt[1]||!((r=kt[0])==null?void 0:r.inputs[0].shape)||!((s=kt[1])==null?void 0:s.inputs[0].shape))return[];Xr=[e.shape[2]||0,e.shape[1]||0],qf++;let n=(t.hand.skipTime||0)>he()-Xx,a=qf<(t.hand.skipFrames||0);return t.skipAllowed&&n&&a?Ut.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>he()-Xx,l=qf<3*(t.hand.skipFrames||0);t.skipAllowed&&Ut.hands.length===t.hand.maxDetected?Ut.hands=await Promise.all(Ut.boxes.map(u=>Kx(e,u,t))):t.skipAllowed&&o&&l&&Ut.hands.length>0?Ut.hands=await Promise.all(Ut.boxes.map(u=>Kx(e,u,t))):(Ut.boxes=await xde(e,t),Xx=he(),Ut.hands=await Promise.all(Ut.boxes.map(u=>Kx(e,u,t))),qf=0);let d=[...Ut.boxes];if(Ut.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<Ut.hands.length;u++){let p=cI(Ut.hands[u].keypoints,Xr);if(p.box[2]/(e.shape[2]||1)>.05&&p.box[3]/(e.shape[1]||1)>.05&&Ut.hands[u].fingerScore&&Ut.hands[u].fingerScore>(t.hand.minConfidence||0)){let c=Hf(p.box,fI),h=Hf(p.boxRaw,fI),f=qx(h);Ut.boxes.push({...d[u],box:c,boxRaw:h,boxCrop:f})}}for(let u=0;u<Ut.hands.length;u++){let p=Oo(Ut.hands[u].keypoints,Xr);Ut.hands[u].box=p.box,Ut.hands[u].boxRaw=p.boxRaw}i(Ut.hands)})}var e5={};ud(e5,{connected:()=>Kf,horizontal:()=>Yx,kpt:()=>Xf,relative:()=>Qx,vertical:()=>Jx});var Xf=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Yx=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Jx=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Qx=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Kf={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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vde(e,t,n,a){let r=[];for(let s=0;s<e[0].length;s++){let i=e[0][s],o=Math.round(100*i[51+4])/100;if(o>t.body.minConfidence){let l=[];for(let c=0;c<17;c++){let h=i[3*c+2];if(h>t.body.minConfidence){let f=[(a[3]-a[1])*i[3*c+1]+a[1],(a[2]-a[0])*i[3*c+0]+a[0]];l.push({part:Xf[c],score:Math.round(100*h)/100,positionRaw:f,position:[Math.round((n.shape[2]||0)*f[0]),Math.round((n.shape[1]||0)*f[1])]})}}let d=Oo(l.map(c=>c.position),[n.shape[2],n.shape[1]]),u={};for(let[c,h]of Object.entries(Kf)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(A=>A.part===h[m]),y=l.find(A=>A.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}u[c]=f}let p={id:s,score:o,box:d.box,boxRaw:d.boxRaw,keypoints:[...l],annotations:u};t5(p),r.push(p)}}return r.sort((s,i)=>i.score-s.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function a5(e,t){if(!Sn||!(Sn==null?void 0:Sn.inputs[0].shape))return[];t.skipAllowed||(_o.boxes.length=0),n5++;let n=(t.body.skipTime||0)>he()-_o.last,a=n5<(t.body.skipFrames||0);return t.skipAllowed&&n&&a?_o.bodies:new Promise(async r=>{let s={};n5=0,s.input=bI(e,Zf),s.res=await(Sn==null?void 0:Sn.predict(s.input)),_o.last=he();let i=await s.res.array();_o.bodies=s.res.shape[2]===17?await bde(i,t,e,[0,0,1,1]):await vde(i,t,e,[0,0,1,1]);for(let o of _o.bodies)vI(o,[e.shape[2]||1,e.shape[1]||1]),xI(o.keypoints);Object.keys(s).forEach(o=>Q(s[o])),r(_o.bodies)})}var ya,Yf=[],kI=0,r5=Number.MAX_SAFE_INTEGER,Jf=2.5;async function II(e){if(!ya||ye.initial){ya=await et(tt(e.modelBasePath,e.object.modelPath||""));let t=Object.values(ya.modelSignature.inputs);if(ya.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!ya.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!ya||!ya.modelUrl?se("load model failed:",e.object.modelPath):e.debug&&se("load model:",ya.modelUrl)}else e.debug&&se("cached model:",ya.modelUrl);return ya}async function wde(e,t,n,a){let r=0,s=[];for(let d of[1,2,4])G(async()=>{var g,y;let u=d*13,p=(g=e.find(A=>A.shape[1]===u**2&&A.shape[2]===ju.length))==null?void 0:g.squeeze(),c=(y=e.find(A=>A.shape[1]===u**2&&A.shape[2]<ju.length))==null?void 0:y.squeeze(),f=await c.reshape([-1,4,c.shape[1]/4]).argMax(2).array(),m=await p.array();for(let A=0;A<p.shape[0];A++)for(let x=0;x<p.shape[1];x++){let b=m[A][x];if(b>a.object.minConfidence&&x!