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this.throwIfDisposed(),xt.softplus(this)},t.prototype.zerosLike=function(){return this.throwIfDisposed(),xt.zerosLike(this)},t.prototype.onesLike=function(){return this.throwIfDisposed(),xt.onesLike(this)},t.prototype.sin=function(){return this.throwIfDisposed(),xt.sin(this)},t.prototype.cos=function(){return this.throwIfDisposed(),xt.cos(this)},t.prototype.tan=function(){return this.throwIfDisposed(),xt.tan(this)},t.prototype.asin=function(){return this.throwIfDisposed(),xt.asin(this)},t.prototype.acos=function(){return this.throwIfDisposed(),xt.acos(this)},t.prototype.atan=function(){return this.throwIfDisposed(),xt.atan(this)},t.prototype.sinh=function(){return this.throwIfDisposed(),xt.sinh(this)},t.prototype.cosh=function(){return this.throwIfDisposed(),xt.cosh(this)},t.prototype.tanh=function(){return this.throwIfDisposed(),xt.tanh(this)},t.prototype.asinh=function(){return this.throwIfDisposed(),xt.asinh(this)},t.prototype.acosh=function(){return this.throwIfDisposed(),xt.acosh(this)},t.prototype.atanh=function(){return this.throwIfDisposed(),xt.atanh(this)},t.prototype.erf=function(){return this.throwIfDisposed(),xt.erf(this)},t.prototype.round=function(){return this.throwIfDisposed(),xt.round(this)},t.prototype.step=function(t){return void 0===t&&(t=0),this.throwIfDisposed(),xt.step(this,t)},t.prototype.softmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),xt.softmax(this,t)},t.prototype.logSoftmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),xt.logSoftmax(this,t)},t.prototype.resizeBilinear=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),xt.image.resizeBilinear(this,t,e)},t.prototype.resizeNearestNeighbor=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),xt.image.resizeNearestNeighbor(this,t,e)},t.prototype.conv1d=function(t,e,n,r,o,a){return void 0===r&&(r=\"NWC\"),void 0===o&&(o=1),this.throwIfDisposed(),xt.conv1d(this,t,e,n,r,o,a)},t.prototype.conv2d=function(t,e,n,r,o,a){return void 0===r&&(r=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),xt.conv2d(this,t,e,n,r,o,a)},t.prototype.conv2dTranspose=function(t,e,n,r,o){return this.throwIfDisposed(),xt.conv2dTranspose(this,t,e,n,r,o)},t.prototype.depthwiseConv2D=function(t,e,n,r,o,a){return void 0===r&&(r=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),xt.depthwiseConv2d(this,t,e,n,r,o,a)},t.prototype.separableConv2d=function(t,e,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a=\"NHWC\"),this.throwIfDisposed(),xt.separableConv2d(this,t,e,n,r,o,a)},t.prototype.avgPool=function(t,e,n,r){return this.throwIfDisposed(),xt.avgPool(this,t,e,n,r)},t.prototype.maxPool=function(t,e,n,r){return this.throwIfDisposed(),xt.maxPool(this,t,e,n,r)},t.prototype.localResponseNormalization=function(t,e,n,r){return void 0===t&&(t=5),void 0===e&&(e=1),void 0===n&&(n=1),void 0===r&&(r=.5),xt.localResponseNormalization(this,t,e,n,r)},t.prototype.pool=function(t,e,n,r,o){return this.throwIfDisposed(),xt.pool(this,t,e,n,r,o)},t.prototype.variable=function(t,e,n){return void 0===t&&(t=!0),this.throwIfDisposed(),yt().makeVariable(this,t,e,n)},t.prototype.unsortedSegmentSum=function(t,e){return this.throwIfDisposed(),xt.unsortedSegmentSum(this,t,e)},t.prototype.batchToSpaceND=function(t,e){return this.throwIfDisposed(),xt.batchToSpaceND(this,t,e)},t.prototype.spaceToBatchND=function(t,e){return this.throwIfDisposed(),xt.spaceToBatchND(this,t,e)},t.prototype.topk=function(t,e){return void 0===t&&(t=1),void 0===e&&(e=!0),this.throwIfDisposed(),xt.topk(this,t,e)},t.prototype.stridedSlice=function(t,e,n,r,o,a,i,s){return void 0===r&&(r=0),void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),this.throwIfDisposed(),xt.stridedSlice(this,t,e,n,r,o,a,i,s)},t.prototype.depthToSpace=function(t,e){return this.throwIfDisposed(),xt.depthToSpace(this,t,e)},t.prototype.fft=function(){return this.throwIfDisposed(),xt.spectral.fft(this)},t.prototype.ifft=function(){return this.throwIfDisposed(),xt.spectral.ifft(this)},t.prototype.rfft=function(){return this.throwIfDisposed(),xt.spectral.rfft(this)},t.prototype.irfft=function(){return this.throwIfDisposed(),xt.spectral.irfft(this)},t}();Object.defineProperty(wt,Symbol.hasInstance,{value:function(t){return!!t&&null!=t.dataId&&null!=t.shape&&null!=t.dtype}});var Ct,Et,Rt,It,kt,St=function(t){function n(e,n,r,o){var a=t.call(this,e.shape,e.dtype,e.dataId,o)||this;return a.trainable=n,a.name=r,a}return e(n,t),n.prototype.assign=function(t){if(t.dtype!==this.dtype)throw new Error(\"dtype of the new value (\"+t.dtype+\") and previous value (\"+this.dtype+\") must match\");if(!S(t.shape,this.shape))throw new Error(\"shape of the new value (\"+t.shape+\") and previous value (\"+this.shape+\") must match\");yt().disposeTensor(this),this.dataId=t.dataId,yt().incRef(this,null)},n.prototype.dispose=function(){yt().disposeVariable(this),this.isDisposedInternal=!0},n}(wt);Object.defineProperty(St,Symbol.hasInstance,{value:function(t){return t instanceof wt&&null!=t.assign&&t.assign instanceof Function}}),function(t){t.R0=\"R0\",t.R1=\"R1\",t.R2=\"R2\",t.R3=\"R3\",t.R4=\"R4\",t.R5=\"R5\",t.R6=\"R6\"}(Ct||(Ct={})),function(t){t.float32=\"float32\",t.int32=\"int32\",t.bool=\"int32\",t.complex64=\"complex64\"}(Et||(Et={})),function(t){t.float32=\"float32\",t.int32=\"int32\",t.bool=\"bool\",t.complex64=\"complex64\"}(Rt||(Rt={})),function(t){t.float32=\"float32\",t.int32=\"float32\",t.bool=\"float32\",t.complex64=\"complex64\"}(It||(It={})),function(t){t.float32=\"complex64\",t.int32=\"complex64\",t.bool=\"complex64\",t.complex64=\"complex64\"}(kt||(kt={}));var At={float32:It,int32:Et,bool:Rt,complex64:kt};function Dt(t,e){if(\"string\"===t||\"string\"===e){if(\"string\"===t&&\"string\"===e)return\"string\";throw new Error(\"Can not upcast \"+t+\" with \"+e)}return At[t][e]}function Tt(t){return Dt(t,\"int32\")}function Nt(t,e){if(t.dtype===e.dtype)return[t,e];var n=Dt(t.dtype,e.dtype);return[t.cast(n),e.cast(n)]}function Ft(t,e){C(t.dtype===e.dtype,(function(){return\"The dtypes of the first(\"+t.dtype+\") and second(\"+e.dtype+\") input must match\"}))}function _t(t){var e=[];return function t(e,n,r){if(null==e)return;if(e instanceof wt)return void n.push(e);if(o=e,!Array.isArray(o)&&\"object\"!=typeof o)return;var o;var a=e;for(var i in a){var s=a[i];r.has(s)||(r.add(s),t(s,n,r))}}(t,e,new Set),e}var Ot,Mt=Object.freeze({makeTypesMatch:Nt,assertTypesMatch:Ft,isTensorInList:function(t,e){return e.some((function(e){return e.id===t.id}))},getTensorsInContainer:_t}),Bt=function(){function t(){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}}return t.prototype.dispose=function(){for(var t in this.registeredVariables)this.registeredVariables[t].dispose()},t}(),Pt=function(){function t(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Bt}return t.prototype.ready=function(){return n(this,void 0,void 0,(function(){var t,e,n;return r(this,(function(r){switch(r.label){case 0:if(null!=this.pendingBackendInit)return[2,this.pendingBackendInit.then((function(){}))];if(null!=this.backendInstance)return[2];t=this.getSortedBackends(),e=0,r.label=1;case 1:return e<t.length?(n=t[e],[4,this.initializeBackend(n).success]):[3,5];case 2:return r.sent()?[4,this.setBackend(n)]:[3,4];case 3:return r.sent(),[2];case 4:return e++,[3,1];case 5:throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}}))}))},Object.defineProperty(t.prototype,\"backend\",{get:function(){if(null!=this.pendingBackendInit)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(null==this.backendInstance){var t=this.initializeBackendsAndReturnBest(),e=t.name;if(t.asyncInit)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},enumerable:!0,configurable:!0}),t.prototype.backendNames=function(){return Object.keys(this.registryFactory)},t.prototype.findBackend=function(t){if(!(t in this.registry)){if(!(t in this.registryFactory))return null;if(this.initializeBackend(t).asyncInit)return null}return this.registry[t]},t.prototype.findBackendFactory=function(t){return t in this.registryFactory?this.registryFactory[t].factory:null},t.prototype.registerBackend=function(t,e,n){return void 0===n&&(n=1),t in this.registryFactory?(console.warn(t+\" backend was already registered. Reusing existing backend factory.\"),!1):(this.registryFactory[t]={factory:e,priority:n},!0)},t.prototype.setBackend=function(t){return n(this,void 0,void 0,(function(){var e,n,o;return r(this,(function(r){switch(r.label){case 0:if(null==this.registryFactory[t])throw new Error(\"Backend name '\"+t+\"' not found in registry\");return this.backendName=t,null!=this.registry[t]?[3,4]:(this.backendInstance=null,e=this.initializeBackend(t),n=e.success,e.asyncInit?[4,n]:[3,2]);case 1:return o=r.sent(),[3,3];case 2:o=n,r.label=3;case 3:if(!o)return[2,!1];r.label=4;case 4:return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new ut(this.backendInstance),[2,!0]}}))}))},t.prototype.setupRegisteredKernels=function(){var t=this;f(this.backendName).forEach((function(e){null!=e.setupFunc&&e.setupFunc(t.backendInstance)}))},t.prototype.disposeRegisteredKernels=function(t){var e=this;f(t).forEach((function(n){null!=n.disposeFunc&&n.disposeFunc(e.registry[t])}))},t.prototype.initializeBackend=function(t){var e=this,n=this.registryFactory[t];if(null==n)throw new Error(\"Cannot initialize backend \"+t+\", no registration found.\");try{var r=n.factory();if(Promise.resolve(r)===r){var o=++this.pendingBackendInitId,a=r.then((function(n){return!(o<e.pendingBackendInitId)&&(e.registry[t]=n,e.pendingBackendInit=null,!0)})).catch((function(n){return!(o<e.pendingBackendInitId)&&(e.pendingBackendInit=null,console.warn(\"Initialization of backend \"+t+\" failed\"),console.warn(n.stack||n.message),!1)}));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(e){return console.warn(\"Initialization of backend \"+t+\" failed\"),console.warn(e.stack||e.message),{success:!1,asyncInit:!1}}},t.prototype.removeBackend=function(t){if(!(t in this.registryFactory))throw new Error(t+\" backend not found in registry\");this.backendName===t&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)},t.prototype.getSortedBackends=function(){var t=this;if(0===Object.keys(this.registryFactory).length)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((function(e,n){return t.registryFactory[n].priority-t.registryFactory[e].priority}))},t.prototype.initializeBackendsAndReturnBest=function(){for(var t=this.getSortedBackends(),e=0;e<t.length;e++){var n=t[e],r=this.initializeBackend(n),o=r.success,a=r.asyncInit;if(a||o)return{name:n,asyncInit:a}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")},t.prototype.moveData=function(t,e){var n=this.state.tensorInfo.get(e),r=n.backend,o=this.readSync(e);r.disposeData(e),n.backend=t,t.move(e,o,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++},t.prototype.tidy=function(t,e){var n,r=this,o=null;if(null==e){if(\"function\"!=typeof t)throw new Error(\"Please provide a function to tidy()\");e=t}else{if(\"string\"!=typeof t&&!(t instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(\"function\"!=typeof e)throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");o=t}return this.scopedRun((function(){return r.startScope(o)}),(function(){return r.endScope(n)}),(function(){return(n=e())instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n}))},t.prototype.scopedRun=function(t,e,n){t();try{var r=n();return e(),r}catch(t){throw e(),t}},t.prototype.nextTensorId=function(){return t.nextTensorId++},t.prototype.nextVariableId=function(){return t.nextVariableId++},t.prototype.clone=function(t){var e=this.makeTensorFromDataId(t.dataId,t.shape,t.dtype),n={x:t};return this.addTapeNode(this.state.activeScope.name,n,[e],(function(t){return{x:function(){return t.toFloat()}}}),[]),e},t.prototype.runKernel=function(t,e,n,r,o){return this.runKernelFunc(null,e,null,t,n,r,o)},t.prototype.shouldCheckForMemLeaks=function(){return this.ENV.getBool(\"IS_TEST\")},t.prototype.checkKernelForMemLeak=function(t,e,n){var r=this.backend.numDataIds(),o=0;n.forEach((function(t){o+=\"complex64\"===t.dtype?3:1}));var a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-e-o-a;if(i>0)throw new Error(\"Backend '\"+this.backendName+\"' has an internal memory leak (\"+i+\" data ids) after running '\"+t+\"'\")},t.prototype.runKernelFunc=function(t,e,n,r,o,a,i){var s,u=this;void 0===a&&(a=[]),void 0===i&&(i=[]);var c=[],h=this.isTapeOn();null==r&&(r=null!=this.state.activeScope?this.state.activeScope.name:\"\");var f,d=function(t){h&&(c=t.map((function(t){return u.keep(u.clone(t))})))},p=this.state.numBytes,v=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);var m,g=l(r,this.backendName);return f=null!=g?function(){var t=u.backend.numDataIds();m=g.kernelFunc({inputs:e,attrs:o,backend:u.backend});var n=Array.isArray(m)?m:[m];u.shouldCheckForMemLeaks()&&u.checkKernelForMemLeak(r,t,n);var s=n.map((function(t){var e=t.dataId,n=t.shape,r=t.dtype;return u.makeTensorFromDataId(e,n,r)})),c=s.filter((function(t,e){return i[e]}));return d((a||[]).slice().concat(c)),s}:function(){var e=u.backend.numDataIds();m=u.tidy((function(){return t(u.backend,d)}));var n=Array.isArray(m)?m:[m];return u.shouldCheckForMemLeaks()&&u.checkKernelForMemLeak(r,e,n),n},this.scopedRun((function(){return u.state.kernelDepth++}),(function(){return u.state.kernelDepth--}),(function(){s=u.ENV.getBool(\"DEBUG\")?u.profiler.profileKernel(r,e,(function(){return f()})):f()})),h&&this.addTapeNode(r,e,s,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:r,bytesAdded:this.state.numBytes-p,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-v,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map((function(t){return e[t].shape})),outputShapes:s.map((function(t){return t.shape}))}),Array.isArray(m)?s:s[0]},t.prototype.makeTensor=function(t,e,n,r){if(null==t)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",r=r||this.backend;var o=t;\"string\"===n&&H(t[0])&&(o=t.map((function(t){return rt(t)})));var a=r.write(o,e,n),i=new wt(e,n,a,this.nextTensorId());if(this.incRef(i,r),\"string\"===n){var s=this.state.tensorInfo.get(a),u=G(o);this.state.numBytes+=u-s.bytes,s.bytes=u}return i},t.prototype.makeTensorFromDataId=function(t,e,n,r){var o=new wt(e,n=n||\"float32\",t,this.nextTensorId());return this.incRef(o,r),o},t.prototype.makeVariable=function(t,e,n,r){void 0===e&&(e=!0),n=n||this.nextVariableId().toString(),null!=r&&r!==t.dtype&&(t=t.asType(r));var o=new St(t,e,n,this.nextTensorId());if(null!=this.state.registeredVariables[o.name])throw new Error(\"Variable with name \"+o.name+\" was already registered\");return this.state.registeredVariables[o.name]=o,this.incRef(o,this.backend),o},t.prototype.incRef=function(t,e){var n=this.state.tensorInfo.has(t.dataId)?this.state.tensorInfo.get(t.dataId).refCount:0;if(this.state.numTensors++,\"string\"===t.dtype&&this.state.numStringTensors++,0===n){this.state.numDataBuffers++;var r=0;\"complex64\"!==t.dtype&&\"string\"!==t.dtype&&(r=t.size*z(t.dtype)),this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:r,refCount:0}),this.state.numBytes+=r}this.state.tensorInfo.get(t.dataId).refCount++,t instanceof St||this.track(t)},t.prototype.disposeTensor=function(t){if(this.state.tensorInfo.has(t.dataId)){this.state.numTensors--,\"string\"===t.dtype&&this.state.numStringTensors--;var e=this.state.tensorInfo.get(t.dataId);e.refCount<=1?(\"complex64\"!==t.dtype&&(this.state.numBytes-=e.bytes),this.state.numDataBuffers--,e.backend.disposeData(t.dataId),this.state.tensorInfo.delete(t.dataId)):this.state.tensorInfo.get(t.dataId).refCount--}},t.prototype.disposeVariables=function(){for(var t in this.state.registeredVariables){var e=this.state.registeredVariables[t];this.disposeVariable(e)}},t.prototype.disposeVariable=function(t){this.disposeTensor(t),null!=this.state.registeredVariables[t.name]&&delete this.state.registeredVariables[t.name]},t.prototype.memory=function(){var t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,null==t.reasons&&(t.reasons=[]),t.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),t},t.prototype.profile=function(t){return n(this,void 0,void 0,(function(){var e,n;return r(this,(function(r){return this.state.profiling=!0,e=this.state.numBytes,n=this.state.numTensors,this.state.activeProfile.kernels=[],this.state.activeProfile.result=t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max.apply(Math,this.state.activeProfile.kernels.map((function(t){return t.totalBytesSnapshot}))),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n,[2,this.state.activeProfile]}))}))},t.prototype.isTapeOn=function(){return this.state.gradientDepth>0&&0===this.state.kernelDepth},t.prototype.addTapeNode=function(t,e,n,r,o){var a=this,i={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:o},s=h(t);null!=s&&(r=s.gradFunc),null!=r&&(i.gradient=function(t){return t=t.map((function(t,e){if(null==t){var r=n[e],o=tt(r.size,r.dtype);return a.makeTensor(o,r.shape,r.dtype)}return t})),r(t.length>1?t:t[0],o)}),this.state.activeTape.push(i)},t.prototype.keep=function(t){return t.kept=!0,t},t.prototype.startTape=function(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++},t.prototype.endTape=function(){this.state.gradientDepth--},t.prototype.startScope=function(t){var e={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e},t.prototype.endScope=function(t){for(var e=this,n=_t(t),r=new Set(n.map((function(t){return t.id}))),o=0;o<this.state.activeScope.track.length;o++){var a=this.state.activeScope.track[o];a.kept||r.has(a.id)||a.dispose()}var i=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],n.forEach((function(t){t.kept||t.scopeId!==i.id||e.track(t)}))},t.prototype.gradients=function(t,e,n,r){var o=this;if(void 0===r&&(r=!1),C(e.length>0,(function(){return\"gradients() received an empty list of xs.\"})),null!=n&&\"float32\"!==n.dtype)throw new Error(\"dy must have 'float32' dtype, but has '\"+n.dtype+\"'\");var a=this.scopedRun((function(){return o.startTape()}),(function(){return o.endTape()}),(function(){return o.tidy(\"forward\",t)}));C(a instanceof wt,(function(){return\"The result y returned by f() must be a tensor.\"}));var i=function(t,e,n){for(var r={},o={},a=0;a<e.length;a++)r[e[a].id]=!0;for(a=0;a<t.length;a++){var i=(p=t[a]).inputs;for(var s in i){for(var u=i[s],c=!1,l=0;l<e.length;l++)if(r[u.id]){p.outputs.forEach((function(t){return r[t.id]=!0})),c=!0,o[p.id]=!0;break}if(c)break}}var h={};h[n.id]=!0;var f={};for(a=t.length-1;a>=0;a--)for(i=(p=t[a]).inputs,l=0;l<p.outputs.length;l++)if(h[p.outputs[l].id]){for(var s in i)h[i[s].id]=!0,f[p.id]=!0;break}var d=[];for(a=0;a<t.length;a++){var p;if(o[(p=t[a]).id]&&f[p.id]){var v={};for(var s in p.inputs){var m=p.inputs[s];r[m.id]&&(v[s]=m)}var g=Object.assign({},p);g.inputs=v,g.outputs=p.outputs,d.push(g)}}return d}(this.state.activeTape,e,a);if(!r&&0===i.length&&e.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. 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t.score-e.score||t.score===e.score&&e.boxIndex-t.boxIndex}function Zo(t,e,n){var r=new Array(t.rank).fill(0),o=t.shape.slice();return e.map((function(e){o[n]=e;var a=t.slice(r,o);return r[n]+=e,a}))}function ta(t,e){for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[r]*e[r];var o=dr(n,t.dtype);for(r=0;r<o.values.length;++r){for(var a=o.indexToLoc(r),i=new Array(t.rank),s=0;s<i.length;s++)i[s]=a[s]%t.shape[s];var u=t.locToIndex(i);o.values[r]=t.values[u]}return o.toTensor()}function ea(t,e,n,r,o){for(var a=e[e.length-1],i=[t.length/a,a],s=i[0],u=i[1],c=B(n,s*r),l=B(\"int32\",s*r),h=0;h<s;h++){for(var f=h*u,d=t.subarray(f,f+u),p=[],v=0;v<d.length;v++)p.push({value:d[v],index:v});p.sort((function(t,e){return e.value-t.value}));var m=h*r,g=c.subarray(m,m+r),y=l.subarray(m,m+r);for(v=0;v<r;v++)g[v]=p[v].value,y[v]=p[v].index}var x=e.slice();return x[x.length-1]=r,[Fn(c,x,n),Fn(l,x,\"int32\")]}function na(t,e){for(var n=[],r=0;r<e.length;r++)e[r]&&n.push(r);var o=dr(t,\"int32\"),a=dr([n.length,t.length],\"int32\");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*t.length;a.values.set(i,s)}return a.toTensor()}var ra=function(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((function(t,e){return\"T\"+e}));var n=[];this.variableNames.forEach((function(t){n.push(\"float v\"+t+\" = get\"+t+\"AtOutCoords();\")}));var r=this.variableNames.map((function(t){return\"v\"+t})).join(\" + \");this.userCode=\"\\n void main() {\\n \"+n.join(\"\\n \")+\"\\n\\n float result = \"+r+\";\\n setOutput(result);\\n }\\n \"},oa=function(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((function(t,e){return\"T\"+e}));var n=[];this.variableNames.forEach((function(t){n.push(\"vec4 v\"+t+\" = get\"+t+\"AtOutCoords();\")}));var r=this.variableNames.map((function(t){return\"v\"+t})).join(\" + \");this.userCode=\"\\n void main() {\\n \"+n.join(\"\\n \")+\"\\n\\n vec4 result = \"+r+\";\\n setOutput(result);\\n }\\n \"},aa=function(t,e,n){this.variableNames=[\"A\"];var r=t.windowSize,o=t.batchSize,a=t.inSize,i=Math.ceil(a/r);n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[o,i];var s=\"max\"===e?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=\"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n int outIdx = coords[1];\\n int inOffset = outIdx * \"+r+\";\\n\\n int bestIndex = inOffset;\\n float bestValue = getA(batch, bestIndex);\\n\\n for (int i = 0; i < \"+r+\"; i++) {\\n int inIdx = \"+u+\";\\n float candidate = getA(batch, inIdx);\\n if (candidate \"+s+\" bestValue) {\\n bestValue = candidate;\\n bestIndex = inIdx;\\n }\\n }\\n setOutput(float(bestIndex));\\n }\\n \"};function ia(t,e){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,e).map((function(e){return t+\".\"+e}))}function sa(t,e){return 1===e?[t]:ia(t,e)}function ua(){var t,e,n,r,o,a,s,u,c,l;return 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float FLOAT_MIN = 1.17549435e-38;\\n\\n lowp vec4 encode_float(highp float v) {\\n if (isnan(v)) {\\n return vec4(255, 255, 255, 255);\\n }\\n\\n highp float av = abs(v);\\n\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n highp float e = floor(log2(av));\\n highp float m = exp2(fract(log2(av))) - 1.0;\\n\\n c[2] = floor(128.0 * m);\\n m -= c[2] / 128.0;\\n c[1] = floor(32768.0 * m);\\n m -= c[1] / 32768.0;\\n c[0] = floor(8388608.0 * m);\\n\\n highp float ebias = e + 127.0;\\n c[3] = floor(ebias / 2.0);\\n ebias -= c[3] * 2.0;\\n c[2] += floor(ebias) * 128.0;\\n\\n c[3] += 128.0 * step(0.0, -v);\\n\\n return c / 255.0;\\n }\\n\";function fa(t,e,n,r){var o=[];t.forEach((function(t){var e=k(t.shapeInfo.logicalShape);t.shapeInfo.isUniform?o.push(\"uniform float \"+t.name+(e>1?\"[\"+e+\"]\":\"\")+\";\"):(o.push(\"uniform sampler2D \"+t.name+\";\"),o.push(\"uniform int offset\"+t.name+\";\"))}));var a,i,s=o.join(\"\\n\"),u=t.map((function(t){return function(t,e,n){void 0===n&&(n=!1);var r=\"\";r+=n?pa(t):da(t);var o=t.shapeInfo.logicalShape,a=e.logicalShape;o.length<=a.length&&(r+=n?function(t,e){var n,r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=\"get\"+o+\"AtOutCoords\",i=t.shapeInfo.logicalShape.length,s=e.logicalShape.length,u=Co(t.shapeInfo.logicalShape,e.logicalShape),c=wa(s),l=s-i,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];n=0===i?\"\":s<2&&u.length>=1?\"coords = 0;\":u.map((function(t){return\"coords.\"+h[t+l]+\" = 0;\"})).join(\"\\n\");var f=\"\";f=s<2&&i>0?\"coords\":t.shapeInfo.logicalShape.map((function(t,e){return\"coords.\"+h[e+l]})).join(\", \");var d=\"return outputValue;\",p=1===k(t.shapeInfo.logicalShape),v=1===k(e.logicalShape);if(1!==i||p||v){if(p&&!v)d=1===s?\"\\n return vec4(outputValue.x, outputValue.x, 0., 0.);\\n 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c,l=wa(u),h=Co(t.shapeInfo.logicalShape,e.logicalShape),f=u-s,d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];c=0===s?\"\":u<2&&h.length>=1?\"coords = 0;\":h.map((function(t){return\"coords.\"+d[t+f]+\" = 0;\"})).join(\"\\n\");var p=\"\";p=u<2&&s>0?\"coords\":t.shapeInfo.logicalShape.map((function(t,e){return\"coords.\"+d[e+f]})).join(\", \");return\"\\n float \"+o+\"() {\\n \"+l+\" coords = getOutputCoords();\\n \"+c+\"\\n return get\"+r+\"(\"+p+\");\\n }\\n \"}(t,e));return r}(t,e,r)})).join(\"\\n\"),c=e.texShape,l=ua(),h=function(t){return\"\\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\\n return \"+t.texture2D+\"(textureSampler, uv).r;\\n }\\n \"}(l),f=function(t){return t.version+\"\\n precision highp float;\\n precision highp int;\\n precision highp sampler2D;\\n \"+t.varyingFs+\" vec2 resultUV;\\n \"+t.defineOutput+\"\\n const vec2 halfCR = vec2(0.5, 0.5);\\n\\n struct ivec5\\n {\\n int x;\\n int y;\\n int z;\\n int w;\\n int u;\\n };\\n\\n struct ivec6\\n {\\n int x;\\n int y;\\n int z;\\n int w;\\n int u;\\n int v;\\n };\\n\\n uniform float NAN;\\n \"+t.defineSpecialNaN+\"\\n \"+t.defineSpecialInf+\"\\n \"+t.defineRound+\"\\n\\n int imod(int x, int y) {\\n return x - y * (x / y);\\n }\\n\\n int idiv(int a, int b, float sign) {\\n int res = a / b;\\n int mod = imod(a, b);\\n if (sign < 0. && mod != 0) {\\n res -= 1;\\n }\\n return res;\\n }\\n\\n //Based on the work of Dave Hoskins\\n //https://www.shadertoy.com/view/4djSRW\\n #define HASHSCALE1 443.8975\\n float random(float seed){\\n vec2 p = resultUV * seed;\\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\\n p3 += dot(p3, p3.yzx + 19.19);\\n return fract((p3.x + p3.y) * p3.z);\\n }\\n\\n \"+va+\"\\n \"+ma+\"\\n \"+ga+\"\\n \"}(l);return e.isPacked?(a=function(t,e){switch(t.length){case 0:return\"\\n int getOutputCoords() {\\n return 0;\\n }\\n \";case 1:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(1===n[0])return\"\\n int getOutputCoords() {\\n return 2 * int(resultUV.x * \"+n[1]+\".0);\\n }\\n \";if(1===n[1])return\"\\n int getOutputCoords() {\\n return 2 * int(resultUV.y * \"+n[0]+\".0);\\n }\\n \";return\"\\n int getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+n[0]+\", \"+n[1]+\"));\\n return 2 * (resTexRC.x * \"+n[1]+\" + resTexRC.y);\\n }\\n \"}(0,e);case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(S(t,e))return\"\\n ivec2 getOutputCoords() {\\n return 2 * ivec2(resultUV.yx * vec2(\"+n[0]+\", \"+n[1]+\"));\\n }\\n \";var r=Math.ceil(t[1]/2);return\"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+n[0]+\", \"+n[1]+\"));\\n\\n int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n int r = 2 * (index / \"+r+\");\\n int c = imod(index, \"+r+\") * 2;\\n\\n return ivec2(r, c);\\n }\\n \"}(t,e);case 3:return n=t,r=e,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=Math.ceil(n[2]/2),i=a*Math.ceil(n[1]/2),\"\\n ivec3 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+o[0]+\", \"+o[1]+\"));\\n int index = resTexRC.x * \"+o[1]+\" + resTexRC.y;\\n\\n int b = index / \"+i+\";\\n index -= b * \"+i+\";\\n\\n int r = 2 * (index / \"+a+\");\\n int c = imod(index, \"+a+\") * 2;\\n\\n return ivec3(b, r, c);\\n }\\n \";default:return function(t,e){for(var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],r=Math.ceil(t[t.length-1]/2),o=r*Math.ceil(t[t.length-2]/2),a=o,i=\"\",s=\"b, r, c\",u=2;u<t.length-1;u++)a*=t[t.length-u-1],i=\"\\n int b\"+u+\" = index / \"+a+\";\\n index -= b\"+u+\" * \"+a+\";\\n \"+i,s=\"b\"+u+\", \"+s;return\"\\n ivec\"+t.length+\" getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+n[0]+\", \"+n[1]+\"));\\n int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n\\n \"+i+\"\\n\\n int b = index / \"+o+\";\\n index -= b * \"+o+\";\\n\\n int r = 2 * (index / \"+r+\");\\n int c = imod(index, \"+r+\") * 2;\\n\\n return ivec\"+t.length+\"(\"+s+\");\\n }\\n \"}(t,e)}var n,r,o,a,i}(e.logicalShape,c),i=function(t){return\"\\n void setOutput(vec4 val) {\\n \"+t.output+\" = val;\\n }\\n \"}(l)):(a=function(t,e){switch(t.length){case 0:return\"\\n int getOutputCoords() {\\n return 0;\\n }\\n \";case 1:return function(t,e){if(1===e[0])return\"\\n int getOutputCoords() {\\n return int(resultUV.x * \"+e[1]+\".0);\\n }\\n \";if(1===e[1])return\"\\n int getOutputCoords() {\\n return int(resultUV.y * \"+e[0]+\".0);\\n }\\n \";return\"\\n int getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n return resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n }\\n \"}(0,e);case 2:return function(t,e){if(S(t,e))return\"\\n ivec2 getOutputCoords() {\\n return ivec2(resultUV.yx * vec2(\"+e[0]+\", \"+e[1]+\"));\\n }\\n \";if(1===t[1])return\"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n return ivec2(index, 0);\\n }\\n \";if(1===t[0])return\"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n return ivec2(0, index);\\n }\\n \";return\"\\n ivec2 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n int r = index / \"+t[1]+\";\\n int c = index - r * \"+t[1]+\";\\n return ivec2(r, c);\\n }\\n \"}(t,e);case 3:return n=e,r=ca([\"r\",\"c\",\"d\"],t),\"\\n ivec3 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+n[0]+\", \"+n[1]+\"));\\n int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n \"+r+\"\\n return ivec3(r, c, d);\\n }\\n \";case 4:return function(t,e){var n=ca([\"r\",\"c\",\"d\",\"d2\"],t);return\"\\n ivec4 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n \"+n+\"\\n return ivec4(r, c, d, d2);\\n }\\n \"}(t,e);case 5:return function(t,e){var n=ca([\"r\",\"c\",\"d\",\"d2\",\"d3\"],t);return\"\\n ivec5 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(\"+e[0]+\",\\n \"+e[1]+\"));\\n\\n int index = resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n\\n \"+n+\"\\n\\n ivec5 outShape = ivec5(r, c, d, d2, d3);\\n return outShape;\\n }\\n \"}(t,e);case 6:return function(t,e){var n=ca([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],t);return\"\\n ivec6 getOutputCoords() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = resTexRC.x * \"+e[1]+\" + resTexRC.y;\\n\\n \"+n+\"\\n\\n ivec6 result = ivec6(r, c, d, d2, d3, d4);\\n return result;\\n }\\n \"}(t,e);default:throw new Error(t.length+\"-D output sampling is not yet supported\")}var n,r}(e.logicalShape,c),i=function(t){return\"\\n void setOutput(float val) {\\n \"+t.output+\" = vec4(val, 0, 0, 0);\\n }\\n \"}(l)),r&&(f+=ya),[f,h,i,s,a,u,n].join(\"\\n\")}function da(t){var e=t.shapeInfo.logicalShape;switch(e.length){case 0:return function(t){var e=t.name,n=\"get\"+e.charAt(0).toUpperCase()+e.slice(1);if(t.shapeInfo.isUniform)return\"float \"+n+\"() {return \"+e+\";}\";var r=t.shapeInfo.texShape,o=r[0],a=r[1];if(1===o&&1===a)return\"\\n float \"+n+\"() {\\n return sampleTexture(\"+e+\", halfCR);\\n }\\n \";var i=t.shapeInfo.texShape,s=i[0],u=i[1],c=xa(e);return\"\\n float \"+n+\"() {\\n vec2 uv = uvFromFlat(\"+s+\", \"+u+\", \"+c+\");\\n return sampleTexture(\"+e+\", uv);\\n }\\n \"}(t);case 1:return function(t){var e=t.name,n=\"get\"+e.charAt(0).toUpperCase()+e.slice(1);if(t.shapeInfo.isUniform)return\"\\n float \"+n+\"(int index) {\\n \"+ba(t)+\"\\n }\\n \";var r=t.shapeInfo.texShape,o=r[0],a=r[1];if(1===a&&1===o)return\"\\n float \"+n+\"(int index) {\\n return sampleTexture(\"+e+\", halfCR);\\n }\\n \";var i=xa(e);if(1===a)return\"\\n float \"+n+\"(int index) {\\n vec2 uv = vec2(0.5, (float(index + \"+i+\") + 0.5) / \"+o+\".0);\\n return sampleTexture(\"+e+\", uv);\\n }\\n \";if(1===o)return\"\\n float \"+n+\"(int index) {\\n vec2 uv = vec2((float(index + \"+i+\") + 0.5) / \"+a+\".0, 0.5);\\n return sampleTexture(\"+e+\", uv);\\n }\\n \";return\"\\n float \"+n+\"(int index) {\\n vec2 uv = uvFromFlat(\"+o+\", \"+a+\", index + \"+i+\");\\n return sampleTexture(\"+e+\", uv);\\n }\\n \"}(t);case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape;if(null!=o&&S(e,o)){var a=o[0],i=o[1];return\"\\n float \"+r+\"(int row, int col) {\\n vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+i+\".0, \"+a+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \"}var s=M(e),u=s.newShape,c=s.keptDims,l=u;if(l.length<e.length){var h=Ca(t,l);return\"\\n \"+da(h)+\"\\n float \"+r+\"(int row, int col) {\\n return \"+r+\"(\"+Ea([\"row\",\"col\"],c)+\");\\n }\\n \"}if(t.shapeInfo.isUniform)return\"\\n float \"+r+\"(int row, int col) {\\n int index = round(dot(vec2(row, col), vec2(\"+e[1]+\", 1)));\\n \"+ba(t)+\"\\n }\\n \";var f=o[0],d=o[1],p=xa(n);if(1===d)return\"\\n float \"+r+\"(int row, int col) {\\n float index = dot(vec3(row, col, \"+p+\"), vec3(\"+e[1]+\", 1, 1));\\n vec2 uv = vec2(0.5, (index + 0.5) / \"+f+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";if(1===f)return\"\\n float \"+r+\"(int row, int col) {\\n float index = dot(vec3(row, col, \"+p+\"), vec3(\"+e[1]+\", 1, 1));\\n vec2 uv = vec2((index + 0.5) / \"+d+\".0, 0.5);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";return\"\\n float \"+r+\"(int row, int col) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \"+e[1]+\" + col + \"+p+\";\\n vec2 uv = uvFromFlat(\"+f+\", \"+d+\", index);\\n return sampleTexture(\"+n+\", uv);\\n }\\n\"}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e[1]*e[2],a=e[2],i=M(e),s=i.newShape,u=i.keptDims,c=s;if(c.length<e.length){var l=Ca(t,c);return\"\\n \"+da(l)+\"\\n float \"+r+\"(int row, int col, int depth) {\\n return \"+r+\"(\"+Ea([\"row\",\"col\",\"depth\"],u)+\");\\n }\\n \"}if(t.shapeInfo.isUniform)return\"\\n float \"+r+\"(int row, int col, int depth) {\\n int index = round(dot(vec3(row, col, depth),\\n vec3(\"+o+\", \"+a+\", 1)));\\n \"+ba(t)+\"\\n }\\n \";var h=t.shapeInfo.texShape,f=h[0],d=h[1],p=t.shapeInfo.flatOffset;if(d===o&&null==p)return\"\\n float \"+r+\"(int row, int col, int depth) {\\n float texR = float(row);\\n float texC = dot(vec2(col, depth), vec2(\"+a+\", 1));\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+d+\".0, \"+f+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";if(d===a&&null==p)return\"\\n float \"+r+\"(int row, int col, int depth) {\\n float texR = dot(vec2(row, col), vec2(\"+e[1]+\", 1));\\n float texC = float(depth);\\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+d+\".0, \"+f+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";var v=xa(n);return\"\\n float \"+r+\"(int row, int col, int depth) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \"+o+\" + col * \"+a+\" + depth + \"+v+\";\\n vec2 uv = uvFromFlat(\"+f+\", \"+d+\", index);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \"}(t);case 4:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e[3],a=e[2]*o,i=e[1]*a,s=M(e),u=s.newShape,c=s.keptDims;if(u.length<e.length){var l=Ca(t,u);return\"\\n \"+da(l)+\"\\n float \"+r+\"(int row, int col, int depth, int depth2) {\\n return \"+r+\"(\"+Ea([\"row\",\"col\",\"depth\",\"depth2\"],c)+\");\\n }\\n \"}if(t.shapeInfo.isUniform)return\"\\n float \"+r+\"(int row, int col, int depth, int depth2) {\\n int index = round(dot(vec4(row, col, depth, depth2),\\n vec4(\"+i+\", \"+a+\", \"+o+\", 1)));\\n \"+ba(t)+\"\\n }\\n \";var h=t.shapeInfo.flatOffset,f=t.shapeInfo.texShape,d=f[0],p=f[1];if(p===i&&null==h)return\"\\n float \"+r+\"(int row, int col, int depth, int depth2) {\\n float texR = float(row);\\n float texC =\\n dot(vec3(col, depth, depth2),\\n vec3(\"+a+\", \"+o+\", 1));\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+p+\".0, \"+d+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";if(p===o&&null==h)return\"\\n float \"+r+\"(int row, int col, int depth, int depth2) {\\n float texR = dot(vec3(row, col, depth),\\n vec3(\"+e[1]*e[2]+\", \"+e[2]+\", 1));\\n float texC = float(depth2);\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+p+\".0, \"+d+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";var v=xa(n);return\"\\n float \"+r+\"(int row, int col, int depth, int depth2) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \"+i+\" + col * \"+a+\" +\\n depth * \"+o+\" + depth2;\\n vec2 uv = uvFromFlat(\"+d+\", \"+p+\", index + \"+v+\");\\n return sampleTexture(\"+n+\", uv);\\n }\\n \"}(t);case 5:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e[4],a=e[3]*o,i=e[2]*a,s=e[1]*i,u=M(e),c=u.newShape,l=u.keptDims;if(c.length<e.length){var h=Ca(t,c);return\"\\n \"+da(h)+\"\\n float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n return \"+r+\"(\"+Ea([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"],l)+\");\\n }\\n \"}if(t.shapeInfo.isUniform)return\"\\n float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n float index = dot(\\n vec4(row, col, depth, depth2),\\n vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) +\\n depth3;\\n \"+ba(t)+\"\\n }\\n \";var f=t.shapeInfo.flatOffset,d=t.shapeInfo.texShape,p=d[0],v=d[1];if(v===s&&null==f)return\"\\n float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n int texR = row;\\n float texC = dot(vec4(col, depth, depth2, depth3),\\n vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+v+\".0, \"+p+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";if(v===o&&null==f)return\"\\n float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n float texR = dot(\\n vec4(row, col, depth, depth2),\\n vec4(\"+e[1]*e[2]*e[3]+\",\\n \"+e[2]*e[3]+\", \"+e[3]+\", 1));\\n int texC = depth3;\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+v+\".0, \"+p+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";var m=xa(n);return\"\\n float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \"+s+\" + col * \"+i+\" + depth * \"+a+\" +\\n depth2 * \"+o+\" + depth3 + \"+m+\";\\n vec2 uv = uvFromFlat(\"+p+\", \"+v+\", index);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \"}(t);case 6:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=M(e),a=o.newShape,i=o.keptDims;if(a.length<e.length){var s=Ca(t,a);return\"\\n \"+da(s)+\"\\n float \"+r+\"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n return \"+r+\"(\"+Ea([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"],i)+\");\\n }\\n \"}var u=e[5],c=e[4]*u,l=e[3]*c,h=e[2]*l,f=e[1]*h;if(t.shapeInfo.isUniform)return\"\\n float \"+r+\"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n int index = round(dot(\\n vec4(row, col, depth, depth2),\\n vec4(\"+f+\", \"+h+\", \"+l+\", \"+c+\")) +\\n dot(\\n vec2(depth3, depth4),\\n vec2(\"+u+\", 1)));\\n \"+ba(t)+\"\\n }\\n \";var d=t.shapeInfo.flatOffset,p=t.shapeInfo.texShape,v=p[0],m=p[1];if(m===f&&null==d)return\"\\n float \"+r+\"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n int texR = row;\\n float texC = dot(vec4(col, depth, depth2, depth3),\\n vec4(\"+h+\", \"+l+\", \"+c+\", \"+u+\")) +\\n float(depth4);\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+m+\".0, \"+v+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";if(m===u&&null==d)return\"\\n float \"+r+\"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n float texR = dot(vec4(row, col, depth, depth2),\\n vec4(\"+e[1]*e[2]*e[3]*e[4]+\",\\n \"+e[2]*e[3]*e[4]+\",\\n \"+e[3]*e[4]+\",\\n \"+e[4]+\")) + float(depth3);\\n int texC = depth4;\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+m+\".0, \"+v+\".0);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \";var g=xa(n);return\"\\n float \"+r+\"(int row, int col, int depth,\\n int depth2, int depth3, int depth4) {\\n // Explicitly use integer operations as dot() only works on floats.\\n int index = row * \"+f+\" + col * \"+h+\" + depth * \"+l+\" +\\n depth2 * \"+c+\" + depth3 * \"+u+\" + depth4 + \"+g+\";\\n vec2 uv = uvFromFlat(\"+v+\", \"+m+\", index);\\n return sampleTexture(\"+n+\", uv);\\n }\\n \"}(t);default:throw new Error(e.length+\"-D input sampling is not yet supported\")}}function pa(t){var e,n,r;switch(t.shapeInfo.logicalShape.length){case 0:return e=t.name,n=\"get\"+e.charAt(0).toUpperCase()+e.slice(1),r=ua(),\"\\n vec4 \"+n+\"() {\\n return \"+r.texture2D+\"(\"+e+\", halfCR);\\n }\\n \";case 1:return function(t){var e=t.name,n=\"get\"+e.charAt(0).toUpperCase()+e.slice(1),r=t.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=ua();return\"\\n vec4 \"+n+\"(int index) {\\n vec2 uv = packedUVfrom1D(\\n \"+o[0]+\", \"+o[1]+\", index);\\n return \"+a.texture2D+\"(\"+e+\", uv);\\n }\\n \"}(t);case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,a=o[0],i=o[1],s=ua();if(null!=o&&S(e,o))return\"\\n vec4 \"+r+\"(int row, int col) {\\n vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+i+\".0, \"+a+\".0);\\n\\n return \"+s.texture2D+\"(\"+n+\", uv);\\n }\\n \";var u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],c=Math.ceil(e[1]/2);return\"\\n vec4 \"+r+\"(int row, int col) {\\n vec2 uv = packedUVfrom2D(\"+c+\", \"+u[0]+\", \"+u[1]+\", row, col);\\n return \"+s.texture2D+\"(\"+n+\", uv);\\n }\\n \"}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===e[0]){var i=e.slice(1),s=Ca(t,i);return\"\\n \"+pa(s)+\"\\n vec4 \"+r+\"(int b, int row, int col) {\\n return \"+r+\"(\"+Ea([\"b\",\"row\",\"col\"],[1,2])+\");\\n }\\n \"}var u=a[0],c=a[1],l=Math.ceil(e[2]/2),h=l*Math.ceil(e[1]/2),f=ua();return\"\\n vec4 \"+r+\"(int b, int row, int col) {\\n vec2 uv = packedUVfrom3D(\\n \"+u+\", \"+c+\", \"+h+\", \"+l+\", b, row, col);\\n return \"+f.texture2D+\"(\"+n+\", uv);\\n }\\n \"}(t);default:return function(t){for(var e=t.shapeInfo.logicalShape,n=e.length,r=t.name,o=\"get\"+r.charAt(0).toUpperCase()+r.slice(1),a=t.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=i[0],u=i[1],c=Math.ceil(e[n-1]/2),l=c*Math.ceil(e[n-2]/2),h=\"int b, int row, int col\",f=\"b * \"+l+\" + (row / 2) * \"+c+\" + (col / 2)\",d=2;d<n-1;d++)h=\"int b\"+d+\", \"+h,l*=e[n-d-1],f=\"b\"+d+\" * \"+l+\" + \"+f;var p=ua();return\"\\n vec4 \"+o+\"(\"+h+\") {\\n int index = \"+f+\";\\n int texR = index / \"+u+\";\\n int texC = index - texR * \"+u+\";\\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+u+\", \"+s+\");\\n return \"+p.texture2D+\"(\"+r+\", uv);\\n }\\n \"}(t)}}var va=\"\\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\\n int texR = index / texNumC;\\n int texC = index - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\\n int texelIndex = index / 2;\\n int texR = texelIndex / texNumC;\\n int texC = texelIndex - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",ma=\"\\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\\n int texNumC, int row, int col) {\\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\\n int texR = texelIndex / texNumC;\\n int texC = texelIndex - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",ga=\"\\nvec2 packedUVfrom3D(int texNumR, int texNumC,\\n int texelsInBatch, int texelsInLogicalRow, int b,\\n int row, int col) {\\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\\n int texR = index / texNumC;\\n int texC = index - texR * texNumC;\\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",ya=\"\\n float getChannel(vec4 frag, vec2 innerDims) {\\n vec2 modCoord = mod(innerDims, 2.);\\n return modCoord.x == 0. ?\\n (modCoord.y == 0. ? frag.r : frag.g) :\\n (modCoord.y == 0. ? frag.b : frag.a);\\n }\\n float getChannel(vec4 frag, int dim) {\\n float modCoord = mod(float(dim), 2.);\\n return modCoord == 0. ? frag.r : frag.g;\\n }\\n\";function xa(t){return\"offset\"+t}function ba(t){var e=t.name,n=k(t.shapeInfo.logicalShape);return n<2?\"return \"+e+\";\":\"\\n for (int i = 0; i < \"+n+\"; i++) {\\n if (i == index) {\\n return \"+e+\"[i];\\n }\\n }\\n \"}function wa(t){if(t<=1)return\"int\";if(2===t)return\"ivec2\";if(3===t)return\"ivec3\";if(4===t)return\"ivec4\";if(5===t)return\"ivec5\";if(6===t)return\"ivec6\";throw Error(\"GPU for rank \"+t+\" is not yet supported\")}function Ca(t,e){var n=JSON.parse(JSON.stringify(t));return n.shapeInfo.logicalShape=e,n}function Ea(t,e){return e.map((function(e){return t[e]})).join(\", \")}var Ra=function(t,e,n,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,C(t.length>2,(function(){return\"Packed arg\"+(n.charAt(0).toUpperCase()+n.slice(1))+\" supports only inputs with rank above 2.\"}));var o=t[t.length-1],a=Math.ceil(o/e);this.outputShape=t.slice(0,-1),a>1&&this.outputShape.push(a),r||this.variableNames.push(\"bestIndicesA\");var i,s,u=this.outputShape,c=u.length,l=wa(c),h=sa(\"coords\",c);if(1===a){var f=wa(s=c+1);i=\"\\n \"+f+\" sourceLocR = \"+f+\"(\"+h.join()+\", 0);\\n ++\"+h[c-1]+\";\\n \"+f+\" sourceLocG = \"+f+\"(\"+h.join()+\", 0);\\n ++\"+h[c-2]+\";\\n \"+f+\" sourceLocA = \"+f+\"(\"+h.join()+\", 0);\\n --\"+h[c-1]+\";\\n \"+f+\" sourceLocB = \"+f+\"(\"+h.join()+\", 0);\\n --\"+h[c-2]+\";\"}else s=c,i=\"\\n \"+l+\" sourceLocR = coords;\\n ++\"+h[c-1]+\";\\n \"+l+\" sourceLocG = coords;\\n ++\"+h[c-2]+\";\\n \"+l+\" sourceLocA = coords;\\n --\"+h[c-1]+\";\\n \"+l+\" sourceLocB = coords;\\n --\"+h[c-2]+\";\";var d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s),p=\".\"+d[s-1],v=d.map((function(t){return\"int \"+t})),m=sa(\"sourceLocR\",s-1).concat(\"inIdx.r\"),g=sa(\"sourceLocG\",s-1).concat(\"inIdx.g\"),y=sa(\"sourceLocB\",s-1).concat(\"inIdx.b\"),x=sa(\"sourceLocA\",s-1).concat(\"inIdx.a\"),b=\"max\"===n?\"greaterThan\":\"lessThan\",w=r?\"\":\"\\n inIdx = round(vec4(getBestIndicesAChannel(\"+m.join()+\"),\\n getBestIndicesAChannel(\"+g.join()+\"),\\n getBestIndicesAChannel(\"+y.join()+\"),\\n getBestIndicesAChannel(\"+x.join()+\")));\",E=\"vec4(\\n getAChannel(\"+m.join()+\"),\\n hasNextCol ? getAChannel(\"+g.join()+\") : 0.,\\n hasNextRow ? getAChannel(\"+y.join()+\") : 0.,\\n hasNextRow && hasNextCol ? getAChannel(\"+x.join()+\") : 0.)\",R=r?\"\":\"\\n float getBestIndicesAChannel(\"+v.join()+\") {\\n return getChannel(getBestIndicesA(\"+d.join()+\"),\\n vec2(\"+d.slice(-2).join()+\"));\\n }\";this.userCode=\"\\n float getAChannel(\"+v.join()+\") {\\n return getChannel(getA(\"+d.join()+\"),\\n vec2(\"+d.slice(-2).join()+\"));\\n }\\n \"+R+\"\\n void main() {\\n \"+l+\" coords = getOutputCoords();\\n bool hasNextCol = \"+h[c-1]+\" < \"+(u[c-1]-1)+\";\\n bool hasNextRow = \"+h[c-2]+\" < \"+(u[c-2]-1)+\";\\n \"+i+\"\\n ivec4 srcIdx = ivec4(sourceLocR\"+p+\", sourceLocG\"+p+\",\\n sourceLocB\"+p+\", sourceLocA\"+p+\") * \"+e+\";\\n ivec4 inIdx = srcIdx;\\n vec4 bestIndex = vec4(inIdx);\\n vec4 bestValue = \"+E+\";\\n\\n for (int i = 0; i < \"+e+\"; i++) {\\n inIdx = srcIdx;\\n \"+w+\"\\n vec4 candidate = \"+E+\";\\n bvec4 nan = isnan(candidate);\\n bvec4 replace = bvec4(\\n vec4(\"+b+\"(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\\n\\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\\n replace.y ? candidate.y : bestValue.y,\\n replace.z ? candidate.z : bestValue.z,\\n replace.w ? candidate.w : bestValue.w);\\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\\n srcIdx++;\\n }\\n setOutput(bestIndex);\\n }\\n \"},Ia=function(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=s-1-t.padInfo.top,l=u-1-t.padInfo.left,h=1/(e*n);this.userCode=\"\\n const ivec2 pads = ivec2(\"+c+\", \"+l+\");\\n const float avgMultiplier = float(\"+h+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n\\n ivec2 dyRCCorner = coords.yz - pads;\\n int dyRCorner = dyRCCorner.x;\\n int dyCCorner = dyRCCorner.y;\\n\\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \"+s+\";\\n wR += \"+a+\") {\\n float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n for (int wC = 0; wC < \"+u+\";\\n wC+= \"+i+\") {\\n float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n float dyValue = getDy(b, idyR, idyC, d);\\n\\n dotProd += dyValue * avgMultiplier;\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},ka=function(t){this.variableNames=[\"dy\"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,h=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=l-1-t.padInfo.front,p=h-1-t.padInfo.top,v=f-1-t.padInfo.left,m=1/(e*n*r);this.userCode=\"\\n const ivec3 pads = ivec3(\"+d+\", \"+p+\", \"+v+\");\\n const float avgMultiplier = float(\"+m+\");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int ch = coords.u;\\n\\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\\n int dyDCorner = dyCorner.x;\\n int dyRCorner = dyCorner.y;\\n int dyCCorner = dyCorner.z;\\n\\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\\n // dx(xD, xR, xC, ch).