==61){let v=(.5+Math.trunc(A%u))/u,k=(.5+Math.trunc(A/u))/u,N=f[A].map(j=>j*(u/d/t)),[C,E]=[v-Jf/d*N[0],k-Jf/d*N[1]],[O,D]=[v+Jf/d*N[2]-C,k+Jf/d*N[3]-E],T=[C,E,O,D];T=T.map(j=>Math.max(0,Math.min(j,1)));let P=[T[0]*n[0],T[1]*n[1],T[2]*n[0],T[3]*n[1]],_={id:r++,score:Math.round(100*b)/100,class:x+1,label:ju[x].label,box:P.map(j=>Math.trunc(j)),boxRaw:T};s.push(_)}}});e.forEach(d=>Q(d));let i=s.map(d=>[d.boxRaw[1],d.boxRaw[0],d.boxRaw[3],d.boxRaw[2]]),o=s.map(d=>d.score),l=[];if(i&&i.length>0){let d=await 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nc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],kde=nc.length,ac=nc.reduce((e,t,n)=>(e[t]=n,e),{}),Ide=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],nhe=Ide.map(([e,t])=>[ac[e],ac[t]]),TI=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function SI(e){let t=e.reduce(({maxX:n,maxY:a,minX:r,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(a,o),minX:Math.min(r,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function NI(e,[t,n],[a,r]){let s=t/a,i=n/r,o=(d,u)=>({id:u,score:d.score,boxRaw:[d.box[0]/r,d.box[1]/a,d.box[2]/r,d.box[3]/a],box:[Math.trunc(d.box[0]*i),Math.trunc(d.box[1]*s),Math.trunc(d.box[2]*i),Math.trunc(d.box[3]*s)],keypoints:d.keypoints.map(({score:p,part:c,position:h})=>({score:p,part:c,position:[Math.trunc(h.x*i),Math.trunc(h.y*s)],positionRaw:[h.x/a,h.y/a]}))});return e.map((d,u)=>o(d,u))}var i5=class{constructor(t,n){ce(this,"priorityQueue");ce(this,"numberOfElements");ce(this,"getElementValue");this.priorityQueue=new 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r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function PI(e,t,n,a=5){let r,s,i;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),s=a*Math.cos(r)+n[0],i=a*Math.sin(r)+n[1],e.moveTo(s,i),r+=1/3*(2*Math.PI),s=a*Math.cos(r)+n[0],i=a*Math.sin(r)+n[1],e.lineTo(s,i),r+=1/3*(2*Math.PI),s=a*Math.cos(r)+n[0],i=a*Math.sin(r)+n[1],e.lineTo(s,i),e.closePath(),e.stroke(),e.fill()}async function g5(e,t,n){let a=kn(Kr,n);if(!(!t||!e)&&a.drawGestures){let r=Po(e);r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let d=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${d}: 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pitch:${Ku(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&p.push(`gaze: ${Ku(u.rotation.gaze.bearing)}\xB0`)),p.length===0&&p.push("face"),r.fillStyle=a.color;for(let c=p.length-1;c>=0;c--){let h=Math.max(u.box[0],0),f=c*a.lineHeight+u.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(p[c],h+5,f+16)),r.fillStyle=a.labelColor,r.fillText(p[c],h+4,f+15)}}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(a.drawPoints)for(let p of u.mesh)m5(r,p[0],p[1],p[2],a);if(a.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let p=0;p<Mo.length/3;p++){let c=[Mo[p*3+0],Mo[p*3+1],Mo[p*3+2]].map(h=>u.mesh[h]);_I(r,c,a)}if(u.annotations&&u.annotations.leftEyeIris&&u.annotations.leftEyeIris[0]){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,c=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,c=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((s=u.rotation)==null?void 0:s.angle)){r.strokeStyle="pink";let p=u.box[0]+u.box[2]/2-u.box[3]*Ku(u.rotation.angle.yaw)/90,c=u.box[1]+u.box[3]/2+u.box[2]*Ku(u.rotation.angle.pitch)/90,h=new Path2D(`
|
|
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
|
|
C
|
|
${p} ${u.box[1]},
|
|
${p} ${u.box[1]+u.box[3]},
|
|
${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
|
|
`),f=new Path2D(`
|
|
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
|
|
C
|
|
${u.box[0]} ${c},
|
|
${u.box[0]+u.box[2]} ${c},
|
|
${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
|
|