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n\\n for (int wD = 0; wD < \"+l+\";\\n wD += \"+s+\") {\\n float dyD = float(dyDCorner + wD) / \"+o+\".0;\\n\\n if (dyD < 0.0 || dyD >= \"+t.outDepth+\".0 || fract(dyD) > 0.0) {\\n continue;\\n }\\n int idyD = int(dyD);\\n\\n for (int wR = 0; wR < \"+h+\";\\n wR += \"+u+\") {\\n float dyR = float(dyRCorner + wR) / \"+a+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 ||\\n fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n for (int wC = 0; wC < \"+f+\";\\n wC += \"+c+\") {\\n float dyC = float(dyCCorner + wC) / \"+i+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\\n\\n dotProd += dyValue * avgMultiplier;\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Sa=function(t,e,n,r,o,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],Ro(t,e),Ro(t,n);var i=\"0.0\";null!=r&&(Ro(t,r),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"1.0\";null!=o&&(Ro(t,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=\"\\n void main() {\\n float x = getXAtOutCoords();\\n float mean = getMeanAtOutCoords();\\n float variance = getVarianceAtOutCoords();\\n float offset = \"+i+\";\\n float scale = \"+s+\";\\n float inv = scale * inversesqrt(variance + float(\"+a+\"));\\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\\n }\\n \"},Aa=function(t,e,n,r,o,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],Ro(t,e),Ro(t,n);var i=\"vec4(0.0)\";null!=r&&(Ro(t,r),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"vec4(1.0)\";null!=o&&(Ro(t,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=t,this.userCode=\"\\n void main() {\\n vec4 offset = \"+i+\";\\n vec4 scale = \"+s+\";\\n\\n vec4 x = getXAtOutCoords();\\n vec4 mean = getMeanAtOutCoords();\\n vec4 variance = getVarianceAtOutCoords();\\n\\n vec4 inv = scale * inversesqrt(variance + vec4(\"+a+\"));\\n\\n setOutput((x - mean) * inv + offset);\\n }\\n \"},Da=\"return areal * breal - aimag * bimag;\",Ta=\"return areal * bimag + aimag * breal;\",Na=function(t,e,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=Ro(e,n),this.userCode=\"\\n float binaryOpComplex(\\n float areal, float aimag, float breal, float bimag) {\\n \"+t+\"\\n }\\n\\n void main() {\\n float areal = getARealAtOutCoords();\\n float aimag = getAImagAtOutCoords();\\n float breal = getBRealAtOutCoords();\\n float bimag = getBImagAtOutCoords();\\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\\n }\\n \"},Fa=\"return a + b;\",_a=\"return a - b;\",Oa=\"return a * b;\",Ma=\"return (a < 0.) ? b * a : a;\",Ba=function(t,e,n){this.variableNames=[\"A\",\"B\"],this.outputShape=Ro(e,n),this.userCode=\"\\n float binaryOperation(float a, float b) {\\n \"+t+\"\\n }\\n\\n void main() {\\n float a = getAAtOutCoords();\\n float b = getBAtOutCoords();\\n setOutput(binaryOperation(a, b));\\n }\\n \"},Pa=\"\\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\\n\",La=function(t,e,n,r){void 0===r&&(r=!1),this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Ro(e,n);var o=this.outputShape.length,a=\"\";if(r)if(0===o||1===k(this.outputShape))a=\"\\n result.y = 0.;\\n result.z = 0.;\\n result.w = 0.;\\n \";else if(a=\"\\n \"+wa(o)+\" coords = getOutputCoords();\\n \",1===o)a+=\"\\n result.y = (coords + 1) >= \"+this.outputShape[0]+\" ? 0. : result.y;\\n result.z = 0.;\\n result.w = 0.;\\n \";else{var i=sa(\"coords\",o);a+=\"\\n bool nextRowOutOfBounds =\\n (\"+i[o-2]+\" + 1) >= \"+this.outputShape[o-2]+\";\\n bool nextColOutOfBounds =\\n (\"+i[o-1]+\" + 1) >= \"+this.outputShape[o-1]+\";\\n result.y = nextColOutOfBounds ? 0. : result.y;\\n result.z = nextRowOutOfBounds ? 0. : result.z;\\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\\n \"}this.userCode=\"\\n vec4 binaryOperation(vec4 a, vec4 b) {\\n \"+t+\"\\n }\\n\\n void main() {\\n vec4 a = getAAtOutCoords();\\n vec4 b = getBAtOutCoords();\\n\\n vec4 result = binaryOperation(a, b);\\n \"+a+\"\\n\\n setOutput(result);\\n }\\n \"},Wa=function(){function t(t){this.variableNames=[\"A\"],this.outputShape=t,this.userCode=\"\\n uniform float minVal;\\n uniform float maxVal;\\n\\n void main() {\\n float value = getAAtOutCoords();\\n if (isnan(value)) {\\n setOutput(value);\\n return;\\n }\\n\\n setOutput(clamp(value, minVal, maxVal));\\n }\\n \"}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,\"minVal\"),n.maxLoc=r.getUniformLocationNoThrow(o,\"maxVal\")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e)}},t}(),Ua=function(){function t(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=\"\\n uniform float minVal;\\n uniform float maxVal;\\n\\n void main() {\\n vec4 value = getAAtOutCoords();\\n\\n if (any(isnan(value))) {\\n setOutput(value);\\n return;\\n }\\n\\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\\n }\\n \"}return t.prototype.getCustomSetupFunc=function(t,e){var n=this;return function(r,o){null==n.minLoc&&(n.minLoc=r.getUniformLocationNoThrow(o,\"minVal\"),n.maxLoc=r.getUniformLocationNoThrow(o,\"maxVal\")),r.gl.uniform1f(n.minLoc,t),r.gl.uniform1f(n.maxLoc,e)}},t}(),Va=function(t){this.variableNames=[\"real\",\"imag\"],this.outputShape=t,this.userCode=\"\\n void main() {\\n float re = abs(getRealAtOutCoords());\\n float im = abs(getImagAtOutCoords());\\n float mx = max(re, im);\\n\\n // sadly the length function in glsl is not underflow-safe\\n // (at least not on Intel GPUs). So the safe solution is\\n // to ensure underflow-safety in all cases.\\n setOutput(\\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\\n );\\n }\\n \"},za=function(t){this.outputShape=[],this.outputShape=Sn(t,1),this.variableNames=t.map((function(t,e){return\"T\"+e}));var e=new Array(t.length-1);e[0]=t[0][1];for(var n=1;n<e.length;n++)e[n]=e[n-1]+t[n][1];var r=[\"if (yC < \"+e[0]+\") setOutput(getT0(yR, yC));\"];for(n=1;n<e.length;n++){var o=e[n-1];r.push(\"else if (yC < \"+e[n]+\") setOutput(getT\"+n+\"(yR, yC-\"+o+\"));\")}var a=e.length,i=e[e.length-1];r.push(\"else setOutput(getT\"+a+\"(yR, yC-\"+i+\"));\"),this.userCode=\"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int yR = coords.x;\\n int yC = coords.y;\\n\\n \"+r.join(\"\\n \")+\"\\n }\\n \"},Ga=function(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Sn(t,e);var n=this.outputShape,r=n.length,o=wa(r),a=sa(\"coords\",r),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,r);this.variableNames=t.map((function(t,e){return\"T\"+e}));var s=new Array(t.length-1);s[0]=t[0][e];for(var u=1;u<s.length;u++)s[u]=s[u-1]+t[u][e];var c=i[e],l=i.slice(-2),h=i.join(),f=\"if (\"+c+\" < \"+s[0]+\") {\\n return getChannel(\\n getT0(\"+h+\"), vec2(\"+l.join()+\"));\\n }\";for(u=1;u<s.length;u++){var d=s[u-1];f+=\"\\n if (\"+c+\" < \"+s[u]+\" && \"+c+\" >= \"+s[u-1]+\") {\\n return getChannel(\\n getT\"+u+\"(\"+Ha(i,c,d)+\"),\\n vec2(\"+Ha(l,c,d)+\"));\\n }\"}var p=s.length,v=s[s.length-1];f+=\"\\n return getChannel(\\n getT\"+p+\"(\"+Ha(i,c,v)+\"),\\n vec2(\"+Ha(l,c,v)+\"));\",this.userCode=\"\\n float getValue(\"+i.map((function(t){return\"int \"+t}))+\") {\\n \"+f+\"\\n }\\n\\n void main() {\\n \"+o+\" coords = getOutputCoords();\\n vec4 result = vec4(getValue(\"+a+\"), 0., 0., 0.);\\n\\n \"+a[r-1]+\" = \"+a[r-1]+\" + 1;\\n if (\"+a[r-1]+\" < \"+n[r-1]+\") {\\n result.g = getValue(\"+a+\");\\n }\\n\\n \"+a[r-2]+\" = \"+a[r-2]+\" + 1;\\n if (\"+a[r-2]+\" < \"+n[r-2]+\") {\\n result.a = getValue(\"+a+\");\\n }\\n\\n \"+a[r-1]+\" = \"+a[r-1]+\" - 1;\\n if (\"+a[r-2]+\" < \"+n[r-2]+\" &&\\n \"+a[r-1]+\" < \"+n[r-1]+\") {\\n result.b = getValue(\"+a+\");\\n }\\n setOutput(result);\\n }\\n \"};function Ha(t,e,n){var r=t.indexOf(e);return t.map((function(t,e){return e===r?t+\" - \"+n:t})).join()}var qa=function(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,a=\"channelsLast\"===t.dataFormat;this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int wR = coords.x;\\n int wC = coords.y;\\n int d1 = coords.z;\\n int d2 = coords.w;\\n\\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n\\n for (int b = 0; b < \"+t.batchSize+\"; b++) {\\n for (int yR = 0; yR < \"+t.outHeight+\"; yR++) {\\n int xR = wR + yR * \"+e+\" - \"+r+\";\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int yC = 0; yC < \"+t.outWidth+\"; yC++) {\\n int xC = wC + yC * \"+n+\" - \"+o+\";\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n if (\"+a+\") {\\n float dyValue = getDy(b, yR, yC, d2);\\n float xValue = getX(b, xR, xC, d1);\\n dotProd += (xValue * dyValue);\\n } else {\\n float dyValue = getDy(b, d2, yR, yC);\\n float xValue = getX(b, d1, xR, xC);\\n dotProd += (xValue * dyValue);\\n }\\n\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Ka=function(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=\"channelsLast\"===t.dataFormat,i=e-1-t.padInfo.top,s=n-1-t.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=\"\\n const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d1 = coords[\"+l+\"];\\n\\n ivec2 dyCorner = ivec2(coords[\"+u+\"], coords[\"+c+\"]) - pads;\\n int dyRCorner = dyCorner.x;\\n int dyCCorner = dyCorner.y;\\n\\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \"+e+\"; wR++) {\\n float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n int wRPerm = \"+e+\" - 1 - wR;\\n\\n for (int wC = 0; wC < \"+n+\"; wC++) {\\n float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n int wCPerm = \"+n+\" - 1 - wC;\\n\\n for (int d2 = 0; d2 < \"+t.outChannels+\"; d2++) {\\n\\n if (\"+a+\") {\\n float xValue = getDy(batch, idyR, idyC, d2);\\n float wValue = getW(wRPerm, wCPerm, d1, d2);\\n dotProd += xValue * wValue;\\n } else {\\n float xValue = getDy(batch, d2, idyR, idyC);\\n float wValue = getW(wRPerm, wCPerm, d1, d2);\\n dotProd += xValue * wValue;\\n }\\n\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},ja=function(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode=\"\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int wF = coords.x;\\n int wR = coords.y;\\n int wC = coords.z;\\n int d1 = coords.w;\\n int d2 = coords.u;\\n\\n float dotProd = 0.0;\\n\\n for (int b = 0; b < \"+t.batchSize+\"; b++) {\\n for (int yF = 0; yF < \"+t.outDepth+\"; yF++) {\\n int xF = wF + yF * \"+e+\" - \"+o+\";\\n\\n if (xF < 0 || xF >= \"+t.inDepth+\") {\\n continue;\\n }\\n\\n for (int yR = 0; yR < \"+t.outHeight+\"; yR++) {\\n int xR = wR + yR * \"+n+\" - \"+a+\";\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int yC = 0; yC < \"+t.outWidth+\"; yC++) {\\n int xC = wC + yC * \"+r+\" - \"+i+\";\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n float dyValue = getDy(b, yF, yR, yC, d2);\\n float xValue = getX(b, xF, xR, xC, d1);\\n dotProd += (xValue * dyValue);\\n }\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Xa=function(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,c=r-1-t.padInfo.left;this.userCode=\"\\n const ivec3 pads = ivec3(\"+s+\", \"+u+\", \"+c+\");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int d1 = coords.u;\\n\\n\\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\\n int dyFCorner = dyCorner.x;\\n int dyRCorner = dyCorner.y;\\n int dyCCorner = dyCorner.z;\\n\\n float dotProd = 0.0;\\n for (int wF = 0; wF < \"+e+\"; wF++) {\\n float dyF = float(dyFCorner + wF) / \"+o+\".0;\\n\\n if (dyF < 0.0 || dyF >= \"+t.outDepth+\".0 || fract(dyF) > 0.0) {\\n continue;\\n }\\n int idyF = int(dyF);\\n\\n int wFPerm = \"+e+\" - 1 - wF;\\n\\n for (int wR = 0; wR < \"+n+\"; wR++) {\\n float dyR = float(dyRCorner + wR) / \"+a+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 ||\\n fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n int wRPerm = \"+n+\" - 1 - wR;\\n\\n for (int wC = 0; wC < \"+r+\"; wC++) {\\n float dyC = float(dyCCorner + wC) / \"+i+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n int wCPerm = \"+r+\" - 1 - wC;\\n\\n for (int d2 = 0; d2 < \"+t.outChannels+\"; d2++) {\\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\\n dotProd += xValue * wValue;\\n }\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Ya=function(t){this.variableNames=[\"x\",\"dy\"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int wR = coords.x;\\n int wC = coords.y;\\n int d1 = coords.z;\\n int dm = coords.w;\\n int d2 = d1 * \"+a+\" + dm;\\n\\n float dotProd = 0.0;\\n\\n // TO DO: Vec4 over the batch size\\n for (int b = 0; b < \"+t.batchSize+\"; b++) {\\n for (int yR = 0; yR < \"+t.outHeight+\"; yR++) {\\n int xR = wR + yR * \"+e+\" - \"+r+\";\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int yC = 0; yC < \"+t.outWidth+\"; yC++) {\\n int xC = wC + yC * \"+n+\" - \"+o+\";\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n float dyValue = getDy(b, yR, yC, d2);\\n float xValue = getX(b, xR, xC, d1);\\n dotProd += (xValue * dyValue);\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},$a=function(t){this.variableNames=[\"dy\",\"W\"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=e-1-t.padInfo.top,i=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode=\"\\n const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d1 = coords[3];\\n ivec2 dyCorner = coords.yz - pads;\\n int dyRCorner = dyCorner.x;\\n int dyCCorner = dyCorner.y;\\n\\n float dotProd = 0.0;\\n\\n for (int wR = 0; wR < \"+e+\"; wR++) {\\n float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n int wRPerm = \"+e+\" - 1 - wR;\\n\\n for (int wC = 0; wC < \"+n+\"; wC++) {\\n float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n int wCPerm = \"+n+\" - 1 - wC;\\n\\n // TO DO: Vec4 over the channelMul\\n for (int dm = 0; dm < \"+s+\"; dm++) {\\n int d2 = d1 * \"+s+\" + dm;\\n float xValue = getDy(batch, idyR, idyC, d2);\\n float wValue = getW(wRPerm, wCPerm, d1, dm);\\n dotProd += xValue * wValue;\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Qa=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;var o=t.padInfo.top,a=t.padInfo.left,i=t.strideHeight,s=t.strideWidth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterHeight,h=t.filterWidth,f=4*Math.floor(t.inChannels/4),d=t.inChannels%4,p=\"channelsLast\"===t.dataFormat,v=p?1:2,m=p?2:3,g=p?3:1,y=\"\",x=\"\";n&&(y=r?\"float activation(float a) {\\n float b = getPreluActivationWeightsAtOutCoords();\\n \"+n+\"\\n }\":\"\\n float activation(float x) {\\n \"+n+\"\\n }\\n \",x=\"result = activation(result);\");var b=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),r&&this.variableNames.push(\"preluActivationWeights\"),this.userCode=\"\\n \"+y+\"\\n\\n const ivec2 strides = ivec2(\"+i+\", \"+s+\");\\n const ivec2 pads = ivec2(\"+o+\", \"+a+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d2 = coords[\"+g+\"];\\n\\n ivec2 xRCCorner =\\n ivec2(coords[\"+v+\"], coords[\"+m+\"]) * strides - pads;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \"+l+\"; wR++) {\\n int xR = xRCorner + wR * \"+u+\";\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+h+\"; wC++) {\\n int xC = xCCorner + wC * \"+c+\";\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n for (int d1 = 0; d1 < \"+f+\"; d1 += 4) {\\n vec4 wValues = vec4(\\n getW(wR, wC, d1, d2),\\n getW(wR, wC, d1 + 1, d2),\\n getW(wR, wC, d1 + 2, d2),\\n getW(wR, wC, d1 + 3, d2)\\n );\\n\\n if (\"+p+\") {\\n vec4 xValues = vec4(\\n getX(batch, xR, xC, d1),\\n getX(batch, xR, xC, d1 + 1),\\n getX(batch, xR, xC, d1 + 2),\\n getX(batch, xR, xC, d1 + 3)\\n );\\n dotProd += dot(xValues, wValues);\\n } else {\\n vec4 xValues = vec4(\\n getX(batch, d1, xR, xC),\\n getX(batch, d1 + 1, xR, xC),\\n getX(batch, d1 + 2, xR, xC),\\n getX(batch, d1 + 3, xR, xC)\\n );\\n dotProd += dot(xValues, wValues);\\n }\\n }\\n\\n if (\"+(1===d)+\") {\\n\\n if (\"+p+\") {\\n dotProd +=\\n getX(batch, xR, xC, \"+f+\") *\\n getW(wR, wC, \"+f+\", d2);\\n } else {\\n dotProd +=\\n getX(batch, \"+f+\", xR, xC) *\\n getW(wR, wC, \"+f+\", d2);\\n }\\n\\n } else if (\"+(2===d)+\") {\\n vec2 wValues = vec2(\\n getW(wR, wC, \"+f+\", d2),\\n getW(wR, wC, \"+f+\" + 1, d2)\\n );\\n\\n if (\"+p+\") {\\n vec2 xValues = vec2(\\n getX(batch, xR, xC, \"+f+\"),\\n getX(batch, xR, xC, \"+f+\" + 1)\\n );\\n dotProd += dot(xValues, wValues);\\n } else {\\n vec2 xValues = vec2(\\n getX(batch, \"+f+\", xR, xC),\\n getX(batch, \"+f+\" + 1, xR, xC)\\n );\\n dotProd += dot(xValues, wValues);\\n }\\n\\n } else if (\"+(3===d)+\") {\\n vec3 wValues = vec3(\\n getW(wR, wC, \"+f+\", d2),\\n getW(wR, wC, \"+f+\" + 1, d2),\\n getW(wR, wC, \"+f+\" + 2, d2)\\n );\\n\\n if (\"+p+\") {\\n vec3 xValues = vec3(\\n getX(batch, xR, xC, \"+f+\"),\\n getX(batch, xR, xC, \"+f+\" + 1),\\n getX(batch, xR, xC, \"+f+\" + 2)\\n );\\n dotProd += dot(xValues, wValues);\\n } else {\\n vec3 xValues = vec3(\\n getX(batch, \"+f+\", xR, xC),\\n getX(batch, \"+f+\" + 1, xR, xC),\\n getX(batch, \"+f+\" + 2, xR, xC)\\n );\\n dotProd += dot(xValues, wValues);\\n }\\n\\n }\\n }\\n }\\n\\n float result = dotProd;\\n \"+b+\"\\n \"+x+\"\\n setOutput(result);\\n }\\n \"},Ja=function(t){this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,h=t.filterHeight,f=t.filterWidth,d=4*Math.floor(t.inChannels/4),p=t.inChannels%4;this.userCode=\"\\n const ivec3 strides = ivec3(\"+o+\", \"+a+\", \"+i+\");\\n const ivec3 pads = ivec3(\"+e+\", \"+n+\", \"+r+\");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int d2 = coords.u;\\n\\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\\n int xFCorner = xFRCCorner.x;\\n int xRCorner = xFRCCorner.y;\\n int xCCorner = xFRCCorner.z;\\n\\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\\n // values in that axis.\\n float dotProd = 0.0;\\n for (int wF = 0; wF < \"+l+\"; wF++) {\\n int xF = xFCorner + wF * \"+s+\";\\n\\n if (xF < 0 || xF >= \"+t.inDepth+\") {\\n continue;\\n }\\n\\n for (int wR = 0; wR < \"+h+\"; wR++) {\\n int xR = xRCorner + wR * \"+u+\";\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+f+\"; wC++) {\\n int xC = xCCorner + wC * \"+c+\";\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n for (int d1 = 0; d1 < \"+d+\"; d1 += 4) {\\n vec4 xValues = vec4(\\n getX(batch, xF, xR, xC, d1),\\n getX(batch, xF, xR, xC, d1 + 1),\\n getX(batch, xF, xR, xC, d1 + 2),\\n getX(batch, xF, xR, xC, d1 + 3)\\n );\\n vec4 wValues = vec4(\\n getW(wF, wR, wC, d1, d2),\\n getW(wF, wR, wC, d1 + 1, d2),\\n getW(wF, wR, wC, d1 + 2, d2),\\n getW(wF, wR, wC, d1 + 3, d2)\\n );\\n\\n dotProd += dot(xValues, wValues);\\n }\\n\\n if (\"+(1===p)+\") {\\n dotProd +=\\n getX(batch, xF, xR, xC, \"+d+\") *\\n getW(wF, wR, wC, \"+d+\", d2);\\n } else if (\"+(2===p)+\") {\\n vec2 xValues = vec2(\\n getX(batch, xF, xR, xC, \"+d+\"),\\n getX(batch, xF, xR, xC, \"+d+\" + 1)\\n );\\n vec2 wValues = vec2(\\n getW(wF, wR, wC, \"+d+\", d2),\\n getW(wF, wR, wC, \"+d+\" + 1, d2)\\n );\\n dotProd += dot(xValues, wValues);\\n } else if (\"+(3===p)+\") {\\n vec3 xValues = vec3(\\n getX(batch, xF, xR, xC, \"+d+\"),\\n getX(batch, xF, xR, xC, \"+d+\" + 1),\\n getX(batch, xF, xR, xC, \"+d+\" + 2)\\n );\\n vec3 wValues = vec3(\\n getW(wF, wR, wC, \"+d+\", d2),\\n getW(wF, wR, wC, \"+d+\" + 1, d2),\\n getW(wF, wR, wC, \"+d+\" + 2, d2)\\n );\\n dotProd += dot(xValues, wValues);\\n }\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Za=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=[\"x\",\"W\"],this.outputShape=t.outShape;var o=t.inHeight,a=t.inWidth,i=t.padInfo.top,s=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,f=t.filterHeight,d=t.filterWidth,p=t.outChannels/t.inChannels,v=\"\",m=\"\";n&&(v=r?\"float activation(float a) {\\n float b = getPreluActivationWeightsAtOutCoords();\\n \"+n+\"\\n }\":\"\\n float activation(float x) {\\n \"+n+\"\\n }\\n \",m=\"result = activation(result);\");var g=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),r&&this.variableNames.push(\"preluActivationWeights\"),this.userCode=\"\\n \"+v+\"\\n\\n const ivec2 strides = ivec2(\"+u+\", \"+c+\");\\n const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords.x;\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int d2 = coords.w;\\n int d1 = d2 / \"+p+\";\\n int q = d2 - d1 * \"+p+\";\\n\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\\n for (int wR = 0; wR < \"+f+\"; wR++) {\\n int xR = xRCorner + wR * \"+l+\";\\n\\n if (xR < 0 || xR >= \"+o+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+d+\"; wC++) {\\n int xC = xCCorner + wC * \"+h+\";\\n\\n if (xC < 0 || xC >= \"+a+\") {\\n continue;\\n }\\n\\n float xVal = getX(batch, xR, xC, d1);\\n float wVal = getW(wR, wC, d1, q);\\n dotProd += xVal * wVal;\\n }\\n }\\n\\n float result = dotProd;\\n \"+g+\"\\n \"+m+\"\\n setOutput(result);\\n }\\n \"},ti=function(t,e,n,r){void 0===e&&(e=!1),void 0===n&&(n=null),void 0===r&&(r=!1),this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;for(var o=t.inHeight,a=t.inWidth,i=t.padInfo.top,s=t.padInfo.left,u=t.strideHeight,c=t.strideWidth,l=t.dilationHeight,h=t.dilationWidth,f=t.filterHeight,d=t.filterWidth,p=d,v=\"int xR; int xC; int xCOffset;\",m=0;m<f;m++)for(var g=0;g<d;g++)v+=\"\\n vec4 xTexelR\"+m+\"C\"+2*g+\" = vec4(0.);\\n vec4 wR\"+m+\"C\"+g+\" = vec4(0.);\\n vec4 xR\"+m+\"C\"+g+\" = vec4(0.);\";for(m=0;m<f;m++)for(var y=0;y<p;y++){if(v+=\"\\n xR = xRCorner + \"+m*l+\";\\n xC = xCCorner + \"+(g=2*y)*h+\";\\n \",1===c){if(g<d&&(v+=s%2==1?\"\\n xCOffset = xC + 1;\\n if(xR >= 0 && xR < \"+o+\" && xCOffset >= 0 && xCOffset < \"+a+\") {\\n xTexelR\"+m+\"C\"+g+\" = getX(batch, xR, xCOffset, d1);\\n\\n // Need to manually clear unused channels in case\\n // we're reading from recycled texture.\\n if(xCOffset + 1 >= \"+a+\") {\\n xTexelR\"+m+\"C\"+g+\".zw = vec2(0.);\\n }\\n } else {\\n xTexelR\"+m+\"C\"+g+\" = vec4(0.);\\n }\\n\\n xCOffset = xC + 1 - 2;\\n if(xR >= 0 && xR < \"+o+\" && xCOffset >= 0 && xCOffset < \"+a+\") {\\n vec4 previous = getX(batch, xR, xCOffset, d1);\\n\\n // Need to manually clear unused channels in case\\n // we're reading from recycled texture.\\n if(xCOffset + 1 >= \"+a+\") {\\n previous.zw = vec2(0.);\\n }\\n\\n xR\"+m+\"C\"+g+\" = vec4(previous.zw, xTexelR\"+m+\"C\"+g+\".xy);\\n } else {\\n xR\"+m+\"C\"+g+\" = vec4(0, 0, xTexelR\"+m+\"C\"+g+\".xy);\\n }\\n \":\"\\n if(xR >= 0 && xR < \"+o+\" && xC >= 0 && xC < \"+a+\") {\\n xTexelR\"+m+\"C\"+g+\" = getX(batch, xR, xC, d1);\\n } else {\\n xTexelR\"+m+\"C\"+g+\" = vec4(0.);\\n }\\n\\n xR\"+m+\"C\"+g+\" = xTexelR\"+m+\"C\"+g+\";\\n \",g+1<d)){var x=s%2==0?b(h):h;h%2==0&&s%2==1||h%2!=0&&s%2!=1?