`);r.stroke(f),r.stroke(h)}if(a.drawGaze&&((o=(i=u.rotation)==null?void 0:i.gaze)==null?void 0:o.strength)&&((d=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:d.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let p=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];PI(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[p[0],p[1]],4);let c=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];PI(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[c[0],c[1]],4)}}}}}async function A5(e,t,n){var s;let a=kn(Kr,n);if(!t||!e)return;let r=Po(e);r.lineJoin="round";for(let i=0;i<t.length;i++){if(r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(sc(r,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)r.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,m5(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&t[i].keypoints){r.font=a.font;for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:a.color,r.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4)}if(a.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)Dde(r,l,a)}}async function x5(e,t,n){let a=kn(Kr,n);if(!t||!e)return;let r=Po(e);r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,sc(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)r.fillStyle=a.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:a.color,m5(r,i[0],i[1],0,a);if(a.drawLabels&&s.annotations){let i=(o,l)=>{!o||o.length===0||!o[0]||(r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4))};r.font=a.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(a.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++)r.beginPath(),r.strokeStyle=a.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:a.color,r.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),r.lineTo(o[l][0],o[l][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}async function b5(e,t,n){let a=kn(Kr,n);if(!t||!e)return;let r=Po(e);r.lineJoin="round",r.font=a.font;for(let s of t)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,sc(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}async function zI(e,t,n){let a=kn(Kr,n);if(!t||!e)return;let r=Po(e);r.lineJoin="round",r.font=a.font;for(let s=0;s<t.length;s++)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,sc(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels){let i=`person #${s}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,t[s].box[0]+3,1+t[s].box[1]+a.lineHeight,t[s].box[2])),r.fillStyle=a.labelColor,r.fillText(i,t[s].box[0]+2,0+t[s].box[1]+a.lineHeight,t[s].box[2])}r.stroke()}}async function LI(e,t){if(!e||!t)return;Po(t).drawImage(e,0,0)}async function WI(e,t,n){if(!t||!t.performance||!t||!e)return null;let a=he(),r=kn(Kr,n),s=Promise.all([y5(e,t.face,r),A5(e,t.body,r),x5(e,t.hand,r),b5(e,t.object,r),g5(e,t.gesture,r)]);return t.performance.draw=ye.perfadd?(t.performance.draw||0)+Math.trunc(he()-a):Math.trunc(he()-a),s}var $de=e=>{let t=(p,c)=>Math.atan2(p[1]-c[1],p[0]-c[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],a=1,r=e.mesh[33][2]>e.mesh[263][2],s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],d=Math.sqrt(l[0]**2+l[1]**2);return d=Math.min(d,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:d}},BI=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},a=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],b=g[2]-y[2];return[A,x,b]},r=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],b=g[0]*y[1]-g[1]*y[0];return[A,x,b]},s=g=>{let[y,A,x,b,v,k,N,C,E]=g,O,D,T;return b<1?b>-1?(T=Math.asin(b),D=Math.atan2(-N,y),O=Math.atan2(-k,v)):(T=-Math.PI/2,D=-Math.atan2(C,E),O=0):(T=Math.PI/2,D=Math.atan2(C,E),O=0),isNaN(O)&&(O=0),isNaN(D)&&(D=0),isNaN(T)&&(T=0),{pitch:2*-O,yaw:2*-D,roll:2*-T}},i=g=>{let y=(x,b,v,k)=>Math.atan2(k-b,v-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,d=[o[10],o[152],o[234],o[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(a(d[1],d[0])),p=n(a(d[3],d[2])),c=n(r(p,u));p=r(u,c);let h=[p[0],p[1],p[2],u[0],u[1],u[2],c[0],c[1],c[2]],f=s(h),m=o.length===478?