(v+=\"\\n xCOffset = xC + \"+s%2+\" + \"+x+\";\\n\\n if(xR >= 0 && xR < \"+o+\" &&\\n xCOffset >= 0 && xCOffset < \"+a+\") {\\n xTexelR\"+m+\"C\"+(g+2)+\" = getX(batch, xR, xCOffset, d1);\\n }\\n \",h>1&&(v+=\"\\n xCOffset -= 2;\\n if(xR >= 0 && xR < \"+o+\" &&\\n xCOffset >= 0 && xCOffset < \"+a+\") {\\n xTexelR\"+m+\"C\"+g+\" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\"+m+\"C\"+g+\" = vec4(0.);\\n }\\n \"),v+=\"\\n xR\"+m+\"C\"+(g+1)+\" = vec4(\\n xTexelR\"+m+\"C\"+g+\".zw, xTexelR\"+m+\"C\"+(g+2)+\".xy);\\n \"):v+=\"\\n xCOffset = xC + \"+x+\";\\n\\n if(xR >= 0 && xR < \"+o+\" &&\\n xCOffset >= 0 && xCOffset < \"+a+\") {\\n xTexelR\"+m+\"C\"+(g+2)+\" = getX(batch, xR, xCOffset, d1);\\n }\\n\\n xR\"+m+\"C\"+(g+1)+\" = xTexelR\"+m+\"C\"+(g+2)+\";\\n \"}}else g<d&&(v+=\"\\n if(xR >= 0 && xR < \"+o+\") {\\n \",s%2==1?(v+=\"\\n xCOffset = xC + 1 - \"+c+\";\\n if(xCOffset >= 0 && xCOffset < \"+a+\") {\\n xTexelR\"+m+\"C\"+g+\" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\"+m+\"C\"+g+\" = vec4(0.);\\n }\\n\\n if(xC + 1 >= 0 && xC + 1 < \"+a+\") {\\n xTexelR\"+m+\"C\"+(g+2)+\" = getX(batch, xR, xC + 1, d1);\\n } else {\\n xTexelR\"+m+\"C\"+(g+2)+\" = vec4(0.);\\n }\\n\\n xR\"+m+\"C\"+g+\" = vec4(\\n xTexelR\"+m+\"C\"+g+\".zw, xTexelR\"+m+\"C\"+(g+2)+\".zw);\\n \",g+1<d&&(v+=\"\\n vec4 final = vec4(0.);\\n xCOffset = xC + 1 + \"+c+\";\\n if(xCOffset >= 0 && xCOffset < \"+a+\") {\\n final = getX(batch, xR, xCOffset, d1);\\n }\\n xR\"+m+\"C\"+(g+1)+\" = vec4(xTexelR\"+m+\"C\"+(g+2)+\".xy, final.xy);\\n \")):(v+=\"\\n if(xC >= 0 && xC < \"+a+\") {\\n xTexelR\"+m+\"C\"+g+\" = getX(batch, xR, xC, d1);\\n } else {\\n xTexelR\"+m+\"C\"+g+\" = vec4(0.);\\n }\\n\\n xCOffset = xC + \"+c+\";\\n if(xCOffset >= 0 && xCOffset < \"+a+\") {\\n xTexelR\"+m+\"C\"+(g+2)+\" = getX(batch, xR, xCOffset, d1);\\n } else {\\n xTexelR\"+m+\"C\"+(g+2)+\" = vec4(0.);\\n }\\n\\n xR\"+m+\"C\"+g+\" = vec4(\\n xTexelR\"+m+\"C\"+g+\".xy, xTexelR\"+m+\"C\"+(g+2)+\".xy);\\n \",g+1<d&&(v+=\"\\n xR\"+m+\"C\"+(g+1)+\" = vec4(\\n xTexelR\"+m+\"C\"+g+\".zw, xTexelR\"+m+\"C\"+(g+2)+\".zw);\\n \")),v+=\"}\");g<d&&(v+=\"\\n vec4 wTexelR\"+m+\"C\"+g+\" = getW(\"+m+\", \"+g+\", d1, q);\\n wR\"+m+\"C\"+g+\" = vec4(wTexelR\"+m+\"C\"+g+\".xz, wTexelR\"+m+\"C\"+g+\".xz);\\n \",g+1<d&&(v+=\"\\n vec4 wTexelR\"+m+\"C\"+(g+1)+\" = getW(\"+m+\", \"+(g+1)+\", d1, q);\\n wR\"+m+\"C\"+(g+1)+\" =\\n vec4(wTexelR\"+m+\"C\"+(g+1)+\".xz, wTexelR\"+m+\"C\"+(g+1)+\".xz);\"))}for(m=0;m<f;m++)for(g=0;g<d;g++)v+=\"dotProd += xR\"+m+\"C\"+g+\" * wR\"+m+\"C\"+g+\";\";var w=\"\",C=\"\";n&&(w=r?\"vec4 activation(vec4 a) {\\n vec4 b = getPreluActivationWeightsAtOutCoords();\\n \"+n+\"\\n }\":\"vec4 activation(vec4 x) {\\n \"+n+\"\\n }\",C=\"result = activation(result);\");var E=e?\"result += getBiasAtOutCoords();\":\"\";e&&this.variableNames.push(\"bias\"),r&&this.variableNames.push(\"preluActivationWeights\"),this.userCode=\"\\n \"+w+\"\\n\\n const ivec2 strides = ivec2(\"+u+\", \"+c+\");\\n const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\n\\n void main() {\\n\\n ivec4 coords = getOutputCoords();\\n int batch = coords.x;\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int d2 = coords.w;\\n int d1 = d2;\\n int q = 0;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n vec4 dotProd = vec4(0.);\\n\\n \"+v+\"\\n\\n vec4 result = dotProd;\\n \"+E+\"\\n \"+C+\"\\n setOutput(result);\\n }\\n \"},ei=function(t,e,n,r,o){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];var a=t[0],i=t[1],s=t[2],u=t[3],c=e[0],l=n[0],h=n[1];this.outputShape=[c,l,h,u];var f=\"bilinear\"===r?1:0,d=[i-1+\".0\",s-1+\".0\"],p=d[0],v=d[1],m=l>1?[\"\"+(i-1)/(l-1),\"(y2-y1) * height_ratio\",\"y1*\"+p+\" + float(y)*(height_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (y1+y2) * \"+p],g=m[0],y=m[1],x=m[2],b=h>1?[\"\"+(s-1)/(h-1),\"(x2-x1) * width_ratio\",\"x1*\"+v+\" + float(x)*(width_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (x1+x2) * \"+v],w=b[0],C=b[1],E=b[2];this.userCode=\"\\n const float height_ratio = float(\"+g+\");\\n const float width_ratio = float(\"+w+\");\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int y = coords[1];\\n int x = coords[2];\\n int d = coords[3];\\n\\n // get box vals\\n float y1 = getBoxes(b,0);\\n float x1 = getBoxes(b,1);\\n float y2 = getBoxes(b,2);\\n float x2 = getBoxes(b,3);\\n\\n // get image in batch index\\n int bInd = round(getBoxInd(b));\\n if(bInd < 0 || bInd >= \"+a+\") {\\n return;\\n }\\n\\n float height_scale = \"+y+\";\\n float width_scale = \"+C+\";\\n\\n float in_y = \"+x+\";\\n if( in_y < 0.0 || in_y > \"+p+\" ) {\\n setOutput(float(\"+o+\"));\\n return;\\n }\\n float in_x = \"+E+\";\\n if( in_x < 0.0 || in_x > \"+v+\" ) {\\n setOutput(float(\"+o+\"));\\n return;\\n }\\n\\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\\n if(\"+f+\" == 1) {\\n // Compute the four integer indices.\\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\\n\\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\\n\\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\\n\\n float top = topLeft + (topRight - topLeft) * fracCR.x;\\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\\n float newValue = top + (bottom - top) * fracCR.y;\\n setOutput(newValue);\\n } else {\\n // Compute the coordinators of nearest neighbor point.\\n ivec2 sourceNearestCR = ivec2(floor(\\n sourceFracIndexCR + vec2(0.5,0.5)));\\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\\n setOutput(newValue);\\n }\\n }\\n \"},ni=function(t,e,n){this.variableNames=[\"x\"],this.outputShape=t;var r=t.length,o=t[t.length-1],a=n?\"<\":\">\";this.userCode=\"\\n int getIndex(int i) {\\n \"+(n?\"return \"+o+\" -i - 1;\":\"return i;\")+\"\\n }\\n\\n void main() {\\n \"+wa(r)+\" coords = getOutputCoords();\\n int end = \"+ri(r,\"coords\")+\";\\n float val = 0.0;\\n for (int i = \"+o+\" - 1; i >= 0; i -= 1) {\\n int idx = getIndex(i);\\n if (idx \"+a+\" end) {\\n continue;\\n }\\n if (idx == end && \"+e+\") {\\n continue;\\n }\\n \"+ri(r,\"coords\")+\" = idx;\\n val += getX(\"+function(t,e){if(1===t)return\"\"+e;if(2===t)return e+\".x, \"+e+\".y\";if(3===t)return e+\".x, \"+e+\".y, \"+e+\".z\";if(4===t)return e+\".x, \"+e+\".y, \"+e+\".z, \"+e+\".w\";throw Error(\"Cumulative sum for rank \"+t+\" is not yet supported\")}(r,\"coords\")+\");\\n }\\n setOutput(val);\\n }\\n \"};function ri(t,e){if(1===t)return\"\"+e;if(2===t)return e+\".y\";if(3===t)return e+\".z\";if(4===t)return e+\".w\";throw Error(\"Cumulative sum for rank \"+t+\" is not yet supported\")}var oi=function(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Vt.DENSE;var e=Yt(t),n=ua();this.outputShape=t,this.userCode=\"\\n ivec3 outCoordsFromFlatIndex(int index) {\\n \"+ca([\"r\",\"c\",\"d\"],t)+\"\\n return ivec3(r, c, d);\\n }\\n\\n void main() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = 4 * (resTexRC.x * \"+e[1]+\" + resTexRC.y);\\n\\n vec4 result = vec4(0.);\\n\\n for (int i=0; i<4; i++) {\\n int flatIndex = index + i;\\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\\n result[i] = getA(rc.x, rc.y, rc.z);\\n }\\n\\n \"+n.output+\" = result;\\n }\\n \"},ai=function(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Vt.DENSE;var e=Yt(t),n=ua();this.outputShape=t,this.userCode=\"\\n ivec3 outCoordsFromFlatIndex(int index) {\\n \"+ca([\"r\",\"c\",\"d\"],t)+\"\\n return ivec3(r, c, d);\\n }\\n\\n void main() {\\n ivec2 resTexRC = ivec2(resultUV.yx *\\n vec2(\"+e[0]+\", \"+e[1]+\"));\\n int index = 4 * (resTexRC.x * \"+e[1]+\" + resTexRC.y);\\n\\n vec4 result = vec4(0.);\\n\\n for (int i=0; i<4; i++) {\\n int flatIndex = index + i;\\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\\n }\\n\\n \"+n.output+\" = result;\\n }\\n \"},ii=function(){function t(t,e,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int h = \"+this.getHeightCoordString()+\";\\n int w = \"+this.getWidthCoordString()+\";\\n int d = \"+this.getDepthCoordString()+\";\\n\\n int in_h = h / \"+e+\";\\n int offset_h = imod(h, \"+e+\");\\n int in_w = w / \"+e+\";\\n int offset_w = imod(w, \"+e+\");\\n int offset_d = (offset_h * \"+e+\" + offset_w) *\\n \"+this.getOutputDepthSize()+\";\\n int in_d = d + offset_d;\\n\\n float result = \"+this.getInputSamplingString()+\";\\n setOutput(result);\\n }\\n \"}return t.prototype.getHeightCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[1]\":\"coords[2]\"},t.prototype.getWidthCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[2]\":\"coords[3]\"},t.prototype.getDepthCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[3]\":\"coords[1]\"},t.prototype.getOutputDepthSize=function(){return\"NHWC\"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return\"NHWC\"===this.dataFormat?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"},t}(),si=function(t){this.variableNames=[\"X\"],this.outputShape=[t,t],this.userCode=\"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\\n setOutput(val);\\n }\\n \"},ui=function(t){this.variableNames=[\"A\"],this.outTexUsage=zt.DOWNLOAD;var e=ua();this.outputShape=t,this.userCode=\"\\n \"+ha+\"\\n\\n void main() {\\n float x = getAAtOutCoords();\\n \"+e.output+\" = encode_float(x);\\n }\\n \"},ci=function(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=zt.DOWNLOAD;var e=ua();this.outputShape=t,this.userCode=\"\\n \"+ha+\"\\n\\n void main() {\\n ivec3 coords = getOutputCoords();\\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\\n \"+e.output+\" = encode_float(x);\\n }\\n \"},li=function(t,e,n){void 0===n&&(n=!1),this.variableNames=[\"A\"];var r=ua(),o=e[0],a=e[1];this.outputShape=t;var i=\"result\";n&&(i=\"floor(result * 255. + 0.5)\"),this.userCode=\"\\n \"+la(t)+\"\\n\\n void main() {\\n ivec3 coords = getOutputCoords();\\n\\n int flatIndex = getFlatIndex(coords);\\n int offset = imod(flatIndex, 4);\\n\\n flatIndex = idiv(flatIndex, 4, 1.);\\n \\n int r = flatIndex / \"+a+\";\\n int c = imod(flatIndex, \"+a+\");\\n vec2 uv = (vec2(c, r) + halfCR) / vec2(\"+a+\".0, \"+o+\".0);\\n vec4 values = \"+r.texture2D+\"(A, uv);\\n\\n float result;\\n\\n if(offset == 0) {\\n result = values[0];\\n } else if(offset == 1) {\\n result = values[1];\\n } else if(offset == 2) {\\n result = values[2];\\n } else {\\n result = values[3];\\n }\\n\\n \"+r.output+\" = vec4(\"+i+\", 0., 0., 0.);\\n }\\n \"},hi=function(t,e,n){void 0===n&&(n=!1),this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;var r=ua(),o=e[0],a=e[1];this.outputShape=t;var i=\"\",s=\"result\";n&&(s=\"floor(result * 255. + 0.5)\");for(var u=0;u<=1;u++)for(var c=0;c<=1;c++){var l=2*u+c;i+=\"\\n localCoords = coords;\\n if(localCoords[2] + \"+c+\" < \"+t[2]+\") {\\n localCoords[2] += \"+c+\";\\n if(localCoords[1] + \"+u+\" < \"+t[1]+\") {\\n localCoords[1] += \"+u+\";\\n\\n flatIndex = getFlatIndex(localCoords);\\n offset = imod(flatIndex, 4);\\n\\n flatIndex = idiv(flatIndex, 4, 1.);\\n\\n r = flatIndex / \"+a+\";\\n c = imod(flatIndex, \"+a+\");\\n uv = (vec2(c, r) + halfCR) / vec2(\"+a+\".0, \"+o+\".0);\\n values = \"+r.texture2D+\"(A, uv);\\n\\n if(offset == 0) {\\n result[\"+l+\"] = values[0];\\n } else if(offset == 1) {\\n result[\"+l+\"] = values[1];\\n } else if(offset == 2) {\\n result[\"+l+\"] = values[2];\\n } else {\\n result[\"+l+\"] = values[3];\\n }\\n }\\n }\\n \"}this.userCode=\"\\n \"+la(t)+\"\\n\\n void main() {\\n ivec3 coords = getOutputCoords();\\n\\n vec4 result = vec4(0.);\\n int flatIndex, r, c, offset;\\n ivec3 localCoords;\\n vec2 uv;\\n vec4 values;\\n\\n \"+i+\"\\n\\n \"+r.output+\" = \"+s+\";\\n }\\n \"},fi=\"return real * expR - imag * expI;\",di=\"return real * expI + imag * expR;\",pi=function(t,e,n){this.variableNames=[\"real\",\"imag\"];var r=e[1];this.outputShape=e;var o=n?\"2.0 * \"+Math.PI:\"-2.0 * \"+Math.PI,a=n?r+\".0\":\"1.0\";this.userCode=\"\\n const float exponentMultiplier = \"+o+\";\\n\\n float unaryOpComplex(float real, float expR, float imag, float expI) {\\n \"+t+\"\\n }\\n\\n float mulMatDFT(int batch, int index) {\\n float indexRatio = float(index) / float(\"+r+\");\\n float exponentMultiplierTimesIndexRatio =\\n exponentMultiplier * indexRatio;\\n\\n float result = 0.0;\\n\\n for (int i = 0; i < \"+r+\"; i++) {\\n // x = (-2|2 * PI / N) * index * i;\\n float x = exponentMultiplierTimesIndexRatio * float(i);\\n float expR = cos(x);\\n float expI = sin(x);\\n float real = getReal(batch, i);\\n float imag = getImag(batch, i);\\n\\n result +=\\n unaryOpComplex(real, expR, imag, expI) / \"+a+\";\\n }\\n\\n return result;\\n }\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n setOutput(mulMatDFT(coords[0], coords[1]));\\n }\\n \"},vi=function(){function t(t,e){this.outputShape=[],this.variableNames=[\"x\"],this.outputShape=t,this.userCode=\"\\n uniform float value;\\n void main() {\\n // Input can be obtained from uniform value.\\n setOutput(value);\\n }\\n \"}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.valueLoc&&(e.valueLoc=n.getUniformLocationNoThrow(r,\"value\")),n.gl.uniform1f(e.valueLoc,t)}},t}(),mi=function(t,e,n){this.variableNames=[\"A\",\"indices\"];var r=t.slice();r[n]=e,this.outputShape=r,this.rank=r.length;var o=wa(this.rank),a=function(t,e){var n=t.length;if(n>4)throw Error(\"Gather for rank \"+n+\" is not yet supported\");if(1===n)return\"int(getIndices(resRC))\";for(var r=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],a=0;a<t.length;a++)a===e?o.push(\"int(getIndices(\"+r[a]+\"))\"):o.push(\"\"+r[a]);return o.join()}(t,n);this.userCode=\"\\n void main() {\\n \"+o+\" resRC = getOutputCoords();\\n setOutput(getA(\"+a+\"));\\n }\\n \"};var gi=function(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;var r=wa(e.length),o=wa(n.length),a=this.sliceDim>1?\"strides[j]\":\"strides\";this.userCode=\"\\n \"+r+\" strides = \"+r+\"(\"+this.strides+\");\\n void main() {\\n \"+o+\" coords = getOutputCoords();\\n int flattenIndex = 0;\\n for (int j = 0; j < \"+this.sliceDim+\"; j++) {\\n int index = round(getIndices(coords[0], j));\\n flattenIndex += index * \"+a+\";\\n }\\n setOutput(getX(flattenIndex, coords[1]));\\n }\\n \"};function yi(t,e){var n=ua();return oe(t,e,n.version+\"\\n precision highp float;\\n \"+n.attribute+\" vec3 clipSpacePos;\\n \"+n.attribute+\" vec2 uv;\\n \"+n.varyingVs+\" vec2 resultUV;\\n\\n void main() {\\n gl_Position = vec4(clipSpacePos, 1);\\n resultUV = uv;\\n }\")}function xi(t,e){return fe(t,e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function bi(t,e){return de(t,e,new Uint16Array([0,1,2,2,1,3]))}function wi(t,e,n,r,o,a,i){ve(n,r);var s=pe(t,e),u=t.TEXTURE_2D;return Jt(t,e,(function(){return t.bindTexture(u,s)})),Jt(t,e,(function(){return t.texParameteri(u,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE)})),Jt(t,e,(function(){return t.texParameteri(u,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE)})),Jt(t,e,(function(){return t.texParameteri(u,t.TEXTURE_MIN_FILTER,t.NEAREST)})),Jt(t,e,(function(){return t.texParameteri(u,t.TEXTURE_MAG_FILTER,t.NEAREST)})),Jt(t,e,(function(){return t.texImage2D(u,0,o,n,r,0,a,i,null)})),Jt(t,e,(function(){return t.bindTexture(t.TEXTURE_2D,null)})),s}function Ci(t,e,n,r,o){var a=Xt(n,r);return wi(t,e,a[0],a[1],o.internalFormatFloat,o.textureFormatFloat,t.FLOAT)}function Ei(t,e,n,r,o){var a=Xt(n,r);return wi(t,e,a[0],a[1],o.internalFormatHalfFloat,o.textureFormatFloat,o.textureTypeHalfFloat)}function Ri(t,e,n,r,o){var a=Xt(n,r);return wi(t,e,a[0],a[1],t.RGBA,t.RGBA,t.UNSIGNED_BYTE)}function Ii(t,e,n,r,o){var a=$t(n,r);return wi(t,e,a[0],a[1],o.internalFormatPackedFloat,t.RGBA,t.FLOAT)}function ki(t,e,n,r,o){var a=$t(n,r);return wi(t,e,a[0],a[1],o.internalFormatPackedHalfFloat,t.RGBA,o.textureTypeHalfFloat)}function Si(t,e,n,r){return Jt(t,e,(function(){return t.bindBuffer(t.ARRAY_BUFFER,r)})),ge(t,e,n,\"clipSpacePos\",r,3,20,0)&&ge(t,e,n,\"uv\",r,2,20,12)}function Ai(t,e,n,r,o,a,i){var s,u,c;Jt(t,e,(function(){return t.bindTexture(t.TEXTURE_2D,n)})),a instanceof Uint8Array?(s=new Uint8Array(r*o*4),u=t.UNSIGNED_BYTE,c=t.RGBA):(s=new Float32Array(r*o*4),u=t.FLOAT,c=i.internalFormatPackedFloat),s.set(a),Jt(t,e,(function(){return t.texImage2D(t.TEXTURE_2D,0,c,r,o,0,t.RGBA,u,s)})),Jt(t,e,(function(){return t.bindTexture(t.TEXTURE_2D,null)}))}function Di(t,e,n,r){Jt(t,e,(function(){return t.bindTexture(t.TEXTURE_2D,n)})),r.data instanceof Uint8Array?Jt(t,e,(function(){return t.texImage2D(t.TEXTURE_2D,0,t.RGBA,r.width,r.height,0,t.RGBA,t.UNSIGNED_BYTE,r.data)})):Jt(t,e,(function(){return t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,r)})),Jt(t,e,(function(){return t.bindTexture(t.TEXTURE_2D,null)}))}function Ti(t,e,n,r,o){var a=t.createBuffer();Jt(t,e,(function(){return t.bindBuffer(t.PIXEL_PACK_BUFFER,a)}));var i=16*n*r;return Jt(t,e,(function(){return t.bufferData(t.PIXEL_PACK_BUFFER,i,t.STREAM_READ)})),Jt(t,e,(function(){return t.readPixels(0,0,r,n,t.RGBA,t.FLOAT,0)})),Jt(t,e,(function(){return t.bindBuffer(t.PIXEL_PACK_BUFFER,null)})),a}function Ni(t,e,n){var r=t,o=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,e),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,o),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),o}function Fi(t,e,n,r,o){var a=Xt(n,r),i=a[0],s=a[1],u=new Uint8Array(n*r*4);return Jt(t,e,(function(){return t.readPixels(0,0,i,s,o.downloadTextureFormat,t.UNSIGNED_BYTE,u)})),new Float32Array(u.buffer)}function _i(t,e,n,r,o,a,i,s){var u=t,c=new Float32Array(function(t,e){var n=$t(t,e);return n[0]*n[1]*4}(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,e),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,c),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),c}function Oi(t,e,n,r){var o=new Float32Array(n*r*4);return Jt(t,e,(function(){return t.readPixels(0,0,r,n,t.RGBA,t.FLOAT,o)})),o}var Mi=Object.freeze({createVertexShader:yi,createVertexBuffer:xi,createIndexBuffer:bi,createFloat32MatrixTexture:Ci,createFloat16MatrixTexture:Ei,createUnsignedBytesMatrixTexture:Ri,createPackedMatrixTexture:Ii,createFloat16PackedMatrixTexture:ki,bindVertexProgramAttributeStreams:Si,uploadDenseMatrixToTexture:Ai,uploadPixelDataToTexture:Di,createBufferFromOutputTexture:Ti,downloadFloat32MatrixFromBuffer:Ni,downloadByteEncodedFloatMatrixFromOutputTexture:Fi,downloadPackedMatrixFromBuffer:_i,downloadMatrixFromPackedOutputTexture:Oi}),Bi=function(){function t(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];var e=i().getNumber(\"WEBGL_VERSION\");null!=t?(this.gl=t,Kt(e,t)):this.gl=jt(e);var n=\"WEBGL_color_buffer_float\";if(1===i().getNumber(\"WEBGL_VERSION\")){if(this.textureFloatExtension=re(this.gl,this.debug,\"OES_texture_float\"),Pe(this.gl,\"OES_texture_half_float\"))this.textureHalfFloatExtension=re(this.gl,this.debug,\"OES_texture_half_float\");else if(i().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),Pe(this.gl,\"EXT_color_buffer_half_float\"))this.colorBufferHalfFloatExtension=re(this.gl,this.debug,\"EXT_color_buffer_half_float\");else if(i().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\",Pe(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!Pe(this.gl,\"EXT_color_buffer_half_float\"))throw new Error(\"GL context does not support color renderable floats\");this.colorBufferHalfFloatExtension=this.gl.getExtension(\"EXT_color_buffer_half_float\")}this.vertexBuffer=xi(this.gl,this.debug),this.indexBuffer=bi(this.gl,this.debug),this.framebuffer=me(this.gl,this.debug),this.textureConfig=Qt(this.gl,this.textureHalfFloatExtension)}return Object.defineProperty(t.prototype,\"debug\",{get:function(){return i().getBool(\"DEBUG\")},enumerable:!0,configurable:!0}),t.prototype.dispose=function(){var t=this;if(!this.disposed){null!=this.program&&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.\"),null!=this.outputTexture&&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.\");var e=this.gl;Jt(e,this.debug,(function(){return e.finish()})),Jt(e,this.debug,(function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)})),Jt(e,this.debug,(function(){return e.deleteFramebuffer(t.framebuffer)})),Jt(e,this.debug,(function(){return e.bindBuffer(e.ARRAY_BUFFER,null)})),Jt(e,this.debug,(function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)})),Jt(e,this.debug,(function(){return e.deleteBuffer(t.indexBuffer)})),this.disposed=!0}},t.prototype.createFloat32MatrixTexture=function(t,e){return this.throwIfDisposed(),Ci(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.createFloat16MatrixTexture=function(t,e){return this.throwIfDisposed(),Ei(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.createUnsignedBytesMatrixTexture=function(t,e){return this.throwIfDisposed(),Ri(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.uploadPixelDataToTexture=function(t,e){this.throwIfDisposed(),Di(this.gl,this.debug,t,e)},t.prototype.uploadDenseMatrixToTexture=function(t,e,n,r){this.throwIfDisposed(),Ai(this.gl,this.debug,t,e,n,r,this.textureConfig)},t.prototype.createFloat16PackedMatrixTexture=function(t,e){return this.throwIfDisposed(),ki(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.createPackedMatrixTexture=function(t,e){return this.throwIfDisposed(),Ii(this.gl,this.debug,t,e,this.textureConfig)},t.prototype.deleteMatrixTexture=function(t){var e=this;this.throwIfDisposed(),this.outputTexture===t&&(Ee(this.gl,this.debug,this.framebuffer),this.outputTexture=null),Jt(this.gl,this.debug,(function(){return e.gl.deleteTexture(t)}))},t.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,(function(){return Fi(r.gl,r.debug,e,n,r.textureConfig)}))},t.prototype.downloadPackedMatrixFromBuffer=function(t,e,n,r,o,a){return _i(this.gl,t,0,0,0,o,a,this.textureConfig)},t.prototype.downloadFloat32MatrixFromBuffer=function(t,e){return Ni(this.gl,t,e)},t.prototype.createBufferFromTexture=function(t,e,n){this.bindTextureToFrameBuffer(t);var r=Ti(this.gl,this.debug,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},t.prototype.createAndWaitForFence=function(){var t=this.createFence(this.gl);return this.pollFence(t)},t.prototype.createFence=function(t){var e,n,r=this;if(i().getBool(\"WEBGL_FENCE_API_ENABLED\")){var o=t,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=function(){var t=o.clientWaitSync(a,0,0);return t===o.ALREADY_SIGNALED||t===o.CONDITION_SATISFIED},e=a}else i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(e=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(e,i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}):n=function(){return!0};return{query:e,isFencePassed:n}},t.prototype.downloadMatrixFromPackedTexture=function(t,e,n){var r=this;return this.downloadMatrixDriver(t,(function(){return Oi(r.gl,r.debug,e,n)}))},t.prototype.createProgram=function(t){this.throwIfDisposed();var e=this.gl,n=ae(e,this.debug,t),r=yi(e,this.debug),o=ce(e,this.debug);return Jt(e,this.debug,(function(){return e.attachShader(o,r)})),Jt(e,this.debug,(function(){return e.attachShader(o,n)})),le(e,this.debug,o),this.debug&&he(e,this.debug,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=Si(e,this.debug,this.program,this.vertexBuffer)),o},t.prototype.deleteProgram=function(t){var e=this;this.throwIfDisposed(),t===this.program&&(this.program=null),null!=t&&Jt(this.gl,this.debug,(function(){return e.gl.deleteProgram(t)}))},t.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,null!=this.program&&this.debug&&he(this.gl,this.debug,this.program),Jt(this.gl,this.debug,(function(){return e.gl.useProgram(t)}))},t.prototype.getUniformLocation=function(t,e,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?xe(this.gl,this.debug,t,e):be(this.gl,t,e)},t.prototype.getAttributeLocation=function(t,e){var n=this;return this.throwIfDisposed(),Jt(this.gl,this.debug,(function(){return n.gl.getAttribLocation(t,e)}))},t.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},t.prototype.setInputMatrixTexture=function(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),we(this.gl,this.debug,this.program,t,e,n)},t.prototype.setOutputMatrixTexture=function(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)},t.prototype.setOutputPackedMatrixTexture=function(t,e,n){this.throwIfDisposed();var r=$t(e,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(t,o,a)},t.prototype.setOutputMatrixWriteRegion=function(t,e,n,r){this.setOutputMatrixWriteRegionDriver(n,t,r,e)},t.prototype.setOutputPackedMatrixWriteRegion=function(t,e,n,r){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")},t.prototype.debugValidate=function(){null!=this.program&&he(this.gl,this.debug,this.program),Re(this.gl)},t.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.debug&&this.debugValidate(),Jt(t,this.debug,(function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)}))},t.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),Jt(this.gl,this.debug,(function(){return t.gl.finish()}))},t.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=re(this.gl,this.debug,2===i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension},t.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},t.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},t.prototype.beginQuery=function(){if(2===i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),n=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},t.prototype.endQuery=function(){if(2!==i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}else{var e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT)}},t.prototype.waitForQueryAndGetTime=function(t){return n(this,void 0,void 0,(function(){var e=this;return r(this,(function(n){switch(n.label){case 0:return[4,F((function(){return e.disposed||e.isQueryAvailable(t,i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}))];case 1:return n.sent(),[2,this.getQueryTime(t,i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))]}}))}))},t.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},t.prototype.isQueryAvailable=function(t,e){if(0===e)return!0;if(2===e){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},t.prototype.pollFence=function(t){var e=this;return new Promise((function(n){e.addItemToPoll((function(){return t.isFencePassed()}),(function(){return n()}))}))},t.prototype.pollItems=function(){for(var t=function(t){for(var e=0;e<t.length;++e){if(!t[e]())break}return e-1}(this.itemsToPoll.map((function(t){return t.isDoneFn}))),e=0;e<=t;++e){(0,this.itemsToPoll[e].resolveFn)()}this.itemsToPoll=this.itemsToPoll.slice(t+1)},t.prototype.addItemToPoll=function(t,e){var n=this;this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1||F((function(){return n.pollItems(),0===n.itemsToPoll.length}))},t.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),Ce(this.gl,this.debug,t,this.framebuffer),this.debug&&Re(this.gl)},t.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(Ce(this.gl,this.debug,this.outputTexture,this.framebuffer),this.debug&&Re(this.gl)):Ee(this.gl,this.debug,this.framebuffer)},t.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},t.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;Ce(r,this.debug,t,this.framebuffer),this.debug&&Re(r),this.outputTexture=t,Jt(r,this.debug,(function(){return r.viewport(0,0,e,n)})),Jt(r,this.debug,(function(){return r.scissor(0,0,e,n)}))},t.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var o=this;this.throwIfDisposed(),Jt(this.gl,this.debug,(function(){return o.gl.scissor(t,e,n,r)}))},t.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")},t.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error(\"No GPU program is currently set.\")},t}();function Pi(t,e){if(t.length!==e.length)throw Error(\"Binary was compiled with \"+t.length+\" inputs, but was executed with \"+e.length+\" inputs\");t.forEach((function(t,n){var r=t.logicalShape,o=e[n],a=o.shape;if(!S(r,a))throw Error(\"Binary was compiled with different shapes than the current args. Shapes \"+r+\" and \"+a+\" must match\");if(!t.isUniform||!o.isUniform){var i=t.texShape,s=o.isUniform?null:o.texData.texShape;if(!S(i,s))throw Error(\"Binary was compiled with different texture shapes than the current args. Shape \"+i+\" and \"+s+\" must match\")}}))}var Li=function(t,e,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,c=n.dilationWidth,l=n.dilationHeight,h=n.dataFormat,f=s.left,d=s.top,p=o*r,v=ua(),m=\"channelsLast\"===h,g=m?0:1,y=m?1:2,x=\"\",b=0;b<=1;b++)for(var w=0;w<=1;w++)x+=\"\\n blockIndex = rc.y + \"+w+\";\\n pos = rc.x + \"+b+\";\\n\\n if(blockIndex < \"+t[1]+\" && pos < \"+t[0]+\") {\\n offsetY = int(blockIndex / (\"+u+\")) * \"+i+\" - \"+d+\";\\n d0 = offsetY + \"+l+\" * (pos / \"+p+\");\\n\\n if(d0 < \"+e[g]+\" && d0 >= 0) {\\n\\n offsetX = int(mod(float(blockIndex), \"+u+\".) * \"+a+\". - \"+f+\".);\\n d1 = offsetX + \"+c+\" * (int(mod(float(pos), \"+p+\".) / \"+o+\".));\\n\\n if(d1 < \"+e[y]+\" && d1 >= 0) {\\n\\n ch = int(mod(float(pos), \"+o+\".));\\n\\n if (\"+m+\") {\\n innerDims = vec2(d1, ch);\\n result[\"+(2*b+w)+\"] = getChannel(\\n getA(d0, int(innerDims.x),\\n int(innerDims.y)), innerDims);\\n } else {\\n innerDims = vec2(d0, d1);\\n result[\"+(2*b+w)+\"] = getChannel(\\n getA(ch, int(innerDims.x),\\n int(innerDims.y)), innerDims);\\n }\\n }\\n }\\n }\\n \";this.userCode=\"\\n void main() {\\n ivec2 rc = getOutputCoords();\\n\\n vec4 result = vec4(0);\\n\\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\\n vec2 innerDims;\\n\\n \"+x+\"\\n\\n \"+v.output+\" = result;\\n }\\n \"},Wi=function(t,e,n,r,o){this.variableNames=[\"x\"],this.outputShape=[];var a,i=e,s=t[3]-1;this.outputShape=t;var u=\"float(\"+n+\") + float(\"+r+\") * sum\";a=.5===o?\"inversesqrt(\"+u+\")\":1===o?\"1.0/(\"+u+\")\":\"exp(log(\"+u+\") * float(-\"+o+\"));\",this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int r = coords[1];\\n int c = coords[2];\\n int d = coords[3];\\n float x = getX(b, r, c, d);\\n float sum = 0.0;\\n for (int j = -\"+i+\"; j <= \"+i+\"; j++) {\\n int idx = d + j;\\n if (idx >= 0 && idx <= \"+s+\") {\\n float z = getX(b, r, c, idx);\\n sum += z * z;\\n }\\n }\\n float val = x * \"+a+\";\\n setOutput(val);\\n }\\n \"},Ui=function(t,e,n,r,o){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=o,this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int r = coords[1];\\n int c = coords[2];\\n\\n float result = 0.0;\\n for (int d = 0; d < \"+this.depth+\"; ++d) {\\n int depthBegin = int(max(0.0, float(d - \"+e+\")));\\n int depthEnd = int(min(float(\"+this.depth+\"),\\n float(d + \"+e+\" + 1)));\\n\\n const int MIN_DEPTH_BEGIN = 0;\\n const int MAX_DEPTH_END = \"+this.depth+\";\\n\\n float norm = 0.0;\\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\\n if (k < depthBegin){\\n continue;\\n }\\n else if (k >= depthBegin && k < depthEnd) {\\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\\n }\\n else {\\n break;\\n }\\n }\\n\\n norm = float(\"+r+\") * norm + float(\"+n+\");\\n\\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\\n if (k < depthBegin){\\n continue;\\n }\\n else if (k >= depthBegin && k < depthEnd){\\n float dyi = -2.0 * float(\"+r+\")\\n * float(\"+o+\")\\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\\n / norm;\\n if (k == d) {\\n dyi += pow(norm, -1.0 * \"+o+\");\\n }\\n if (k == coords[3]) {\\n dyi *= getDy(b, r, c, d);\\n result += dyi;\\n }\\n }\\n else {\\n break;\\n }\\n }\\n }\\n setOutput(result);\\n }\\n \"},Vi=function(t,e,n,r,o){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;var a,i=e,s=t[3]-1;this.outputShape=t;var u=\"float(\"+n+\") + float(\"+r+\") * sum\";a=.5===o?\"inversesqrt(\"+u+\")\":1===o?\"1.0/(\"+u+\")\":\"exp(log(\"+u+\") * float(-\"+o+\"));\",this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords.x;\\n int r = coords.y;\\n int c = coords.z;\\n int d = coords.w;\\n\\n bool hasNextCol = d < \"+this.outputShape[3]+\";\\n bool hasNextRow = c < \"+this.outputShape[2]+\";\\n\\n vec4 sum = vec4(0.);\\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\\n\\n vec4 xAtOutputCoords = vec4(\\n getChannel(xFragAtOutputCoords, vec2(c, d)),\\n hasNextCol ?\\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\\n hasNextRow ?\\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\\n (hasNextRow && hasNextCol) ?\\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\\n );\\n\\n int firstChannel = d - \"+i+\";\\n vec2 cache = vec2(0.);\\n if(firstChannel >= 0){\\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\\n if(hasNextRow){\\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\\n }\\n }\\n\\n ivec2 depth = ivec2(d, d + 1);\\n for (int j = - \"+i+\"; j <= \"+i+\"; j++) {\\n ivec2 idx = depth + j;\\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(\"+s+\"));\\n\\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\\n\\n if(depthInRange || depthPlusOneInRange){\\n vec4 z = vec4(0.);\\n vec4 xFragAtCurrentDepth;\\n z.xz = cache.xy;\\n if(depthPlusOneInRange && hasNextCol){\\n xFragAtCurrentDepth = idx.y != d ?\\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\\n if(hasNextRow){\\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\\n }\\n }\\n cache.xy = z.yw;\\n sum += z * z;\\n }\\n }\\n vec4 result = xAtOutputCoords * \"+a+\";\\n setOutput(result);\\n }\\n \"},zi=function(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,o=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=o-1-t.padInfo.top,s=a-1-t.padInfo.left,u=o*a-1;this.userCode=\"\\n const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n\\n ivec2 dyRCCorner = coords.yz - pads;\\n int dyRCorner = dyRCCorner.x;\\n int dyCCorner = dyRCCorner.y;\\n\\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n for (int wR = 0; wR < \"+o+\";\\n wR += \"+r+\") {\\n float dyR = float(dyRCorner + wR) / \"+e+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 || fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n for (int wC = 0; wC < \"+a+\"; wC++) {\\n float dyC = float(dyCCorner + wC) / \"+n+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n float dyValue = getDy(b, idyR, idyC, d);\\n int maxPosValue = \"+u+\" - int(getMaxPos(b, idyR, idyC, d));\\n\\n // Get the current value, check it against the value from the\\n // position matrix.\\n int curPosValue = wR * \"+a+\" + wC;\\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\\n\\n dotProd += dyValue * mask;\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Gi=function(t){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=t.inShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.dilationDepth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterDepth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.front,h=u-1-t.padInfo.top,f=c-1-t.padInfo.left,d=s*u*c-1;this.userCode=\"\\n const ivec3 pads = ivec3(\"+l+\", \"+h+\", \"+f+\");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int ch = coords.u;\\n\\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\\n int dyDCorner = dyCorner.x;\\n int dyRCorner = dyCorner.y;\\n int dyCCorner = dyCorner.z;\\n\\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\\n // dx(xD, xR, xC, ch).\\n // ? = to be determined. : = across all values in that axis.\\n float dotProd = 0.0;\\n\\n for (int wD = 0; wD < \"+s+\";\\n wD += \"+o+\") {\\n float dyD = float(dyDCorner + wD) / \"+e+\".0;\\n\\n if (dyD < 0.0 || dyD >= \"+t.outDepth+\".0 || fract(dyD) > 0.0) {\\n continue;\\n }\\n int idyD = int(dyD);\\n\\n for (int wR = 0; wR < \"+u+\";\\n wR += \"+a+\") {\\n float dyR = float(dyRCorner + wR) / \"+n+\".0;\\n\\n if (dyR < 0.0 || dyR >= \"+t.outHeight+\".0 ||\\n fract(dyR) > 0.0) {\\n continue;\\n }\\n int idyR = int(dyR);\\n\\n for (int wC = 0; wC < \"+c+\";\\n wC += \"+i+\") {\\n float dyC = float(dyCCorner + wC) / \"+r+\".0;\\n\\n if (dyC < 0.0 || dyC >= \"+t.outWidth+\".0 ||\\n fract(dyC) > 0.0) {\\n continue;\\n }\\n int idyC = int(dyC);\\n\\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\\n int maxPosValue = \"+d+\" -\\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\\n\\n // Get the current value, check it against the value from the\\n // position matrix.\\n int curPosValue =\\n wD * \"+u+\" * \"+c+\" +\\n wR * \"+c+\" + wC;\\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\\n\\n dotProd += dyValue * mask;\\n }\\n }\\n }\\n setOutput(dotProd);\\n }\\n \"},Hi=function(t,e,n,r,o,a,i){void 0===n&&(n=!1),void 0===r&&(r=!1),void 0===o&&(o=!1),void 0===a&&(a=null),void 0===i&&(i=!1),this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;var s=n?t[1]:t[2],u=Math.ceil(s/2),c=n?\"i * 2, rc.y\":\"rc.y, i * 2\",l=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],d=\"\",p=\"\";a&&(d=i?\"vec4 activation(vec4 a) {\\n vec4 b = getPreluActivationWeightsAtOutCoords();\\n \"+a+\"\\n }\":\"vec4 activation(vec4 x) {\\n \"+a+\"\\n }\",p=\"result = activation(result);\");var v=o?\"result += getBiasAtOutCoords();\":\"\";o&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\"),this.userCode=\"\\n \"+d+\"\\n\\n const float sharedDimension = \"+u+\".0;\\n\\n vec4 dot2x2ARowBCol(ivec3 rc) {\\n vec4 result = vec4(0);\\n for (int i = 0; i < \"+u+\"; i++) {\\n vec4 a = getMatrixA(rc.x, \"+c+\");\\n vec4 b = getMatrixB(rc.x, \"+l+\");\\n\\n // These swizzled products need to be separately added.\\n // See: https://github.com/tensorflow/tfjs/issues/1735\\n result += (\"+h[0]+\" * \"+f[0]+\");\\n result += (\"+h[1]+\" * \"+f[1]+\");\\n }\\n return result;\\n }\\n\\n void main() {\\n ivec3 rc = getOutputCoords();\\n vec4 result = dot2x2ARowBCol(rc);\\n\\n \"+v+\"\\n\\n \"+p+\"\\n\\n setOutput(result);\\n }\\n \"},qi=function(){function t(t,e,n){this.variableNames=[\"probs\"],this.outputShape=[t,n],this.userCode=\"\\n uniform float seed;\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n\\n float r = random(seed);\\n float cdf = 0.0;\\n\\n for (int i = 0; i < \"+(e-1)+\"; i++) {\\n cdf += getProbs(batch, i);\\n\\n if (r < cdf) {\\n setOutput(float(i));\\n return;\\n }\\n }\\n\\n // If no other event happened, last event happened.\\n setOutput(float(\"+(e-1)+\"));\\n }\\n \"}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.seedLoc&&(e.seedLoc=n.getUniformLocation(r,\"seed\")),n.gl.uniform1f(e.seedLoc,t)}},t}(),Ki=function(t,e,n,r){this.variableNames=[\"indices\"],this.outputShape=[t,e],this.userCode=\"\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int index = round(getIndices(coords.x));\\n setOutput(mix(float(\"+r+\"), float(\"+n+\"),\\n float(index == coords.y)));\\n }\\n \"},ji=function(t){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;var e=t.length;if(0===e)this.userCode=\"\\n void main() {\\n setOutput(vec4(getA(), 0., 0., 0.));\\n }\\n \";else{var n=sa(\"rc\",e),r=wa(e),o=function(t,e,n){if(1===t)return\"rc > \"+e[0];for(var r=\"\",o=t-2;o<t;o++)r+=n[o]+\" >= \"+e[o],o<t-1&&(r+=\"||\");return r}(e,t,n),a=function(t,e,n,r){if(1===t)return\"\";var o=r.slice(-2);return\"\\n int r = \"+o[0]+\";\\n int c = \"+o[1]+\";\\n int rp1 = r + 1;\\n int cp1 = c + 1;\\n\\n bool cEdge = cp1 >= \"+e+\";\\n bool rEdge = rp1 >= \"+n+\";\\n \"}(e,t[t.length-1],t[t.length-2],n),i=function(t,e){var n=t.length,r=function(t,e){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?\"r\":\"rp1\")+\", \"+(0===o?\"c\":\"cp1\"),i=2;i<t;i++)a=e[e.length-1-i]+\",\"+a;n.push(a)}return n}(n,e);return 1===n?\"getA(rc),\\n rc + 1 >= \"+t[0]+\" ? 0. : getA(rc + 1),\\n 0, 0\":\"getA(\"+r[0]+\"),\\n cEdge ? 0. : getA(\"+r[1]+\"),\\n rEdge ? 0. : getA(\"+r[2]+\"),\\n rEdge || cEdge ? 0. : getA(\"+r[3]+\")\"}(t,n);this.userCode=\"\\n void main() {\\n \"+r+\" rc = getOutputCoords();\\n\\n if(\"+o+\") {\\n setOutput(vec4(0));\\n } else {\\n \"+a+\"\\n\\n setOutput(vec4(\"+i+\"));\\n }\\n }\\n \"}};var Xi=function(t,e,n){this.variableNames=[\"x\"],this.outputShape=e.map((function(e,n){return e[0]+t[n]+e[1]}));var r=t.length,o=wa(r),a=e.map((function(t){return t[0]})).join(\",\"),i=e.map((function(e,n){return e[0]+t[n]})).join(\",\"),s=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,r);this.userCode=1!==r?\"\\n \"+o+\" start = \"+o+\"(\"+a+\");\\n \"+o+\" end = \"+o+\"(\"+i+\");\\n\\n void main() {\\n \"+o+\" outC = getOutputCoords();\\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\\n setOutput(float(\"+n+\"));\\n } else {\\n \"+o+\" coords = outC - start;\\n setOutput(getX(\"+s+\"));\\n }\\n }\\n \":\"\\n int start = \"+a+\";\\n int end = \"+i+\";\\n\\n void main() {\\n int outC = getOutputCoords();\\n if (outC < start || outC >= end) {\\n setOutput(float(\"+n+\"));\\n } else {\\n setOutput(getX(outC - start));\\n }\\n }\\n \"},Yi=function(t,e,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((function(e,n){return e[0]+t[n]+e[1]}));for(var r=t.length,o=wa(r),a=e.map((function(t){return t[0]})).join(\",\"),i=e.map((function(e,n){return e[0]+t[n]})).join(\",\"),s=sa(\"rc\",r),u=sa(\"source\",r),c=s[r-1]+\" < \"+this.outputShape[r-1],l=1===r?\"source\":\"vec2(\"+u.slice(-2).join()+\")\",h=[o+\" rc = outputLoc;\",s[r-1]+\" += 1;\\n if(\"+c+\") {\\n \",1===r?\"\":\"}\\n rc = outputLoc;\\n \"+s[r-2]+\" += 1;\\n if(\"+s[r-2]+\" < \"+this.outputShape[r-2]+\") {\",1===r?\"\":\" \"+s[r-1]+\" += 1;\\n if(\"+c+\") {\"],f=1===r?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",d=\"\",p=0,v=1===r?2:4;p<v;p++)d+=\"\\n \"+h[p]+\"\\n if (\"+f+\") {\\n result[\"+p+\"] = float(\"+n+\");\\n } else {\\n \"+o+\" source = rc - start;\\n result[\"+p+\"] = getChannel(getX(\"+u.join()+\"), \"+l+\");\\n }\\n \";d+=1===r?\"} \":\"}}\",this.userCode=\"\\n const \"+o+\" start = \"+o+\"(\"+a+\");\\n const \"+o+\" end = \"+o+\"(\"+i+\");\\n\\n void main() {\\n \"+o+\" outputLoc = getOutputCoords();\\n vec4 result = vec4(0.);\\n \"+d+\"\\n setOutput(result);\\n }\\n \"},$i=function(t,e,n){if(this.variableNames=[\"x\"],\"avg\"===e&&n)throw new Error(\"Cannot compute positions for average pool.\");var r=t.filterWidth,o=t.strideHeight,a=t.strideWidth,i=t.dilationHeight,s=t.dilationWidth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=t.padInfo.top,h=t.padInfo.left;this.outputShape=t.outShape;var f=\"avg\"===e,d=\"0.0\";if(f||(d=\"-1.0 / 1e-20\"),n)this.userCode=\"\\n const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n const ivec2 pads = ivec2(\"+l+\", \"+h+\");\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d = coords[3];\\n\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // max/min x(?, ?, d) to get y(yR, yC, d).\\n // ? = to be determined\\n float minMaxValue = 0.0;\\n float minMaxValueFound = 0.0;\\n int minMaxPosition = 0;\\n float avgValue = 0.0;\\n\\n for (int wR = 0; wR < \"+u+\";\\n wR += \"+i+\") {\\n int xR = xRCorner + wR;\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+c+\";\\n wC += \"+s+\") {\\n int xC = xCCorner + wC;\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n float value = getX(batch, xR, xC, d);\\n\\n // If a min / max value has already been found, use it. If not,\\n // use the current value.\\n float currMinMaxValue = mix(\\n value, minMaxValue, minMaxValueFound);\\n if (value >= currMinMaxValue) {\\n minMaxValue = value;\\n minMaxValueFound = 1.0;\\n minMaxPosition = wR * \"+c+\" + wC;\\n }\\n }\\n }\\n setOutput(float(minMaxPosition));\\n }\\n \";else{var p=e+\"(\"+e+\"(\"+e+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"avg\"===e&&(p=\"avgValue / count\");var v=4*Math.floor(r/4),m=r%4,g=\"\\n if (\"+f+\") {\\n avgValue += dot(values, ones);\\n } else {\\n minMaxValue = max(values, minMaxValue);\\n }\\n \";this.userCode=\"\\n const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n const ivec2 pads = ivec2(\"+l+\", \"+h+\");\\n const float initializationValue = \"+d+\";\\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n float count = 0.0;\\n\\n float getValue(int batch, int xR, int xC, int d) {\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n return initializationValue;\\n }\\n count += 1.0;\\n return getX(batch, xR, xC, d);\\n }\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int batch = coords[0];\\n int d = coords[3];\\n\\n ivec2 xRCCorner = coords.yz * strides - pads;\\n int xRCorner = xRCCorner.x;\\n int xCCorner = xRCCorner.y;\\n\\n // max/min x(?, ?, d) to get y(yR, yC, d).\\n // ? = to be determined\\n vec4 minMaxValue = vec4(\"+d+\");\\n float avgValue = 0.0;\\n count = 0.0;\\n\\n for (int wR = 0; wR < \"+u+\";\\n wR += \"+i+\") {\\n int xR = xRCorner + wR;\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+v+\"; wC += 4) {\\n int xC = xCCorner + wC * \"+s+\";\\n\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n getValue(batch, xR, xC + \"+s+\", d),\\n getValue(batch, xR, xC + 2 * \"+s+\", d),\\n getValue(batch, xR, xC + 3 * \"+s+\", d)\\n );\\n\\n \"+g+\"\\n }\\n\\n int xC = xCCorner + \"+v+\";\\n if (\"+(1===m)+\") {\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n \"+g+\"\\n } else if (\"+(2===m)+\") {\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n getValue(batch, xR, xC + \"+s+\", d),\\n initializationValue,\\n initializationValue\\n );\\n\\n \"+g+\"\\n } else if (\"+(3===m)+\") {\\n vec4 values = vec4(\\n getValue(batch, xR, xC, d),\\n getValue(batch, xR, xC + \"+s+\", d),\\n getValue(batch, xR, xC + 2 * \"+s+\", d),\\n initializationValue\\n );\\n\\n \"+g+\"\\n }\\n }\\n setOutput(\"+p+\");\\n }\\n \"}},Qi=function(t,e,n){if(this.variableNames=[\"x\"],\"avg\"===e&&n)throw new Error(\"Cannot compute positions for average pool.\");var r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.effectiveFilterDepth,h=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,v=t.padInfo.left;this.outputShape=t.outShape;var m=\"avg\"===e,g=\"0.0\";if(m||(g=\"-1.0 / 1e-20\"),n)this.userCode=\"\\n const ivec3 strides =\\n ivec3(\"+o+\", \"+a+\", \"+i+\");\\n const ivec3 pads = ivec3(\"+d+\", \"+p+\", \"+v+\");\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int ch = coords.u;\\n\\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\\n int xDCorner = xCorner.x;\\n int xRCorner = xCorner.y;\\n int xCCorner = xCorner.z;\\n\\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\\n // ? = to be determined\\n float minMaxValue = 0.0;\\n float minMaxValueFound = 0.0;\\n int minMaxPosition = 0;\\n\\n for (int wD = 0; wD < \"+l+\";\\n wD += \"+s+\") {\\n int xD = xDCorner + wD;\\n\\n if (xD < 0 || xD >= \"+t.inDepth+\") {\\n continue;\\n }\\n\\n for (int wR = 0; wR < \"+h+\";\\n wR += \"+u+\") {\\n int xR = xRCorner + wR;\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+f+\";\\n wC += \"+c+\") {\\n int xC = xCCorner + wC;\\n\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n continue;\\n }\\n\\n float value = getX(batch, xD, xR, xC, ch);\\n\\n // If a min / max value has already been found, use it. If not,\\n // use the current value.