$de(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var v5=async(e,t)=>{var c,h,f,m;let n,a,r,s,i,o,l,d,u=[];e.state="run:face",n=he();let p=await L9(t,e.config);if(e.performance.face=ye.perfadd?(e.performance.face||0)+Math.trunc(he()-n):Math.trunc(he()-n),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let g=0;g<p.length;g++){if(e.analyze("Get Face"),!p[g].tensor||p[g].tensor.isDisposedInternal){se("Face object is disposed:",p[g].tensor);continue}let y=BI(p[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?i=e.config.face.emotion.enabled?$x(p[g].tensor||Lt([]),e.config,g,p.length):null:(e.state="run:emotion",n=he(),i=e.config.face.emotion.enabled?await $x(p[g].tensor||Lt([]),e.config,g,p.length):null,e.performance.emotion=ye.perfadd?(e.performance.emotion||0)+Math.trunc(he()-n):Math.trunc(he()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=e.config.face.antispoof.enabled?dx(p[g].tensor||Lt([]),e.config,g,p.length):null:(e.state="run:antispoof",n=he(),l=e.config.face.antispoof.enabled?await dx(p[g].tensor||Lt([]),e.config,g,p.length):null,e.performance.antispoof=ye.perfadd?(e.performance.antispoof||0)+Math.trunc(he()-n):Math.trunc(he()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Description:"),e.config.async?d=e.config.face.description.enabled?Wx(p[g].tensor||Lt([]),e.config,g,p.length):null:(e.state="run:description",n=he(),d=e.config.face.description.enabled?await Wx(p[g].tensor||Lt([]),e.config,g,p.length):null,e.performance.embedding=ye.perfadd?(e.performance.embedding||0)+Math.trunc(he()-n):Math.trunc(he()-n)),e.analyze("End Description:"),e.config.async&&([a,s,i,o,d,r,l]=await Promise.all([a,s,i,o,d,r,l])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((h=(c=p[g])==null?void 0:c.annotations)==null?void 0:h.leftEyeIris)&&((m=(f=p[g])==null?void 0:f.annotations)==null?void 0:m.rightEyeIris)&&(delete p[g].annotations.leftEyeIris,delete p[g].annotations.rightEyeIris);let A=p[g].annotations&&p[g].annotations.leftEyeIris&&p[g].annotations.leftEyeIris[0]&&p[g].annotations.rightEyeIris&&p[g].annotations.rightEyeIris[0]&&p[g].annotations.leftEyeIris.length>0&&p[g].annotations.rightEyeIris.length>0&&p[g].annotations.leftEyeIris[0]!==null&&p[g].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[g].annotations.leftEyeIris[3][0]-p[g].annotations.leftEyeIris[1][0]),Math.abs(p[g].annotations.rightEyeIris[4][1]-p[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0,x=e.config.face.detector.return?st(p[g].tensor):null;Q(p[g].tensor),p[g].tensor&&delete p[g].tensor,u.push({...p[g],id:g,age:d==null?void 0:d.age,gender:d==null?void 0:d.gender,genderScore:d==null?void 0:d.genderScore,embedding:d==null?void 0:d.descriptor,emotion:i,real:l,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:y,tensor:x}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var VI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&a&&r&&a.position[1]<s.position[1]&&r.position[1]<s.position[1]?t.push({body:n,gesture:"i give up"}):s&&a&&a.position[1]<s.position[1]?t.push({body:n,gesture:"raise left hand"}):s&&r&&r.position[1]<s.position[1]&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},UI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let a=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(a)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${a<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},jI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let a=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(a*r),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o),d=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(d=!0,t.push({iris:n,gesture:"facing center"}));let p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],c=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(c>.06||p>.06)&&(d=!1),c>.06&&t.push({iris:n,gesture:"looking right"}),p>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(d=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),d&&t.push({iris:n,gesture:"looking center"})}return t},GI=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let a=[];if(e[n].annotations)for(let[r,s]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(s)&&s[0]&&a.push({name:r.toLowerCase(),position:s[0]});if(a&&a.length>0){let r=a.reduce((i,o)=>i.position[2]<o.position[2]?i:o);t.push({hand:n,gesture:`${r.name} forward`});let s=a.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${s.name} up`})}if(e[n].keypoints){let r=uI(e[n].keypoints);for(let s of r)t.push({hand:n,gesture:s.name})}}return t};var Oe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function HI(e,t){var i,o,l,d,u,p,c,h,f,m,g,y,A,x,b,v,k,N,C,E,O,D,T,P,_,j,q;let n=he();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let a=Date.now()-e.timestamp,r=a<1e3?8-Math.log(a+1):1;if(Oe.canvas=e.canvas,!Oe.body||e.body.length!==Oe.body.length)Oe.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let X=e.body[z].box.map((Y,re)=>((r-1)*Oe.body[z].box[re]+Y)/r),Z=e.body[z].boxRaw.map((Y,re)=>((r-1)*Oe.body[z].boxRaw[re]+Y)/r),te=e.body[z].keypoints.map((Y,re)=>({score:Y.score,part:Y.part,position:[Oe.body[z].keypoints[re]?((r-1)*Oe.body[z].keypoints[re].position[0]+Y.position[0])/r:Y.position[0],Oe.body[z].keypoints[re]?((r-1)*Oe.body[z].keypoints[re].position[1]+Y.position[1])/r:Y.position[1]],positionRaw:[Oe.body[z].keypoints[re]?((r-1)*Oe.body[z].keypoints[re].positionRaw[0]+Y.positionRaw[0])/r:Y.position[0],Oe.body[z].keypoints[re]?((r-1)*Oe.body[z].keypoints[re].positionRaw[1]+Y.positionRaw[1])/r:Y.position[1]]})),J={},ie={connected:{}};((o=(i=t.body)==null?void 0:i.modelPath)==null?void 0:o.includes("efficientpose"))?ie=Cx:((d=(l=t.body)==null?void 0:l.modelPath)==null?void 0:d.includes("blazepose"))?ie=bx:((p=(u=t.body)==null?void 0:u.modelPath)==null?void 0:p.includes("movenet"))&&(ie=e5);for(let[Y,re]of Object.entries(ie.connected)){let de=[];for(let be=0;be<re.length-1;be++){let ve=te.find(Ee=>Ee.part===re[be]),ke=te.find(Ee=>Ee.part===re[be+1]);ve&&ke&&ve.score>(t.body.minConfidence||0)&&ke.score>(t.body.minConfidence||0)&&de.push([ve.position,ke.position])}J[Y]=de}Oe.body[z]={...e.body[z],box:X,boxRaw:Z,keypoints:te,annotations:J}}if(!Oe.hand||e.hand.length!==Oe.hand.length)Oe.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let X=e.hand[z].box.map((ie,Y)=>((r-1)*Oe.hand[z].box[Y]+ie)/r),Z=e.hand[z].boxRaw.map((ie,Y)=>((r-1)*Oe.hand[z].boxRaw[Y]+ie)/r);Oe.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Oe.hand[z].keypoints=e.hand[z].keypoints);let te=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((ie,Y)=>ie.map((re,de)=>((r-1)*(Oe.hand[z].keypoints[Y][de]||1)+(re||0))/r)):[],J={};if(Object.keys(Oe.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Oe.hand[z].annotations=e.hand[z].annotations,J=Oe.hand[z].annotations;else if(e.hand[z].annotations)for(let ie of Object.keys(e.hand[z].annotations))J[ie]=e.hand[z].annotations[ie]&&e.hand[z].annotations[ie][0]?e.hand[z].annotations[ie].map((Y,re)=>Y.map((de,be)=>((r-1)*Oe.hand[z].annotations[ie][re][be]+de)/r)):null;Oe.hand[z]={...e.hand[z],box:X,boxRaw:Z,keypoints:te,annotations:J}}if(!Oe.face||e.face.length!==Oe.face.length)Oe.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let X=e.face[z].box.map((J,ie)=>((r-1)*Oe.face[z].box[ie]+J)/r),Z=e.face[z].boxRaw.map((J,ie)=>((r-1)*Oe.face[z].boxRaw[ie]+J)/r),te={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};te.matrix=(c=e.face[z].rotation)==null?void 0:c.matrix,te.angle={roll:((r-1)*(((f=(h=Oe.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((A=(y=Oe.face[z].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[z].