\\n float currMinMaxValue = mix(\\n value, minMaxValue, minMaxValueFound);\\n if (value >= currMinMaxValue) {\\n minMaxValue = value;\\n minMaxValueFound = 1.0;\\n minMaxPosition =\\n wD * \"+h+\" * \"+f+\" +\\n wR * \"+f+\" + wC;;\\n }\\n }\\n }\\n }\\n setOutput(float(minMaxPosition));\\n }\\n \";else{var y=e+\"(\"+e+\"(\"+e+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"avg\"===e&&(y=\"avgValue / count\");var x=4*Math.floor(r/4),b=r%4,w=\"\\n if (\"+m+\") {\\n avgValue += dot(values, ones);\\n } else {\\n minMaxValue = max(values, minMaxValue);\\n }\\n \";this.userCode=\"\\n const ivec3 strides =\\n ivec3(\"+o+\", \"+a+\", \"+i+\");\\n const ivec3 pads = ivec3(\"+d+\", \"+p+\", \"+v+\");\\n const float initializationValue = \"+g+\";\\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n float count = 0.0;\\n\\n float getValue(int batch, int xD, int xR, int xC, int ch) {\\n if (xC < 0 || xC >= \"+t.inWidth+\") {\\n return initializationValue;\\n }\\n count += 1.0;\\n return getX(batch, xD, xR, xC, ch);\\n }\\n\\n void main() {\\n ivec5 coords = getOutputCoords();\\n int batch = coords.x;\\n int ch = coords.u;\\n\\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\\n int xDCorner = xCorner.x;\\n int xRCorner = xCorner.y;\\n int xCCorner = xCorner.z;\\n\\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\\n // ? = to be determined\\n vec4 minMaxValue = vec4(\"+g+\");\\n float avgValue = 0.0;\\n count = 0.0;\\n\\n for (int wD = 0; wD < \"+l+\";\\n wD += \"+s+\") {\\n int xD = xDCorner + wD;\\n\\n if (xD < 0 || xD >= \"+t.inDepth+\") {\\n continue;\\n }\\n\\n for (int wR = 0; wR < \"+h+\";\\n wR += \"+u+\") {\\n int xR = xRCorner + wR;\\n\\n if (xR < 0 || xR >= \"+t.inHeight+\") {\\n continue;\\n }\\n\\n for (int wC = 0; wC < \"+x+\"; wC += 4) {\\n int xC = xCCorner + wC * \"+c+\";\\n\\n vec4 values = vec4(\\n getValue(batch, xD, xR, xC, ch),\\n getValue(batch, xD, xR, xC + \"+c+\", ch),\\n getValue(batch, xD, xR, xC + 2 * \"+c+\", ch),\\n getValue(batch, xD, xR, xC + 3 * \"+c+\", ch)\\n );\\n\\n \"+w+\"\\n }\\n\\n int xC = xCCorner + \"+x+\";\\n if (\"+(1===b)+\") {\\n vec4 values = vec4(\\n getValue(batch, xD, xR, xC, ch),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n \"+w+\"\\n } else if (\"+(2===b)+\") {\\n vec4 values = vec4(\\n getValue(batch, xD, xR, xC, ch),\\n getValue(batch, xD, xR, xC + \"+c+\", ch),\\n initializationValue,\\n initializationValue\\n );\\n\\n \"+w+\"\\n } else if (\"+(3===b)+\") {\\n vec4 values = vec4(\\n getValue(batch, xD, xR, xC, ch),\\n getValue(batch, xD, xR, xC + \"+c+\", ch),\\n getValue(batch, xD, xR, xC + 2 * \"+c+\", ch),\\n initializationValue\\n );\\n\\n \"+w+\"\\n }\\n }\\n setOutput(\"+y+\");\\n }\\n }\\n \"}},Ji=function(t,e){this.variableNames=[\"x\"];var n=t.windowSize,r=t.batchSize,o=t.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i=\"0.0\",s=\"\";\"prod\"===e?i=\"1.0\":\"min\"===e?(i=\"1.0 / 1e-20\",s=\"min\"):\"max\"===e&&(i=\"-1.0 / 1e-20\",s=\"max\");var u=e+\"(\"+e+\"(\"+e+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"sum\"===e?u=\"sumValue\":\"prod\"===e?u=\"prodValue\":\"all\"===e?u=\"allValue\":\"any\"===e&&(u=\"anyValue\");var c=4*Math.floor(n/4),l=n%4,h=\"\\n if (\"+(\"sum\"===e)+\") {\\n sumValue += dot(values, ones);\\n } else if (\"+(\"prod\"===e)+\") {\\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\\n prodValue *= tmp[0] * tmp[1];\\n } else {\\n minMaxValue = \"+s+\"(values, minMaxValue);\\n }\\n \",f=\"vec4\";\"all\"===e?(i=\"1.0\",h=\"\\n bool reducedAllValue = all(values);\\n float floatedReducedAllValue = float(reducedAllValue);\\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\\n \",f=\"bvec4\"):\"any\"===e&&(i=\"0.0\",h=\"\\n bool reducedAnyValue = any(values);\\n float floatedReducedAnyValue = float(reducedAnyValue);\\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\\n \",f=\"bvec4\");var d=\"\";o%n>0&&(d=\"\\n if (inIdx < 0 || inIdx >= \"+o+\") {\\n return initializationValue;\\n }\\n \"),this.userCode=\"\\n const float initializationValue = \"+i+\";\\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n float getValue(int batch, int inIdx) {\\n \"+d+\"\\n return getX(batch, inIdx);\\n }\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n int outIdx = coords[1];\\n int inOffset = outIdx * \"+n+\";\\n\\n vec4 minMaxValue = vec4(\"+i+\");\\n float prodValue = 1.0;\\n float sumValue = 0.0;\\n float allValue = 1.0;\\n float anyValue = 0.0;\\n\\n for (int i = 0; i < \"+c+\"; i += 4) {\\n int inIdx = inOffset + i;\\n \"+f+\" values = \"+f+\"(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n getValue(batch, inIdx + 3)\\n );\\n\\n \"+h+\"\\n }\\n\\n int inIdx = inOffset + \"+c+\";\\n if (\"+(1===l)+\") {\\n \"+f+\" values = \"+f+\"(\\n getValue(batch, inIdx),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n \"+h+\"\\n } else if (\"+(2===l)+\") {\\n \"+f+\" values = \"+f+\"(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n initializationValue,\\n initializationValue\\n );\\n\\n \"+h+\"\\n } else if (\"+(3===l)+\") {\\n \"+f+\" values = \"+f+\"(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n initializationValue\\n );\\n\\n \"+h+\"\\n }\\n setOutput(\"+u+\");\\n }\\n \"},Zi=function(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;for(var n=\"\",r=0;r<4;r++){var o=\"thisRC = rc;\";r%2==1&&(o+=\"thisRC.z += 1;\"),r>1&&(o+=\"thisRC.y += 1;\"),n+=\"\\n \"+o+\"\\n \"+(r>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\")+\"\\n int flatIndex = getFlatIndex(thisRC);\\n\\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\\n\\n result[\"+r+\"] =\\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\\n \"+(r>0?\"}\":\"\")+\"\\n \"}this.userCode=\"\\n \\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\\n \"+ca([\"r\",\"c\",\"d\"],e)+\"\\n return ivec3(r, c, d);\\n }\\n \\n \"+la(t)+\"\\n\\n void main() {\\n ivec3 rc = getOutputCoords();\\n\\n vec4 result = vec4(0.);\\n\\n ivec3 thisRC;\\n int rows = \"+t[1]+\";\\n int cols = \"+t[2]+\";\\n\\n \"+n+\"\\n\\n setOutput(result);\\n }\\n \"};var ts=function(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],a=r[2],i=t.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=c[0]/l[0],f=c[1]/l[1],d=1/h,p=1/f,v=2*Math.ceil(d)+2,m=2*Math.ceil(p)+2;this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n int r = coords[1];\\n int c = coords[2];\\n\\n float accumulator = 0.0;\\n\\n const float heightScale = float(\"+h+\");\\n const float widthScale = float(\"+f+\");\\n\\n const float invHeightScale = float(\"+d+\");\\n const float invWidthScale = float(\"+p+\");\\n\\n const int winHeight = int(\"+v+\");\\n const int winWidth = int(\"+m+\");\\n\\n // Compute bounds for where in dy we will look\\n float startRLerp = floor(float(r) * invHeightScale);\\n int startDyR = int(startRLerp - float(winHeight / 2));\\n\\n float startCLerp = floor(float(c) * invWidthScale);\\n int startDyC = int(startCLerp - float(winWidth / 2));\\n\\n // Loop over dy\\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n int dyR = dyROffset + startDyR;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyR < 0 || dyR >= \"+s+\") {\\n continue;\\n }\\n\\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n int dyC = dyCOffset + startDyC;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyC < 0 || dyC >= \"+u+\") {\\n continue;\\n }\\n\\n float dxR = float(dyR) * heightScale;\\n int topDxRIndex = int(floor(dxR));\\n int bottomDxRIndex = int(min(ceil(dxR), \"+(o-1)+\".0));\\n float dxRLerp = dxR - float(topDxRIndex);\\n float inverseDxRLerp = 1.0 - dxRLerp;\\n\\n float dxC = float(dyC) * widthScale;\\n int leftDxCIndex = int(floor(dxC));\\n int rightDxCIndex = int(min(ceil(dxC), \"+(a-1)+\".0));\\n float dxCLerp = dxC - float(leftDxCIndex);\\n float inverseDxCLerp = 1.0 - dxCLerp;\\n\\n if (r == topDxRIndex && c == leftDxCIndex) {\\n // topLeft\\n accumulator +=\\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\\n }\\n\\n if (r == topDxRIndex && c == rightDxCIndex) {\\n // topRight\\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\\n }\\n\\n if (r == bottomDxRIndex && c == leftDxCIndex) {\\n // bottomLeft\\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\\n }\\n\\n if (r == bottomDxRIndex && c == rightDxCIndex) {\\n // bottomRight\\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\\n }\\n }\\n }\\n // End loop over dy\\n\\n setOutput(accumulator);\\n }\\n \"},es=function(t,e,n,r){this.variableNames=[\"A\"],this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],c=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode=\"\\n const vec2 effectiveInputOverOutputRatioRC = vec2(\\n \"+u[0]/c[0]+\",\\n \"+u[1]/c[1]+\");\\n const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".0);\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n ivec2 yRC = coords.yz;\\n\\n // Fractional source index.\\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n // Compute the four integer indices.\\n ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n ivec2 sourceCeilRC = ivec2(\\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\\n\\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n float top = topLeft + (topRight - topLeft) * fracRC.y;\\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n float newValue = top + (bottom - top) * fracRC.x;\\n\\n setOutput(newValue);\\n }\\n \"},ns=function(t,e,n,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],c=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode=\"\\n const vec3 effectiveInputOverOutputRatioRC = vec3(\\n \"+u[0]/c[0]+\",\\n \"+u[1]/c[1]+\",\\n \"+u[1]/c[1]+\");\\n const vec3 inputShapeRC = vec3(\"+a+\".0, \"+i+\".0,\\n \"+i+\".0);\\n\\n float getAValue(int b, int r, int c, int d) {\\n return getChannel(getA(b, r, c, d), vec2(c, d));\\n }\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n // Calculate values for next column in yRC.z.\\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\\n\\n // Fractional source index.\\n vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;\\n\\n // Compute the four integer indices.\\n ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);\\n ivec3 sourceCeilRC = ivec3(\\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\\n\\n // Should we calculate next column and row elements in 2x2 packed cell.\\n bool hasNextCol = d < \"+(s-1)+\";\\n bool hasNextRow = coords.z < \"+(n-1)+\";\\n\\n // In parallel, construct four corners for all four components in\\n // packed 2x2 cell.\\n vec4 topLeft = vec4(\\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\\n : 0.0,\\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\\n : 0.0,\\n (hasNextRow && hasNextCol) ?\\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\\n\\n vec4 bottomLeft = vec4(\\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\\n : 0.0,\\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\\n : 0.0,\\n (hasNextRow && hasNextCol) ?\\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\\n\\n vec4 topRight = vec4(\\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\\n : 0.0,\\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\\n : 0.0,\\n (hasNextRow && hasNextCol) ?\\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\\n\\n vec4 bottomRight = vec4(\\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\\n : 0.0,\\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\\n : 0.0,\\n (hasNextRow && hasNextCol) ?\\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\\n\\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\\n\\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\\n vec4 newValue = mix(top, bottom, fracRC.x);\\n\\n setOutput(newValue);\\n }\\n \"},rs=function(t,e,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],a=r[2],i=t.shape,s=i[1],u=i[2],c=[n&&s>1?o-1:o,n&&u>1?a-1:a],l=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=c[0]/l[0],f=c[1]/l[1],d=1/h,p=1/f,v=2*Math.ceil(d)+2,m=2*Math.ceil(p)+2;this.userCode=\"\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n int r = coords[1];\\n int c = coords[2];\\n\\n float accumulator = 0.0;\\n\\n const float heightScale = float(\"+h+\");\\n const float widthScale = float(\"+f+\");\\n\\n const float invHeightScale = float(\"+d+\");\\n const float invWidthScale = float(\"+p+\");\\n\\n const int winHeight = int(\"+v+\");\\n const int winWidth = int(\"+m+\");\\n\\n // Compute bounds for where in dy we will look\\n float startRLerp = floor(float(r) * invHeightScale);\\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\\n\\n float startCLerp = floor(float(c) * invWidthScale);\\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\\n\\n // Loop over dy\\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n int dyR = dyROffset + startDyR;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyR < 0 || dyR >= \"+s+\") {\\n continue;\\n }\\n\\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n int dyC = dyCOffset + startDyC;\\n\\n // Guard against the window exceeding the bounds of dy\\n if (dyC < 0 || dyC >= \"+u+\") {\\n continue;\\n }\\n\\n float sourceFracRow =\\n float(\"+c[0]+\") *\\n (float(dyR) / float(\"+l[0]+\"));\\n\\n float sourceFracCol =\\n float(\"+c[1]+\") *\\n (float(dyC) / float(\"+l[1]+\"));\\n\\n int sourceNearestRow = int(min(\\n float(int(\"+o+\") - 1),\\n \"+n+\" ? float(round(sourceFracRow)) :\\n float(floor(sourceFracRow))));\\n\\n int sourceNearestCol = int(min(\\n float(int(\"+a+\") - 1),\\n \"+n+\" ? float(round(sourceFracCol)) :\\n float(floor(sourceFracCol))));\\n\\n if (r == sourceNearestRow && c == sourceNearestCol) {\\n accumulator += getDy(b, dyR, dyC, d);\\n }\\n }\\n }\\n // End loop over dy\\n\\n setOutput(accumulator);\\n }\\n \"},os=function(t,e,n,r){this.variableNames=[\"A\"],this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],c=[r&&e>1?e-1:e,r&&n>1?n-1:n],l=r?\"0.5\":\"0.0\";this.userCode=\"\\n const vec2 effectiveInputOverOutputRatioRC = vec2(\\n \"+u[0]/c[0]+\",\\n \"+u[1]/c[1]+\");\\n const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".0);\\n\\n void main() {\\n ivec4 coords = getOutputCoords();\\n int b = coords[0];\\n int d = coords[3];\\n ivec2 yRC = coords.yz;\\n\\n // Fractional source index.\\n vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n // Compute the coordinators of nearest neighbor point.\\n ivec2 sourceNearestRC = ivec2(\\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + \"+l+\")));\\n\\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n\\n setOutput(newValue);\\n }\\n \"},as=function(t,e){this.variableNames=[\"x\"];var n=t.length;if(n>4)throw new Error(\"WebGL backend: Reverse of rank-\"+n+\" tensor is not yet supported\");if(this.outputShape=t,1!==n){var r=t.map((function(n,r){return function(n){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+\" - coords[\"+n+\"] - 1\":\"coords[\"+n+\"]\"}(r)})).join(\",\"),o=wa(n);this.userCode=\"\\n void main() {\\n \"+o+\" coords = getOutputCoords();\\n setOutput(getX(\"+r+\"));\\n }\\n \"}else this.userCode=\"\\n void main() {\\n int coord = getOutputCoords();\\n setOutput(getX(\"+t[0]+\" - coord - 1));\\n }\\n \"},is=function(t,e){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;var n=t.length;if(n>4)throw new Error(\"WebGL backend: Reverse of rank-\"+n+\" tensor is not yet supported\");this.outputShape=t;var r=sa(\"rc\",n),o=r[n-1]+\" + 1 < \"+this.outputShape[n-1],a=r[n-2]+\" + 1 < \"+this.outputShape[n-2],i=wa(n);function s(n){var r=t.map((function(r,o){return function(n,r){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+\" - \"+r[n]+\" - 1\":\"\"+r[n]}(o,n)}));return\"getChannel(getX(\"+r.join(\",\")+\"), vec2(\"+r.slice(-2).join(\",\")+\"))\"}this.userCode=1===n?\"\\n void main(){\\n int rc = getOutputCoords();\\n vec4 result = vec4(0.);\\n result.r = getChannel(getX(\"+t[0]+\" - rc - 1),\\n \"+t[0]+\" - rc - 1);\\n if(\"+o+\"){\\n result.g = getChannel(getX(\"+t[0]+\" - (rc + 1) - 1),\\n \"+t[0]+\" - (rc + 1) - 1);\\n }\\n setOutput(result);\\n }\\n \":\"\\n void main() {\\n \"+i+\" rc = getOutputCoords();\\n vec4 result = vec4(0.);\\n result.r = \"+function(t){return s(t)}(r.slice())+\";\\n if(\"+o+\"){\\n result.g = \"+function(t){return t[n-1]=\"(\"+t[n-1]+\" + 1)\",s(t)}(r.slice())+\";\\n }\\n if(\"+a+\") {\\n result.b = \"+function(t){return t[n-2]=\"(\"+t[n-2]+\" + 1)\",s(t)}(r.slice())+\";\\n if(\"+o+\") {\\n result.a = \"+function(t){return t[n-1]=\"(\"+t[n-1]+\" + 1)\",t[n-2]=\"(\"+t[n-2]+\" + 1)\",s(t)}(r.slice())+\";\\n }\\n }\\n setOutput(result);\\n }\\n \"},ss=function(t,e,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;var s=wa(o.length),u=wa(a.length),c=\"\";1===n?c=\"i\":2===n&&(c=\"i, j\");var l=\"getIndices(\"+c+\")\",h=\"\";1===r?h=\"i\":2===r&&(h=\"i, coords[1]\");var f=\"getUpdates(\"+h+\")\",d=e>1?\"strides[j]\":\"strides\";this.userCode=\"\\n \"+s+\" strides = \"+s+\"(\"+o+\");\\n\\n void main() {\\n \"+u+\" coords = getOutputCoords();\\n float sum = 0.0;\\n bool found = false;\\n for (int i = 0; i < \"+t+\"; i++) {\\n int flattenedIndex = 0;\\n for (int j = 0; j < \"+e+\"; j++) {\\n int index = round(\"+l+\");\\n flattenedIndex += index * \"+d+\";\\n }\\n if (flattenedIndex == coords[0]) {\\n sum += \"+f+\";\\n found = true;\\n }\\n }\\n setOutput(mix(getDefaultValue(), sum, float(found)));\\n }\\n \"},us=function(t,e){this.variableNames=[\"x\",\"segmentIds\"];var n=t.windowSize,r=t.batchSize,o=t.inSize,a=t.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,c=\"\\n sumValue += dot(values, segFilter);\\n \",l=\"\";o%n>0&&(l=\"\\n if (inIdx < 0 || inIdx >= \"+o+\") {\\n return initializationValue;\\n }\\n \");var h=\"\";o%n>0&&(h=\"\\n if (inIdx < 0 || inIdx >= \"+o+\") {\\n return -1.0;\\n }\\n \"),this.userCode=\"\\n const float initializationValue = 0.0;\\n\\n float getValue(int batch, int inIdx) {\\n \"+l+\"\\n return getX(batch, inIdx);\\n }\\n\\n float getSegmentIdAtIndex(int inIdx) {\\n \"+h+\"\\n return getSegmentIds(inIdx);\\n }\\n\\n void main() {\\n ivec2 coords = getOutputCoords();\\n int batch = coords[0];\\n int outIdx = coords[1];\\n int inOffset = int(floor(float(outIdx) / float(\\n \"+a+\")) * float(\"+n+\"));\\n int currentSeg = int(mod(float(outIdx), float(\"+a+\")));\\n\\n float sumValue = 0.0;\\n\\n for (int i = 0; i < \"+s+\"; i += 4) {\\n int inIdx = inOffset + i;\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n getValue(batch, inIdx + 3)\\n );\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\\n );\\n\\n \"+c+\"\\n }\\n\\n int inIdx = inOffset + \"+s+\";\\n if (\"+(1===u)+\") {\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n initializationValue,\\n initializationValue,\\n initializationValue\\n );\\n\\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n 0,\\n 0,\\n 0\\n );\\n\\n \"+c+\"\\n } else if (\"+(2===u)+\") {\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n initializationValue,\\n initializationValue\\n );\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n 0,\\n 0\\n );\\n\\n \"+c+\"\\n } else if (\"+(3===u)+\") {\\n vec4 values = vec4(\\n getValue(batch, inIdx),\\n getValue(batch, inIdx + 1),\\n getValue(batch, inIdx + 2),\\n initializationValue\\n );\\n\\n vec4 segFilter = vec4(\\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n 0\\n );\\n\\n \"+c+\"\\n }\\n setOutput(sumValue);\\n }\\n \"},cs=function(t,e,n){var r,o;if(this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=e,n>4)throw Error(\"Where for rank \"+n+\" is not yet supported\");if(1===n)o=\"resRC\",r=\"resRC\";else{for(var a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],i=[],s=[],u=0;u<e.length;u++)s.push(\"\"+a[u]),u<t&&i.push(\"\"+a[u]);r=i.join(),o=s.join()}var c=wa(n);this.userCode=\"\\n void main() {\\n \"+c+\" resRC = getOutputCoords();\\n float cVal = getC(\"+r+\");\\n if (cVal >= 1.0) {\\n setOutput(getA(\"+o+\"));\\n } else {\\n setOutput(getB(\"+o+\"));\\n }\\n }\\n \"},ls=function(){function t(t){this.variableNames=[\"source\"],this.outputShape=t,this.rank=t.length;var e,n=wa(this.rank),r=\"uniform int start[\"+this.rank+\"];\",o=function(t){if(1===t)return\"sourceLoc\";if(t<=6)return hs.slice(0,t).map((function(t){return\"sourceLoc.\"+t})).join(\",\");throw Error(\"Slicing for rank \"+t+\" is not yet supported\")}(this.rank);e=\"\\n \"+n+\" sourceLoc;\\n \"+n+\" coords = getOutputCoords();\\n \"+t.map((function(t,e){return\"sourceLoc.\"+hs[e]+\" = start[\"+e+\"] + coords.\"+hs[e]+\";\"})).join(\"\\n\")+\"\\n \",this.userCode=\"\\n \"+r+\"\\n void main() {\\n \"+e+\"\\n setOutput(getSource(\"+o+\"));\\n }\\n \"}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error(\"The rank (\"+this.rank+\") of the program must match the length of start (\"+t.length+\")\");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,\"start\"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t)}},t}(),hs=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];var fs=function(){function t(t){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;var e=wa(this.rank),n=sa(\"coords\",this.rank),r=sa(\"sourceLoc\",this.rank),o=1===this.rank?\"sourceLoc\":\"vec2(\"+r.slice(-2).join()+\")\",a=\"getChannel(getSource(\"+r.join()+\"), \"+o+\")\",i=\"\\n result.x = \"+a+\";\\n if (++\"+n[this.rank-1]+\" < \"+t[this.rank-1]+\") {\\n ++\"+r[this.rank-1]+\";\\n result.y = \"+a+\";\\n --\"+r[this.rank-1]+\";\\n }\\n \",s=1===this.rank?\"\":\"\\n --\"+n[this.rank-1]+\";\\n if (++\"+n[this.rank-2]+\" < \"+t[this.rank-2]+\") {\\n ++\"+r[this.rank-2]+\";\\n result.z = \"+a+\";\\n if (++\"+n[this.rank-1]+\" < \"+t[this.rank-1]+\") {\\n ++\"+r[this.rank-1]+\";\\n result.w = \"+a+\";\\n }\\n }\\n \",u=this.rank<=4?\"sourceLoc = coords +\\n \"+e+\"(\"+t.map((function(t,e){return\"start[\"+e+\"]\"})).join()+\");\":t.map((function(t,e){return r[e]+\" = \"+n[e]+\" + start[\"+e+\"];\"})).join(\"\\n\");this.userCode=\"\\n uniform int start[\"+this.rank+\"];\\n void main() {\\n \"+e+\" coords = getOutputCoords();\\n \"+e+\" sourceLoc;\\n \"+u+\"\\n vec4 result = vec4(0.);\\n \"+i+\"\\n \"+s+\"\\n setOutput(result);\\n }\\n \"}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error(\"The rank (\"+this.rank+\") of the program must match the length of start (\"+t.length+\")\");return function(n,r){null==e.startLoc&&(e.startLoc=n.getUniformLocationNoThrow(r,\"start\"),null==e.startLoc)||n.gl.uniform1iv(e.startLoc,t)}},t}(),ds=function(t,e,n){this.variableNames=[\"x\"],this.outputShape=n;var r=n.length,o=wa(n.length),a=wa(n.length),i=\"\";if(1===r)i=\"coords * strides + begin\";else{var s=0;i=n.map((function(t,e){return s++,1===n.length?\"coords * strides[\"+e+\"] + begin[\"+e+\"]\":\"coords[\"+(s-1)+\"] * strides[\"+e+\"] + begin[\"+e+\"]\"})).join(\",\")}this.userCode=\"\\n \"+o+\" begin = \"+o+\"(\"+t+\");\\n \"+o+\" strides = \"+o+\"(\"+e+\");\\n\\n void main() {\\n \"+a+\" coords = getOutputCoords();\\n setOutput(getX(\"+i+\"));\\n }\\n \"},ps=function(){function t(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return t.prototype.acquireTexture=function(t,e,n){var r,o=vs(e,n),a=ms(t,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===Gt.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Gt.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Gt.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Gt.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Gt.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[a].push(r),r},t.prototype.releaseTexture=function(t,e,n,r){if(null!=this.freeTextures){var o=ms(e,vs(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(t),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(t);if(i<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");a.splice(i,1),this.log()}},t.prototype.log=function(){if(this.logEnabled){var t=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",this.numFreeTextures+\" / \"+this.numUsedTextures,\"(\"+t+\")\")}},t.prototype.getNumUsedTextures=function(){return this.numUsedTextures},t.prototype.getNumFreeTextures=function(){return this.numFreeTextures},t.prototype.dispose=function(){var t=this;if(null!