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(v=Oe.face[z].rotation)==null?void 0:v.angle)==null?void 0:k.pitch)||0)+(((C=(N=e.face[z].rotation)==null?void 0:N.angle)==null?void 0:C.pitch)||0))/r},te.gaze={bearing:((r-1)*(((O=(E=Oe.face[z].rotation)==null?void 0:E.gaze)==null?void 0:O.bearing)||0)+(((T=(D=e.face[z].rotation)==null?void 0:D.gaze)==null?void 0:T.bearing)||0))/r,strength:((r-1)*(((_=(P=Oe.face[z].rotation)==null?void 0:P.gaze)==null?void 0:_.strength)||0)+(((q=(j=e.face[z].rotation)==null?void 0:j.gaze)==null?void 0:q.strength)||0))/r},Oe.face[z]={...e.face[z],rotation:te,box:X,boxRaw:Z}}if(!Oe.object||e.object.length!==Oe.object.length)Oe.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let X=e.object[z].box.map((te,J)=>((r-1)*Oe.object[z].box[J]+te)/r),Z=e.object[z].boxRaw.map((te,J)=>((r-1)*Oe.object[z].boxRaw[J]+te)/r);Oe.object[z]={...e.object[z],box:X,boxRaw:Z}}if(e.persons){let z=e.persons;if(!Oe.persons||z.length!==Oe.persons.length)Oe.persons=JSON.parse(JSON.stringify(z));else for(let X=0;X<z.length;X++)Oe.persons[X].box=z[X].box.map((Z,te)=>((r-1)*Oe.persons[X].box[te]+Z)/r)}e.gesture&&(Oe.gesture=e.gesture);let s=he();return e.performance&&(Oe.performance={...e.performance,interpolate:Math.round(s-n)}),Oe}function tm(e,t,n={order:2,multiplier:20}){let a=0;for(let r=0;r<e.length;r++){let s=n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);a+=n.order===2?s*s:s**n.order}return(n.multiplier||20)*a}function qI(e,t,n={order:2,multiplier:20}){let a=tm(e,t,n),r=n.order===2?Math.sqrt(a):a**(1/n.order);return Math.max(0,100-r)/100}function XI(e,t,n={order:2,threshold:0,multiplier:20}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let a=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let i=tm(e,t[s],n);if(i<a&&(a=i,r=s),a<n.threshold)break}return a=n.order===2?Math.sqrt(a):a**(1/n.order),{index:r,distance:a,similarity:Math.max(0,100-a)/100}}function KI(e,t,n,a,r){var o,l,d,u,p,c,h,f,m,g,y,A,x,b,v,k;let s=0,i=[];for(let N of e){let C={id:s++,face:N,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let _ of t)N.box[0]>_.box[0]&&N.box[0]<_.box[0]+_.box[2]&&N.box[1]+N.box[3]>_.box[1]&&N.box[1]+N.box[3]<_.box[1]+_.box[3]&&(C.body=_);if(C.body)for(let _ of n)_.box[0]+_.box[2]>C.body.box[0]&&_.box[0]+_.box[2]<C.body.box[0]+C.body.box[2]&&_.box[1]+_.box[3]>C.body.box[1]&&_.box[1]+_.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=_),_.box[0]<C.body.box[0]+C.body.box[2]&&_.box[0]>C.body.box[0]&&_.box[1]+_.box[3]>C.body.box[1]&&_.box[1]+_.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=_);for(let _ of a)_.face!==void 0&&_.face===N.id?(o=C.gestures)==null||o.push(_):_.iris!==void 0&&_.iris===N.id?(l=C.gestures)==null||l.push(_):_.body!==void 0&&_.body===((d=C.body)==null?void 0:d.id)?(u=C.gestures)==null||u.push(_):_.hand!==void 0&&_.hand===((c=(p=C.hands)==null?void 0:p.left)==null?void 0:c.id)?(h=C.gestures)==null||h.push(_):_.hand!==void 0&&_.hand===((m=(f=C.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=C.gestures)==null||g.push(_));let E=[],O=[],D=_=>{_&&_.length===4&&(E.push(_[0],_[0]+_[2]),O.push(_[1],_[1]+_[3]))};D((y=C.face)==null?void 0:y.box),D((A=C.body)==null?void 0:A.box),D((b=(x=C.hands)==null?void 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2Q==`;async function Ode(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(e.config.warmup){case"face":n=await t(nm);break;case"body":case"full":n=await t(am);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await e.detect(r,e.config),r.close()}return a}async function _de(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+nm;break;case"full":case"body":n="data:image/jpeg;base64,"+am;break;default:n=null}let a;typeof Image!="undefined"?a=new Image:ye.Image&&(a=new ye.Image),a.onload=async()=>{let r=zn(a.naturalWidth,a.naturalHeight);if(!r)se("Warmup: Canvas not found"),t({});else{let s=r.getContext("2d");s&&s.drawImage(a,0,0);let i=await e.image(r),o=await e.detect(i.tensor,e.config);t(o)}},n?a.src=n:t(null)})}async function Pde(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(nm)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(am)),!n)return 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this.load(),r=he(),this.state="image";let o=Uu(t,this.config);if(this.process=o,this.performance.image=this.env.perfadd?