=this.freeTextures){for(var e in this.freeTextures)this.freeTextures[e].forEach((function(e){t.gpgpu.deleteMatrixTexture(e)}));for(var e in this.usedTextures)this.usedTextures[e].forEach((function(e){t.gpgpu.deleteMatrixTexture(e)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0}},t}();function vs(t,e){if(t===zt.UPLOAD)return Gt.PACKED_2X2_FLOAT32;if(t===zt.RENDER||null==t)return function(t){return i().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?t?Gt.PACKED_2X2_FLOAT32:Gt.UNPACKED_FLOAT32:t?Gt.PACKED_2X2_FLOAT16:Gt.UNPACKED_FLOAT16}(e);if(t===zt.DOWNLOAD||t===zt.PIXELS)return Gt.PACKED_4X1_UNSIGNED_BYTE;throw new Error(\"Unknown logical texture type \"+t)}function ms(t,e,n){return t[0]+\"_\"+t[1]+\"_\"+e+\"_\"+n}var gs=function(t,e){this.variableNames=[\"A\"];for(var n=new Array(t.length),r=0;r<n.length;r++)n[r]=t[r]*e[r];this.outputShape=n,this.rank=n.length;var o=wa(this.rank),a=function(t){var e=t.length;if(e>5)throw Error(\"Tile for rank \"+e+\" is not yet supported\");if(1===e)return\"imod(resRC, \"+t[0]+\")\";for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],r=[],o=0;o<t.length;o++)r.push(\"imod(\"+n[o]+\", \"+t[o]+\")\");return r.join()}(t);this.userCode=\"\\n void main() {\\n \"+o+\" resRC = getOutputCoords();\\n setOutput(getA(\"+a+\"));\\n }\\n \"};var ys=function(t,e){this.variableNames=[\"A\"];for(var n=new Array(t.length),r=0;r<n.length;r++)n[r]=t[e[r]];this.outputShape=n,this.rank=n.length;var o=wa(this.rank),a=function(t){var e=t.length;if(e>6)throw Error(\"Transpose for rank \"+e+\" is not yet supported\");for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],r=new Array(e),o=0;o<t.length;o++)r[t[o]]=n[o];return r.join()}(e);this.userCode=\"\\n void main() {\\n \"+o+\" resRC = getOutputCoords();\\n setOutput(getA(\"+a+\"));\\n }\\n \"};var xs=function(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;for(var n=new Array(t.length),r=0;r<n.length;r++)n[r]=t[e[r]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(\"Packed transpose for rank \"+this.rank+\" is not yet supported.\");var o=wa(this.rank),a=ia(\"rc\",this.rank),i=new Array(this.rank);for(r=0;r<e.length;r++)i[e[r]]=a[r];var s=\"vec2(\"+i.slice(-2).join()+\")\",u=\"++\"+a[this.rank-1]+\" < \"+n[this.rank-1],c=\"getChannel(getA(\"+i.join()+\"), \"+s+\")\";this.userCode=\"\\n void main() {\\n \"+o+\" rc = getOutputCoords();\\n vec4 result = vec4(0.);\\n result[0] = \"+c+\";\\n if(\"+u+\") {\\n result[1] = \"+c+\";\\n }\\n --\"+a[this.rank-1]+\";\\n if(++\"+a[this.rank-2]+\" < \"+n[this.rank-2]+\") {\\n result[2] = \"+c+\";\\n if(\"+u+\") {\\n result[3] = \"+c+\";\\n }\\n }\\n setOutput(result);\\n }\\n \"},bs=1.7580993408473768,ws=1.0507009873554805,Cs=function(t,e){this.variableNames=[\"A\"],this.outputShape=t,this.userCode=\"\\n float unaryOperation(float x) {\\n \"+e+\"\\n }\\n\\n void main() {\\n float x = getAAtOutCoords();\\n float y = unaryOperation(x);\\n\\n setOutput(y);\\n }\\n \"},Es=\"if (isnan(x)) return x;\",Rs=\"return x;\",Is=\"return abs(x);\",ks=Es+\"\\n return (x < 0.0) ? 0.0 : x;\\n\",Ss=Es+\"\\n return (x < 0.0) ? 0.0 : min(6.0, x);\\n\",As=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",Ds=\"\\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\\n // see: https://arxiv.org/abs/1706.02515\\n float scaleAlpha = \"+bs+\";\\n float scale = \"+ws+\";\\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\\n\";var Ts=\"return -x;\",Ns=\"return ceil(x);\",Fs=\"return floor(x);\",_s=\"return exp(x);\",Os=\"return exp(x) - 1.0;\",Ms=Es+\"\\n return sin(x);\\n\",Bs=Es+\"\\n return cos(x);\\n\",Ps=Es+\"\\n if (abs(x) > 1.) {\\n return NAN;\\n }\\n return asin(x);\\n\",Ls=Es+\"\\n if (abs(x) > 1.) {\\n return NAN;\\n }\\n return acos(x);\\n\",Ws=Es+\"\\n return atan(x);\\n\",Us=Es+\"return log(x + sqrt(x * x + 1.0));\",Vs=Es+\"\\n if (x < 1.0) return NAN;\\n return log(x + sqrt(x * x - 1.0));\",zs=Es+\"\\n if ((x < -1.0) || (x > 1.0)) return NAN;\\n return (log(1.0 + x) - log(1.0 - x)) / 2.0;\",Gs=\"return x;\",Hs=\"return x;\",qs=\"\\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\\n bvec4 isNaN = isnan(x);\\n\\n result.r = isNaN.r ? x.r : result.r;\\n result.g = isNaN.g ? x.g : result.g;\\n result.b = isNaN.b ? x.b : result.b;\\n result.a = isNaN.a ? x.a : result.a;\\n\\n return result;\\n\",Ks=\"\\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\\n bvec4 isNaN = isnan(x);\\n\\n result.r = isNaN.r ? x.r : result.r;\\n result.g = isNaN.g ? x.g : result.g;\\n result.b = isNaN.b ? x.b : result.b;\\n result.a = isNaN.a ? x.a : result.a;\\n\\n return result;\\n\",js=\"\\n vec4 result;\\n\\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\\n\\n return result;\\n\",Xs=function(t,e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.userCode=\"\\n vec4 unaryOperation(vec4 x) {\\n \"+e+\"\\n }\\n\\n void main() {\\n vec4 x = getAAtOutCoords();\\n vec4 y = unaryOperation(x);\\n\\n setOutput(y);\\n }\\n \"},Ys=function(t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;var e=t.length,n=sa(\"rc\",e),r=wa(e),o=function(t,e){if(1===t)return\"rc\";for(var n=\"\",r=0;r<t;r++)n+=e[r],r<t-1&&(n+=\",\");return n}(e,n),a=n.slice(-2),i=e<=1?\"rc\":\"vec2(\"+a.join(\",\")+\")\";this.userCode=\"\\n void main() {\\n \"+r+\" rc = getOutputCoords();\\n vec4 packedInput = getA(\"+o+\");\\n\\n setOutput(getChannel(packedInput, \"+i+\"));\\n }\\n \"},$s={};function Qs(t,e){if(void 0===e&&(e=!1),\"linear\"===t)return e?Hs:Rs;if(\"relu\"===t)return e?qs:ks;if(\"elu\"===t)return e?js:As;if(\"relu6\"===t)return e?Ks:Ss;if(\"prelu\"===t)return e?Pa:Ma;throw new Error(\"Activation \"+t+\" has not been implemented for the WebGL backend.\")}var Js=600;var Zs=function(t){function o(e){var n,r=t.call(this)||this;if(r.pendingRead=new WeakMap,r.pendingDisposal=new WeakSet,r.dataRefCount=new WeakMap,r.numBytesInGPU=0,r.uploadWaitMs=0,r.downloadWaitMs=0,r.warnedAboutMemory=!1,r.pendingDeletes=0,r.disposed=!1,!i().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");if(null==e){var o=jt(i().getNumber(\"WEBGL_VERSION\"));r.binaryCache=(n=i().getNumber(\"WEBGL_VERSION\"))in $s?$s[n]:($s[n]={},$s[n]),r.gpgpu=new Bi(o),r.canvas=o.canvas,r.gpgpuCreatedLocally=!0}else r.gpgpu=e,r.binaryCache={},r.gpgpuCreatedLocally=!1,r.canvas=e.gl.canvas;return r.textureManager=new ps(r.gpgpu),r.numMBBeforeWarning=null==i().global.screen?1024:i().global.screen.height*i().global.screen.width*window.devicePixelRatio*Js/1024/1024,r.texData=new xo(r,Lt),r}return e(o,t),o.prototype.numDataIds=function(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes},o.prototype.write=function(t,e,n){if(i().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),\"complex64\"===n&&null!=t)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");var r={};return this.texData.set(r,{shape:e,dtype:n,values:t,usage:zt.UPLOAD}),r},o.prototype.move=function(t,e,n,r){if(i().getBool(\"DEBUG\")&&this.checkNumericalProblems(e),\"complex64\"===r)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(t,{shape:n,dtype:r,values:e,usage:zt.UPLOAD})},o.prototype.readSync=function(t){var e=this.texData.get(t),n=e.values,r=e.dtype,o=e.complexTensors,a=e.slice,i=e.shape,s=e.isPacked;if(null!=a){var u=void 0;u=s?new Xs(i,Gs):new Cs(i,Gs);var c=this.runWebGLProgram(u,[{dataId:t,shape:i,dtype:r}],r),l=this.readSync(c.dataId);return this.disposeData(c.dataId),l}if(null!=n)return this.convertAndCacheOnCPU(t);if(\"string\"===r)return n;var h,f,d=null!=this.activeTimers;(d&&(h=et()),\"complex64\"===r)?f=Vo(o.real.dataSync(),o.imag.dataSync()):f=this.getValuesFromTexture(t);return d&&(this.downloadWaitMs+=et()-h),this.convertAndCacheOnCPU(t,f)},o.prototype.read=function(t){return n(this,void 0,void 0,(function(){var e,n,o,a,s,u,c,l,h,f,d,p,v,m,g,y,x,b,w,C,E,R;return r(this,(function(r){switch(r.label){case 0:if(this.pendingRead.has(t))return e=this.pendingRead.get(t),[2,new Promise((function(t){return e.push(t)}))];if(n=this.texData.get(t),o=n.values,a=n.shape,s=n.slice,u=n.dtype,c=n.complexTensors,l=n.isPacked,null!=s)return h=void 0,h=l?new Xs(a,Gs):new Cs(a,Gs),f=this.runWebGLProgram(h,[{dataId:t,shape:a,dtype:u}],u),d=this.read(f.dataId),this.disposeData(f.dataId),[2,d];if(null!=o)return[2,this.convertAndCacheOnCPU(t)];if(!i().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&2===i().getNumber(\"WEBGL_VERSION\"))throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");return p=null,\"complex64\"!==u&&i().get(\"WEBGL_BUFFER_SUPPORTED\")&&(v=this.decode(t),m=this.texData.get(v.dataId),p=(R=this.gpgpu).createBufferFromTexture.apply(R,[m.texture].concat(Yt(a)))),this.pendingRead.set(t,[]),\"complex64\"===u?[3,2]:[4,this.gpgpu.createAndWaitForFence()];case 1:r.sent(),r.label=2;case 2:return\"complex64\"!==u?[3,4]:[4,Promise.all([c.real.data(),c.imag.data()])];case 3:return y=r.sent(),x=y[0],b=y[1],g=Vo(x,b),[3,5];case 4:null==p?g=this.getValuesFromTexture(t):(w=k(a),g=this.gpgpu.downloadFloat32MatrixFromBuffer(p,w)),r.label=5;case 5:return null!=v&&this.disposeData(v.dataId),C=this.convertAndCacheOnCPU(t,g),E=this.pendingRead.get(t),this.pendingRead.delete(t),E.forEach((function(t){return t(C)})),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t),this.pendingDeletes--),[2,C]}}))}))},o.prototype.checkNumericalProblems=function(t){if(null!=t)for(var e=0;e<t.length;e++){var n=t[e];if(!ee(n)){if(i().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"))throw Error(\"The value \"+n+\" cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'\");throw Error(\"The value \"+n+\" cannot be represented on this device.\")}}},o.prototype.getValuesFromTexture=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,a=n.isPacked,s=k(r);if(i().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){var u=this.decode(t),c=this.texData.get(u.dataId),l=(e=this.gpgpu).downloadMatrixFromPackedTexture.apply(e,[c.texture].concat(Yt(r))).subarray(0,s);return this.disposeData(u.dataId),l}var h=i().getBool(\"WEBGL_PACK\")&&!0===a,f=h?Te(r):r,d=h?new ci(f):new ui(f),p=this.runWebGLProgram(d,[{shape:f,dtype:o,dataId:t}],\"float32\"),v=this.texData.get(p.dataId),m=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(v.texture,v.texShape[0],v.texShape[1]).subarray(0,s);return this.disposeData(p.dataId),m},o.prototype.time=function(t){return n(this,void 0,void 0,(function(){var e,n,o,a,s,u,c;return r(this,(function(r){switch(r.label){case 0:return e=this.activeTimers,n=[],o=!1,null==this.programTimersStack?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t(),a=I(this.activeTimers.map((function(t){return t.query}))).filter((function(t){return null!=t})),s=I(this.activeTimers.map((function(t){return t.name}))).filter((function(t){return null!=t})),this.activeTimers=e,o&&(this.programTimersStack=null),u={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?[4,Promise.all(a)]:[3,2];case 1:return c=r.sent(),u.kernelMs=w(c),u.getExtraProfileInfo=function(){return c.map((function(t,e){return{name:s[e],ms:t}})).map((function(t){return t.name+\": \"+t.ms})).join(\", \")},[3,3];case 2:u.kernelMs={error:\"WebGL query timers are not supported in this environment.\"},r.label=3;case 3:return this.uploadWaitMs=0,this.downloadWaitMs=0,[2,u]}}))}))},o.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},o.prototype.startTimer=function(){return i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:et(),endMs:null}},o.prototype.endTimer=function(t){return i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),t):(t.endMs=et(),t)},o.prototype.getQueryTime=function(t){return n(this,void 0,void 0,(function(){var e;return r(this,(function(n){return i().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?[2,this.gpgpu.waitForQueryAndGetTime(t)]:[2,(e=t).endMs-e.startMs]}))}))},o.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t)){if(this.pendingRead.has(t))return this.pendingDisposal.add(t),void this.pendingDeletes++;if(this.texData.has(t)){this.releaseGPUData(t);var e=this.texData.get(t).complexTensors;null!=e&&(e.real.dispose(),e.imag.dispose()),this.texData.delete(t)}}},o.prototype.releaseGPUData=function(t){var e=this.texData.get(t),n=e.texture,r=e.dtype,o=e.texShape,a=e.usage,i=e.isPacked,s=e.slice,u=s&&s.origDataId||t,c=this.dataRefCount.get(u);c>1?this.dataRefCount.set(u,c-1):(this.dataRefCount.delete(u),null!=n&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(n,o,a,i)));var l=this.texData.get(t);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null},o.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},o.prototype.getDataInfo=function(t){return this.texData.get(t)},o.prototype.getCPUBackend=function(){return i().getBool(\"WEBGL_CPU_FORWARD\")?(null==this.cpuBackend&&(this.cpuBackend=Lt.findBackend(\"cpu\")),this.cpuBackend):null},o.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=128),null!=this.getCPUBackend()&&t.every((function(t){return null==n.texData.get(t.dataId).texture&&t.size<e}))},o.prototype.getGPGPUContext=function(){return this.gpgpu},o.prototype.complex=function(t,e){var n=this.makeOutput(t.shape,\"complex64\");return this.texData.get(n.dataId).complexTensors={real:Lt.keep(t.clone()),imag:Lt.keep(e.clone())},n},o.prototype.real=function(t){return this.texData.get(t.dataId).complexTensors.real.clone()},o.prototype.imag=function(t){return this.texData.get(t.dataId).complexTensors.imag.clone()},o.prototype.slice=function(t,e,n){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.slice(t,e,n);if(0===k(n))return Fn([],n,t.dtype);var r=this.texData.get(t.dataId).isPacked,o=io(t.shape,e,n);if(r||!o){var a=i().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new fs(n):new ls(n),s=a.getCustomSetupFunc(e);return this.compileAndRun(a,[t],null,s)}return this.uploadToGPU(t.dataId),this.shallowSlice(t,e,n)},o.prototype.shallowSlice=function(t,e,n){var r=this.texData.get(t.dataId),o=this.makeOutput(n,t.dtype),a=this.texData.get(o.dataId);Object.assign(a,r),a.shape=n,a.dtype=t.dtype;var i=so(e,t.strides);r.slice&&(i+=r.slice.flatOffset),a.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||t.dataId};var s=this.dataRefCount.get(a.slice.origDataId)||1;return this.dataRefCount.set(a.slice.origDataId,s+1),o},o.prototype.stridedSlice=function(t,e,n,r){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.stridedSlice(t,e,n,r);var o=ro(e,n,r);if(o.some((function(t){return 0===t})))return Fn([],o);var a=new ds(e,r,o);return this.compileAndRun(a,[t])},o.prototype.reverse=function(t,e){var n=i().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new is(t.shape,e):new as(t.shape,e);return this.compileAndRun(n,[t])},o.prototype.concat=function(t,e){if(\"complex64\"===t[0].dtype){var n=t.map((function(t){return Tn(t)})),r=t.map((function(t){return Nn(t)}));return Dn(this.concat(n,e),this.concat(r,e))}if(this.shouldExecuteOnCPU(t))return this.cpuBackend.concat(t,e);if(1===t.length)return t[0];if(t.length>i().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){var o=Math.floor(t.length/2),a=this.concat(t.slice(0,o),e),s=this.concat(t.slice(o),e);return this.concat([a,s],e)}if(i().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&t[0].rank>1){var u=new Ga(t.map((function(t){return t.shape})),e);return this.compileAndRun(u,t)}var c=Sn(t.map((function(t){return t.shape})),e),l=t.map((function(t){return t.as2D(-1,k(t.shape.slice(e)))})),h=new za(l.map((function(t){return t.shape})));return this.compileAndRun(h,l).reshape(c)},o.prototype.neg=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.neg(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,Ts,t.dtype);var e=new Cs(t.shape,Ts);return this.compileAndRun(e,[t])},o.prototype.batchMatMul=function(t,e,n,r){var o=n?t.shape[2]:t.shape[1],a=r?e.shape[1]:e.shape[2],i=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===o||1===a)&&i>1e3){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===a?t:t.as3D(s,i,1),c=1===a?2:1,l=1===a?e.as3D(s,1,i):e;return this.multiply(u,l).sum(c,!0)}var h=Dt(t.dtype,e.dtype),f=new Hi(t.shape,[s,o,a],n,r);return this.compileAndRun(f,[t,e],h)},o.prototype.fusedBatchMatMul=function(t){var e=t.a,n=t.b,r=t.transposeA,o=t.transposeB,a=t.bias,i=t.activation,s=t.preluActivationWeights,u=r?e.shape[2]:e.shape[1],c=o?n.shape[1]:n.shape[2],l=e.shape[0],h=Dt(e.dtype,n.dtype),f=null!=a,d=null!=s,p=i?Qs(i,!0):null,v=new Hi(e.shape,[l,u,c],r,o,f,p,d),m=[e,n];return a&&m.push(a),s&&m.push(s),this.compileAndRun(v,m,h)},o.prototype.multiply=function(t,e){if(\"complex64\"===t.dtype){var n=this.texData.get(t.dataId),r=this.texData.get(e.dataId),o=new Na(Da,t.shape,e.shape),a=new Na(Ta,t.shape,e.shape),s=[this.makeComplexComponentTensorInfo(t,n.complexTensors.real),this.makeComplexComponentTensorInfo(t,n.complexTensors.imag),this.makeComplexComponentTensorInfo(e,r.complexTensors.real),this.makeComplexComponentTensorInfo(e,r.complexTensors.imag)],u=this.compileAndRun(o,s),c=this.compileAndRun(a,s),l=this.complex(u,c);return u.dispose(),c.dispose(),l}if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.multiply(t,e);if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,Oa,t.dtype);var h=new Ba(Oa,t.shape,e.shape);return this.compileAndRun(h,[t,e],t.dtype)},o.prototype.batchNormalization=function(t,e,n,r,o,a){var s=[t,e,n],u=null;null!=a&&(u=a.shape,s.push(a));var c=null;if(null!=o&&(c=o.shape,s.push(o)),i().getBool(\"WEBGL_PACK_NORMALIZATION\")){var l=new Aa(t.shape,e.shape,n.shape,u,c,r);return this.compileAndRun(l,s)}var h=new Sa(t.shape,e.shape,n.shape,u,c,r);return this.compileAndRun(h,s)},o.prototype.localResponseNormalization4D=function(t,e,n,r,o){var a=i().getBool(\"WEBGL_PACK_NORMALIZATION\")?new Vi(t.shape,e,n,r,o):new Wi(t.shape,e,n,r,o);return this.compileAndRun(a,[t])},o.prototype.LRNGrad=function(t,e,n,r,o,a,i){var s=new Ui(e.shape,r,o,a,i);return this.compileAndRun(s,[e,n,t])},o.prototype.tile=function(t,e){if(\"string\"===t.dtype){var n=this.readSync(t.dataId).map((function(t){return ot(t)}));return ta(dr(t.shape,t.dtype,n),e)}var r=new gs(t.shape,e);return this.compileAndRun(r,[t])},o.prototype.pad=function(t,e,n){var r=i().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Yi(t.shape,e,n):new Xi(t.shape,e,n);return this.compileAndRun(r,[t])},o.prototype.transpose=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.transpose(t,e);var n=i().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new xs(t.shape,e):new ys(t.shape,e);return this.compileAndRun(n,[t])},o.prototype.gather=function(t,e,n){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.gather(t,e,n);var r=new mi(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},o.prototype.batchToSpaceND=function(t,e,n){C(t.rank<=4,(function(){return\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\"}));var r=e.reduce((function(t,e){return t*e})),o=zr(t.shape,e,r),a=Gr(o.length,e.length),i=Hr(t.shape,e,r),s=qr(n,e.length),u=Kr(i,n,e.length);return t.reshape(o).transpose(a).reshape(i).slice(s,u)},o.prototype.spaceToBatchND=function(t,e,n){C(t.rank<=4,(function(){return\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\"}));var r=e.reduce((function(t,e){return t*e})),o=[[0,0]];o.push.apply(o,n);for(var a=1+e.length;a<t.shape.length;++a)o.push([0,0]);var i=t.pad(o),s=zr(i.shape,e,r,!1),u=Gr(s.length,e.length,!1),c=Hr(i.shape,e,r,!1);return i.reshape(s).transpose(u).reshape(c)},o.prototype.reduce=function(t,e,n){var r=t.shape[0],o=t.shape[1],a=$r(o),i=new Ji({windowSize:a,inSize:o,batchSize:r},e),s=this.compileAndRun(i,[t],n);return 1===s.shape[1]?s:this.reduce(s,e,n)},o.prototype.argReduce=function(t,e,n){void 0===n&&(n=null);var r=t.shape[0],o=t.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=$r(o),i=new aa({windowSize:a,inSize:o,batchSize:r},e,null==n),s=[t];null!=n&&s.push(n);var u=this.compileAndRun(i,s,\"int32\");return 1===u.shape[1]?u:this.argReduce(t,e,u)},o.prototype.argReducePacked=function(t,e,n){void 0===n&&(n=null);var r=null!=n?n.shape:t.shape,o=$r(r[r.length-1]),a=new Ra(r,o,e,null==n),i=null==n?[t]:[t,n],s=this.compileAndRun(a,i,\"int32\");return s.rank===t.rank?this.argReducePacked(t,e,s):s},o.prototype.sum=function(t,e){Cn(\"sum\",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o),i=Tt(t.dtype);return this.reduce(a,\"sum\",i).reshape(r)},o.prototype.prod=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.prod(t,e);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o),i=Tt(t.dtype);return this.reduce(a,\"prod\",i).reshape(r)},o.prototype.unsortedSegmentSum=function(t,e,n){var r=0,o=En([r],t.rank),a=t;null!=o&&(a=t.transpose(o),r=In(1,t.rank)[0]);var i=function(t,e,n){for(var r=[],o=t.length,a=0;a<o;a++)a!==e?r.push(t[a]):r.push(n);return r}(a.shape,r,n),s=k([a.shape[r]]),u=a.as2D(-1,s),c=Tt(t.dtype),l=this.segOpCompute(u,\"unsortedSegmentSum\",e,c,n).reshape(i);return null!=o&&(l=l.transpose(Rn(o))),l},o.prototype.segOpCompute=function(t,e,n,r,o){var a=t.shape[0],i=t.shape[1],s=function(t,e){var n,r=!1;for(t<=Yr?(n=t,r=!0):n=Y(t,Math.floor(Math.sqrt(t)));!r;)n>e||n===t?r=!0:n=Y(t,n+1);return n}(i,o),u=new us({windowSize:s,inSize:i,batchSize:a,numSegments:o},e),c=this.compileAndRun(u,[t,n],r);return c.shape[1]===o?c:(n=Kn(0,o).tile([i/s]),this.segOpCompute(c,e,n,r,o))},o.prototype.argMinMaxReduce=function(t,e,n){var r=[e];if(Cn(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),r,t.rank),!i().getBool(\"WEBGL_PACK_REDUCE\")||t.rank<=2){var o=bn(t.shape,r),a=o[0],s=k(o[1]),u=t.as2D(-1,s);return this.argReduce(u,n).reshape(a)}return this.argReducePacked(t,n)},o.prototype.argMin=function(t,e){return this.argMinMaxReduce(t,e,\"min\")},o.prototype.argMax=function(t,e){return this.argMinMaxReduce(t,e,\"max\")},o.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error(\"WebGL cumsum shader expects an inner-most axis=\"+(t.rank-1)+\" but got axis=\"+e);var o=new ni(t.shape,n,r);return this.compileAndRun(o,[t])},o.prototype.equal=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(equal(a, b));\\n\",\"bool\");var n=new Ba(\"return float(a == b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.notEqual=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(notEqual(a, b));\\n\",\"bool\");var n=new Ba(\"return float(a != b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.less=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.less(t,e);if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(lessThan(a, b));\\n\",\"bool\");var n=new Ba(\"return float(a < b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.lessEqual=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(lessThanEqual(a, b));\\n\",\"bool\");var n=new Ba(\"return float(a <= b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.greater=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.greater(t,e);if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(greaterThan(a, b));\\n\",\"bool\");var n=new Ba(\"return float(a > b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.greaterEqual=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(greaterThanEqual(a, b));\\n\",\"bool\");var n=new Ba(\"return float(a >= b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.logicalNot=function(t){var e=new Cs(t.shape,\"return float(!