(this.performance.image||0)+Math.trunc(he()-r):Math.trunc(he()-r),this.analyze("Get Image:"),!o.tensor){this.config.debug&&se("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.emit("image"),r=he(),this.config.skipAllowed=await s9(this.config,o.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipAllowed&&this.performance.cached++,this.performance.changed=this.env.perfadd?(this.performance.changed||0)+Math.trunc(he()-r):Math.trunc(he()-r),this.analyze("Check Changed:");let l=[],d=[],u=[],p=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?v5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=he(),l=this.config.face.enabled?await v5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let c=this.config.body.maxDetected===-1?kn(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?d=this.config.body.enabled?p5(o.tensor,c):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?d=this.config.body.enabled?kx(o.tensor,c):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("efficientpose"))?d=this.config.body.enabled?Mx(o.tensor,c):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("movenet"))&&(d=this.config.body.enabled?a5(o.tensor,c):[]),this.performance.body&&delete this.performance.body):(r=he(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?d=this.config.body.enabled?await p5(o.tensor,c):[]:((v=this.config.body.modelPath)==null?void 0:v.includes("blazepose"))?d=this.config.body.enabled?await kx(o.tensor,c):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?d=this.config.body.enabled?await Mx(o.tensor,c):[]:((N=this.config.body.modelPath)==null?void 0:N.includes("movenet"))&&(d=this.config.body.enabled?await a5(o.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?kn(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((E=(C=this.config.hand.detector)==null?void 0:C.modelPath)==null?void 0:E.includes("handdetect"))?u=this.config.hand.enabled?Gx(o.tensor,h):[]:((D=(O=this.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:D.includes("handtrack"))&&(u=this.config.hand.enabled?Zx(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=he(),((P=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:P.includes("handdetect"))?u=this.config.hand.enabled?await Gx(o.tensor,h):[]:((j=(_=this.config.hand.detector)==null?void 0:_.modelPath)==null?void 0:j.includes("handtrack"))&&(u=this.config.hand.enabled?await Zx(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((q=this.config.object.modelPath)==null?void 0:q.includes("nanodet"))?p=this.config.object.enabled?s5(o.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(p=this.config.object.enabled?Tx(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=he(),((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?p=this.config.object.enabled?await s5(o.tensor,this.config):[]:((Z=this.config.object.modelPath)==null?void 0:Z.includes("centernet"))&&(p=this.config.object.enabled?await Tx(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,d,u,p]=await Promise.all([l,d,u,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=he(),f=[...UI(l),...VI(d),...GI(u),...jI(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(he()-r):Math.trunc(he()-r)),this.performance.total=Math.trunc(he()-i);let m=((J=(te=this.process)==null?void 0:te.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:d,hand:u,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return KI(l,d,u,f,m)}},Q(o.tensor),this.emit("detect"),this.state="idle",a(this.result)})}};Zu=new WeakMap,ic=new WeakMap,oc=new WeakMap,rm=new WeakMap;return zde;})();
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* https://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|