(x >= 1.0));\");return this.compileAndRun(e,[t])},o.prototype.logicalAnd=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return vec4(\\n vec4(greaterThanEqual(a, vec4(1.0))) *\\n vec4(greaterThanEqual(b, vec4(1.0))));\\n\",\"bool\");var n=new Ba(\"return float(a >= 1.0 && b >= 1.0);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.logicalOr=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n return min(\\n vec4(greaterThanEqual(a, vec4(1.0))) +\\n vec4(greaterThanEqual(b, vec4(1.0))),\\n vec4(1.0));\\n\",\"bool\");var n=new Ba(\"return float(a >= 1.0 || b >= 1.0);\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"bool\")},o.prototype.select=function(t,e,n){var r=new cs(t.rank,e.shape,e.rank);return this.compileAndRun(r,[t,e,n],Dt(e.dtype,n.dtype))},o.prototype.where=function(t){dn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");var e=t.dataSync();return na(t.shape,e)},o.prototype.topk=function(t,e,n){return ea(t.dataSync(),t.shape,t.dtype,e)},o.prototype.min=function(t,e){Cn(\"min\",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,\"min\",a.dtype).reshape(r)},o.prototype.minimum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.minimum(t,e);var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(\"\\n vec4 result = vec4(min(a, b));\\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\\n \\n result.r = isNaN.r > 0. ? NAN : result.r;\\n result.g = isNaN.g > 0. ? NAN : result.g;\\n result.b = isNaN.b > 0. ? NAN : result.b;\\n result.a = isNaN.a > 0. ? NAN : result.a;\\n\\n return result;\\n\",t.shape,e.shape):new Ba(\"\\n if (isnan(a)) return a;\\n if (isnan(b)) return b;\\n\\n return min(a, b);\\n\",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.mod=function(t,e){var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(\"\\n vec4 result = mod(a, b);\\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\\n \\n result.r = isNaN.r > 0. ? NAN : result.r;\\n result.g = isNaN.g > 0. ? NAN : result.g;\\n result.b = isNaN.b > 0. ? NAN : result.b;\\n result.a = isNaN.a > 0. ? NAN : result.a;\\n\\n return result;\\n\",t.shape,e.shape):new Ba(\"if (b == 0.0) return NAN;\\n return mod(a, b);\",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.max=function(t,e){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.max(t,e);Cn(\"max\",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,\"max\",a.dtype).reshape(r)},o.prototype.maximum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.maximum(t,e);var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(\"\\n vec4 result = vec4(max(a, b));\\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\\n \\n result.r = isNaN.r > 0. ? NAN : result.r;\\n result.g = isNaN.g > 0. ? NAN : result.g;\\n result.b = isNaN.b > 0. ? NAN : result.b;\\n result.a = isNaN.a > 0. ? NAN : result.a;\\n\\n return result;\\n\",t.shape,e.shape):new Ba(\"\\n if (isnan(a)) return a;\\n if (isnan(b)) return b;\\n\\n return max(a, b);\\n\",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.all=function(t,e){Cn(\"all\",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,\"all\",a.dtype).reshape(r)},o.prototype.any=function(t,e){Cn(\"any\",e,t.rank);var n=bn(t.shape,e),r=n[0],o=k(n[1]),a=t.as2D(-1,o);return this.reduce(a,\"any\",a.dtype).reshape(r)},o.prototype.realDivide=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")){return this.packedBinaryOp(t,e,\"\\n // vec4 one = vec4(equal(a, b));\\n // return one + (vec4(1.0) - one) * a / b;\\n vec4 result = a / b;\\n if(a.x == b.x) {\\n result.x = 1.;\\n }\\n if(a.y == b.y) {\\n result.y = 1.;\\n }\\n if(a.z == b.z) {\\n result.z = 1.;\\n }\\n if(a.w == b.w) {\\n result.w = 1.;\\n }\\n\\n return result;\\n\",\"float32\",!0)}var n=new Ba(\"\\nif (a == b) {\\n return 1.0;\\n};\\nreturn a / b;\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"float32\")},o.prototype.floorDiv=function(t,e){if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,\"\\n ivec4 ia = round(a);\\n ivec4 ib = round(b);\\n bvec4 cond = notEqual(ib, ivec4(0));\\n ivec4 result = ivec4(0);\\n vec4 s = sign(a) * sign(b);\\n\\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\\n if (cond[0]) {\\n result[0] = idiv(ia[0], ib[0], s[0]);\\n }\\n if (cond[1]) {\\n result[1] = idiv(ia[1], ib[1], s[1]);\\n }\\n if (cond[2]) {\\n result[2] = idiv(ia[2], ib[2], s[2]);\\n }\\n if (cond[3]) {\\n result[3] = idiv(ia[3], ib[3], s[3]);\\n }\\n return vec4(result);\\n\",\"int32\");var n=new Ba(\"\\n float s = sign(a) * sign(b);\\n int ia = round(a);\\n int ib = round(b);\\n if (ib != 0) {\\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\\n return float(idiv(ia, ib, s));\\n } else {\\n return NAN;\\n }\\n\",t.shape,e.shape);return this.compileAndRun(n,[t,e],\"int32\")},o.prototype.add=function(t,e){if(\"complex64\"===t.dtype&&\"complex64\"===e.dtype)return this.complexSeparableBinaryOp(t,e,Fa);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.add(t,e);var n=Dt(t.dtype,e.dtype);if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,Fa,n);var r=new Ba(Fa,t.shape,e.shape);return this.compileAndRun(r,[t,e],n)},o.prototype.packedUnaryOp=function(t,e,n){var r=new Xs(t.shape,e);return this.compileAndRun(r,[t],n)},o.prototype.packedBinaryOp=function(t,e,n,r,o){void 0===o&&(o=!1);var a=new La(n,t.shape,e.shape,o);return this.compileAndRun(a,[t,e],r)},o.prototype.complexSeparableBinaryOp=function(t,e,n){var r=this,o=this.texData.get(t.dataId),a=this.texData.get(e.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map((function(o){var a=o[0],i=o[1],s=r.makeComplexComponentTensorInfo(t,a),u=r.makeComplexComponentTensorInfo(e,i),c=new Ba(n,t.shape,e.shape);return r.compileAndRun(c,[s,u],Dt(a.dtype,i.dtype))})),s=i[0],u=i[1],c=this.complex(s,u);return s.dispose(),u.dispose(),c},o.prototype.makeComplexComponentTensorInfo=function(t,e){return{dataId:e.dataId,dtype:e.dtype,shape:t.shape}},o.prototype.addN=function(t){if(1===t.length)return t[0];if(t.length>i().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){var e=Math.floor(t.length/2),n=this.addN(t.slice(0,e)),r=this.addN(t.slice(e));return this.addN([n,r])}var o=t.map((function(t){return t.dtype})).reduce((function(t,e){return Dt(t,e)})),a=t.map((function(t){return t.shape})),s=i().getBool(\"WEBGL_PACK\")?new oa(t[0].shape,a):new ra(t[0].shape,a);return this.compileAndRun(s,t,o)},o.prototype.subtract=function(t,e){if(\"complex64\"===t.dtype&&\"complex64\"===e.dtype)return this.complexSeparableBinaryOp(t,e,_a);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.subtract(t,e);var n=Dt(t.dtype,e.dtype);if(i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"))return this.packedBinaryOp(t,e,_a,t.dtype);var r=new Ba(_a,t.shape,e.shape);return this.compileAndRun(r,[t,e],n)},o.prototype.pow=function(t,e){var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(\"\\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\\n vec4 result = multiplier * pow(abs(a), b);\\n\\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\\n bvec4 isExpZero = equal(b, vec4(0.0));\\n result.r = isExpZero.r ? 1.0 : result.r;\\n result.g = isExpZero.g ? 1.0 : result.g;\\n result.b = isExpZero.b ? 1.0 : result.b;\\n result.a = isExpZero.a ? 1.0 : result.a;\\n\\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\\n \\n result.r = isNaN.r > 0. ? NAN : result.r;\\n result.g = isNaN.g > 0. ? NAN : result.g;\\n result.b = isNaN.b > 0. ? NAN : result.b;\\n result.a = isNaN.a > 0. ? NAN : result.a;\\n\\n return result;\\n\",t.shape,e.shape):new Ba(\"\\nif(a < 0.0 && floor(b) < b){\\n return NAN;\\n}\\nif (b == 0.0) {\\n return 1.0;\\n}\\nreturn (round(mod(b, 2.0)) != 1) ?\\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\\n\",t.shape,e.shape),r=Dt(t.dtype,e.dtype);return this.compileAndRun(n,[t,e],r)},o.prototype.ceil=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.ceil(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,Ns,t.dtype);var e=new Cs(t.shape,Ns);return this.compileAndRun(e,[t])},o.prototype.floor=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.floor(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,Fs,t.dtype);var e=new Cs(t.shape,Fs);return this.compileAndRun(e,[t])},o.prototype.sign=function(t){var e=new Cs(t.shape,\"\\n if (isnan(x)) { return 0.0; }\\n return sign(x);\\n\");return this.compileAndRun(e,[t])},o.prototype.isNaN=function(t){var e=new Cs(t.shape,\"return float(isnan(x));\");return this.compileAndRun(e,[t],\"bool\")},o.prototype.isInf=function(t){var e=new Cs(t.shape,\"return float(isinf(x));\");return this.compileAndRun(e,[t],\"bool\")},o.prototype.isFinite=function(t){var e=new Cs(t.shape,\"return float(!isnan(x) && !isinf(x));\");return this.compileAndRun(e,[t],\"bool\")},o.prototype.round=function(t){var e=new Cs(t.shape,\"\\n // OpenGL ES does not support round function.\\n // The algorithm is based on banker's rounding.\\n float base = floor(x);\\n if ((x - base) < 0.5) {\\n return floor(x);\\n } else if ((x - base) > 0.5) {\\n return ceil(x);\\n } else {\\n if (mod(base, 2.0) == 0.0) {\\n return base;\\n } else {\\n return base + 1.0;\\n }\\n }\\n\");return this.compileAndRun(e,[t])},o.prototype.exp=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.exp(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,_s,t.dtype);var e=new Cs(t.shape,_s);return this.compileAndRun(e,[t])},o.prototype.expm1=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.expm1(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,Os,t.dtype);var e=new Cs(t.shape,Os);return this.compileAndRun(e,[t])},o.prototype.softmax=function(t,e){var n=O([e],t.shape),r=this.max(t,n),o=wn(r.shape,n),a=this.subtract(t,r.reshape(o)),i=this.exp(a),s=this.sum(i,n).reshape(o);return this.realDivide(i,s)},o.prototype.log=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.log(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,\"\\n vec4 result = log(x);\\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\\n result.r = isNaN.r == 1.0 ? NAN : result.r;\\n result.g = isNaN.g == 1.0 ? NAN : result.g;\\n result.b = isNaN.b == 1.0 ? NAN : result.b;\\n result.a = isNaN.a == 1.0 ? NAN : result.a;\\n\\n return result;\\n\",t.dtype);var e=new Cs(t.shape,\"if (x < 0.0) return NAN;\\n return log(x);\");return this.compileAndRun(e,[t])},o.prototype.log1p=function(t){var e=new Cs(t.shape,\"return log(1.0 + x);\");return this.compileAndRun(e,[t])},o.prototype.sqrt=function(t){var e=new Cs(t.shape,\"return sqrt(x);\");return this.compileAndRun(e,[t])},o.prototype.rsqrt=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.rsqrt(t);var e=new Cs(t.shape,\"return inversesqrt(x);\");return this.compileAndRun(e,[t])},o.prototype.reciprocal=function(t){var e=new Cs(t.shape,\"return 1.0 / x;\");return this.compileAndRun(e,[t])},o.prototype.relu=function(t){var e;return e=i().getBool(\"WEBGL_PACK\")?new Xs(t.shape,qs):new Cs(t.shape,ks),this.compileAndRun(e,[t])},o.prototype.relu6=function(t){var e;return e=i().getBool(\"WEBGL_PACK\")?new Xs(t.shape,Ks):new Cs(t.shape,Ss),this.compileAndRun(e,[t])},o.prototype.prelu=function(t,e){var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(Pa,t.shape,e.shape):new Ba(Ma,t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.elu=function(t){if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,js,t.dtype);var e=new Cs(t.shape,As);return this.compileAndRun(e,[t])},o.prototype.eluDer=function(t,e){var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(\"\\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\\n\",t.shape,e.shape):new Ba(\"return (b >= 1.0) ? a : a * (b + 1.0);\",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.selu=function(t){var e=new Cs(t.shape,Ds);return this.compileAndRun(e,[t])},o.prototype.int=function(t){var e=new Cs(t.shape,\"return float(int(x));\");return this.compileAndRun(e,[t],\"int32\")},o.prototype.clip=function(t,e,n){var r,o=(r=i().getBool(\"WEBGL_PACK_CLIP\")?new Ua(t.shape):new Wa(t.shape)).getCustomSetupFunc(e,n);return this.compileAndRun(r,[t],null,o)},o.prototype.abs=function(t){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.abs(t);if(i().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(t,Is,t.dtype);var e=new Cs(t.shape,Is);return this.compileAndRun(e,[t])},o.prototype.complexAbs=function(t){var e=this.texData.get(t.dataId),n=new Va(t.shape),r=[this.makeComplexComponentTensorInfo(t,e.complexTensors.real),this.makeComplexComponentTensorInfo(t,e.complexTensors.imag)];return this.compileAndRun(n,r)},o.prototype.sigmoid=function(t){var e=new Cs(t.shape,\"return 1.0 / (1.0 + exp(-1.0 * x));\");return this.compileAndRun(e,[t])},o.prototype.softplus=function(t){var e=new Cs(t.shape,\"\\n float epsilon = 1.1920928955078125e-7;\\n float threshold = log(epsilon) + 2.0;\\n\\n bool too_large = x > -threshold;\\n bool too_small = x < threshold;\\n\\n float result;\\n float exp_x = exp(x);\\n\\n if (too_large){\\n result = x;\\n }\\n else if (too_small){\\n result = exp_x;\\n }\\n else{\\n result = log(exp_x + 1.0);\\n }\\n return result;\\n\");return this.compileAndRun(e,[t])},o.prototype.sin=function(t){var e=new Cs(t.shape,Ms);return this.compileAndRun(e,[t])},o.prototype.cos=function(t){var e=new Cs(t.shape,Bs);return this.compileAndRun(e,[t])},o.prototype.tan=function(t){var e=new Cs(t.shape,\"return tan(x);\");return this.compileAndRun(e,[t])},o.prototype.asin=function(t){var e=new Cs(t.shape,Ps);return this.compileAndRun(e,[t])},o.prototype.acos=function(t){var e=new Cs(t.shape,Ls);return this.compileAndRun(e,[t])},o.prototype.atan=function(t){var e=new Cs(t.shape,Ws);return this.compileAndRun(e,[t])},o.prototype.atan2=function(t,e){var n=i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new La(\"\\n vec4 result = atan(a, b);\\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\\n \\n result.r = isNaN.r > 0. ? NAN : result.r;\\n result.g = isNaN.g > 0. ? NAN : result.g;\\n result.b = isNaN.b > 0. ? NAN : result.b;\\n result.a = isNaN.a > 0. ? NAN : result.a;\\n\\n return result;\\n\",t.shape,e.shape):new Ba(\"\\n if (isnan(a)) return a;\\n if (isnan(b)) return b;\\n\\n return atan(a, b);\\n\",t.shape,e.shape);return this.compileAndRun(n,[t,e])},o.prototype.sinh=function(t){var e=new Cs(t.shape,\"\\n float e2x = exp(x);\\n return (e2x - 1.0 / e2x) / 2.0;\\n\");return this.compileAndRun(e,[t])},o.prototype.cosh=function(t){var e=new Cs(t.shape,\"\\n float e2x = exp(-x);\\n return (e2x + 1.0 / e2x) / 2.0;\\n\");return this.compileAndRun(e,[t])},o.prototype.tanh=function(t){var e=new Cs(t.shape,\"\\n float e2x = exp(-2.0 * abs(x));\\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\\n\");return this.compileAndRun(e,[t])},o.prototype.asinh=function(t){var e=new Cs(t.shape,Us);return this.compileAndRun(e,[t])},o.prototype.acosh=function(t){var e=new Cs(t.shape,Vs);return this.compileAndRun(e,[t])},o.prototype.atanh=function(t){var e=new Cs(t.shape,zs);return this.compileAndRun(e,[t])},o.prototype.erf=function(t){var e=new Cs(t.shape,'\\n // Error function is calculated approximately with elementary function.\\n // See \"Handbook of Mathematical Functions with Formulas,\\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\\n float p = 0.3275911;\\n float a1 = 0.254829592;\\n float a2 = -0.284496736;\\n float a3 = 1.421413741;\\n float a4 = -1.453152027;\\n float a5 = 1.061405429;\\n\\n float sign = sign(x);\\n x = abs(x);\\n float t = 1.0 / (1.0 + p * x);\\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\\n');return this.compileAndRun(e,[t])},o.prototype.step=function(t,e){var n=new Cs(t.shape,function(t){return void 0===t&&(t=0),Es+\"\\n return x > 0.0 ? 1.0 : float(\"+t+\");\\n \"}(e));return this.compileAndRun(n,[t])},o.prototype.conv2dByMatMul=function(t,e,n,r,o,a){var s=t.shape,u=this.texData.get(t.dataId),c=n.inChannels,l=s[0]*s[1]*s[2],h=n.outChannels,f=\"channelsLast\"===n.dataFormat,d=(1===l||1===h)&&c>1e3,p=s[2]%2!=0&&!!u.isPacked;if(d||!i().getBool(\"WEBGL_LAZILY_UNPACK\")||!i().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")||!p){var v=f?s[0]*s[1]*s[2]:s[0]*s[2]*s[3],m=this.reshape(t,[1,v,n.inChannels]),g=this.reshape(e,[1,n.inChannels,n.outChannels]);return this.reshape(this.fusedBatchMatMul({a:m,b:g,transposeA:!1,transposeB:!1,bias:r,activation:o,preluActivationWeights:a}),n.outShape)}var y=f?s[0]*s[1]*(s[2]+1):s[0]*s[2]*(s[3]+1),x={dataId:t.dataId,shape:[1,y,n.inChannels],dtype:t.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,C(_e(u.shape,x.shape),(function(){return\"packed reshape \"+u.shape+\" to \"+x.shape+\" isn't free\"}));var w=this.reshape(e,[1,n.inChannels,n.outChannels]),E=this.fusedBatchMatMul({a:x,b:w,transposeA:!1,transposeB:!1,bias:r,activation:o,preluActivationWeights:a}),R=this.texData.get(E.dataId);return C(R.isPacked,(function(){return\"batchMatMul result is expected to be packed\"})),u.shape=b,R.shape=n.outShape,Lt.makeTensorFromDataId(E.dataId,n.outShape,E.dtype)},o.prototype.conv2dWithIm2Row=function(t,e,n,r,o,a){var i=n.filterWidth,s=n.filterHeight,u=n.inChannels,c=n.outWidth,l=n.outHeight,h=\"channelsLast\"===n.dataFormat,f=i*s*u,d=l*c,p=[f,d],v=t.squeeze([0]),m=e.reshape([1,f,-1]),g=new Li(p,v.shape,n),y=this.compileAndRun(g,[v]).reshape([1,p[0],p[1]]),x=null!=r,b=null!=a,w=o?Qs(o,!0):null,C=new Hi(y.shape,[1,d,n.outChannels],!0,!1,x,w,b),E=[y,m];r&&E.push(r),b&&E.push(a);var R=this.compileAndRun(C,E);return h?R.reshape([1,l,c,n.outChannels]):R.reshape([1,n.outChannels,l,c])},o.prototype.fusedConv2d=function(t){var e=t.input,n=t.filter,r=t.convInfo,o=t.bias,a=t.activation,s=t.preluActivationWeights;if(1===r.filterHeight&&1===r.filterWidth&&1===r.dilationHeight&&1===r.dilationWidth&&1===r.strideHeight&&1===r.strideWidth&&(\"SAME\"===r.padInfo.type||\"VALID\"===r.padInfo.type))return this.conv2dByMatMul(e,n,r,o,a,s);if(i().getBool(\"WEBGL_CONV_IM2COL\")&&1===e.shape[0])return this.conv2dWithIm2Row(e,n,r,o,a,s);var u=null!=o,c=null!=s,l=a?Qs(a,!1):null,h=new Qa(r,u,l,c),f=[e,n];return o&&f.push(o),s&&f.push(s),this.compileAndRun(h,f)},o.prototype.conv2d=function(t,e,n){if(1===n.filterHeight&&1===n.filterWidth&&1===n.dilationHeight&&1===n.dilationWidth&&1===n.strideHeight&&1===n.strideWidth&&(\"SAME\"===n.padInfo.type||\"VALID\"===n.padInfo.type))return this.conv2dByMatMul(t,e,n);if(i().getBool(\"WEBGL_CONV_IM2COL\")&&1===t.shape[0])return this.conv2dWithIm2Row(t,e,n);var r=new Qa(n);return this.compileAndRun(r,[t,e])},o.prototype.conv2dDerInput=function(t,e,n){var r=new Ka(n);return this.compileAndRun(r,[t,e])},o.prototype.conv2dDerFilter=function(t,e,n){var r=new qa(n);return this.compileAndRun(r,[t,e])},o.prototype.fusedDepthwiseConv2D=function(t){var e,n=t.input,r=t.filter,o=t.convInfo,a=t.bias,s=t.activation,u=t.preluActivationWeights,c=i().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&o.strideWidth<=2&&o.outChannels/o.inChannels==1,l=s?Qs(s,c):null,h=[n,r],f=null!=a,d=null!=u;return f&&h.push(a),d&&h.push(u),c?(e=new ti(o,f,l,d),this.compileAndRun(e,h)):(e=new Za(o,f,l,d),this.compileAndRun(e,h))},o.prototype.depthwiseConv2D=function(t,e,n){var r;return i().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new ti(n),this.compileAndRun(r,[t,e])):(r=new Za(n),this.compileAndRun(r,[t,e]))},o.prototype.depthwiseConv2DDerInput=function(t,e,n){var r=new $a(n);return this.compileAndRun(r,[t,e])},o.prototype.depthwiseConv2DDerFilter=function(t,e,n){var r=new Ya(n);return this.compileAndRun(r,[t,e])},o.prototype.conv3d=function(t,e,n){var r=new Ja(n);return this.compileAndRun(r,[t,e])},o.prototype.conv3dDerInput=function(t,e,n){var r=new Xa(n);return this.compileAndRun(r,[t,e])},o.prototype.conv3dDerFilter=function(t,e,n){var r=new ja(n);return this.compileAndRun(r,[t,e])},o.prototype.maxPool=function(t,e){var n=new $i(e,\"max\",!1);return this.compileAndRun(n,[t])},o.prototype.avgPool=function(t,e){var n=new $i(e,\"avg\",!1);return this.compileAndRun(n,[t],\"float32\")},o.prototype.maxPoolBackprop=function(t,e,n,r){var o=new $i(r,\"max\",!0),a=this.compileAndRun(o,[e]),i=new zi(r),s=this.compileAndRun(i,[t,a],e.dtype);return a.dispose(),s},o.prototype.avgPoolBackprop=function(t,e,n){var r=new Ia(n);return this.compileAndRun(r,[t],e.dtype)},o.prototype.cast=function(t,e){return Po(t,e,this)},o.prototype.unstack=function(t,e){for(var n=t.shape[e],r=new Array(t.rank-1),o=0,a=0;a<t.rank;a++)a!==e&&(r[o++]=t.shape[a]);var i=new Array(t.rank).fill(0),s=t.shape.slice();s[e]=1;var u=new Array(n);for(a=0;a<u.length;a++)i[e]=a,u[a]=this.slice(t,i,s).reshape(r);return u},o.prototype.avgPool3d=function(t,e){var n=new Qi(e,\"avg\",!1);return this.compileAndRun(n,[t],\"float32\")},o.prototype.avgPool3dBackprop=function(t,e,n){var r=new ka(n);return this.compileAndRun(r,[t],e.dtype)},o.prototype.maxPool3d=function(t,e){var n=new Qi(e,\"max\",!1);return this.compileAndRun(n,[t],\"float32\")},o.prototype.maxPool3dBackprop=function(t,e,n,r){var o=new Qi(r,\"max\",!0),a=this.compileAndRun(o,[e]),i=new Gi(r),s=this.compileAndRun(i,[t,a],e.dtype);return a.dispose(),s},o.prototype.reshape=function(t,e){var n=this.texData.get(t.dataId);if(n.isPacked&&!_e(t.shape,e)&&(null===n.texture||!_e(n.shape,e))){var r=this.packedReshape(t,e);return Lt.makeTensorFromDataId(r.dataId,r.shape,r.dtype)}return Lo(t,e)},o.prototype.resizeBilinear=function(t,e,n,r){var o=i().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new ns(t.shape,e,n,r):new es(t.shape,e,n,r);return this.compileAndRun(o,[t],\"float32\")},o.prototype.resizeBilinearBackprop=function(t,e,n){var r=new ts(t,e,n);return this.compileAndRun(r,[t])},o.prototype.resizeNearestNeighbor=function(t,e,n,r){var o=new os(t.shape,e,n,r);return this.compileAndRun(o,[t])},o.prototype.resizeNearestNeighborBackprop=function(t,e,n){var r=new rs(t,e,n);return this.compileAndRun(r,[t])},o.prototype.multinomial=function(t,e,n,r){var o=e?t:go(t),a=o.shape[0],i=o.shape[1],s=new qi(a,i,n),u=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],\"int32\",u)},o.prototype.oneHot=function(t,e,n,r){var o=new Ki(t.size,e,n,r);return this.compileAndRun(o,[t])},o.prototype.diag=function(t){var e=new si(t.size);return this.compileAndRun(e,[t])},o.prototype.nonMaxSuppression=function(t,e,n,r,o){return dn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\"),jo(t.dataSync(),e.dataSync(),n,r,o)},o.prototype.cropAndResize=function(t,e,n,r,o,a){var i=new ei(t.shape,e.shape,r,o,a);return this.compileAndRun(i,[t,e,n],\"float32\")},o.prototype.depthToSpace=function(t,e,n){C(e>1,(function(){return\"blockSize should be > 1 for depthToSpace, but was: \"+e}));var r=t.shape[0],o=\"NHWC\"===n?t.shape[1]:t.shape[2],a=\"NHWC\"===n?t.shape[2]:t.shape[3],i=\"NHWC\"===n?t.shape[3]:t.shape[1],s=o*e,u=a*e,c=i/(e*e),l=new ii(\"NHWC\"===n?[r,s,u,c]:[r,c,s,u],e,n);return this.compileAndRun(l,[t])},o.prototype.split=function(t,e,n){return Zo(t,e,n)},o.prototype.scatterND=function(t,e,n){var r=Zr(0,t,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,c=[u/i,i],l=t.reshape([a,o]),h=e.reshape([a,i]);if(0===u)return Lo(Fn([]),n);var f=On(0),d=new ss(a,o,l.rank,h.rank,s,c);return this.compileAndRun(d,[h,l,f]).reshape(n)},o.prototype.sparseToDense=function(t,e,n,r){var o=Zr(0,t,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,c=new ss(i,a,t.rank,e.rank,s,[u,1],!1);return this.compileAndRun(c,[e,t,r]).reshape(n)},o.prototype.fft=function(t){return this.fftImpl(t,!1)},o.prototype.ifft=function(t){return this.fftImpl(t,!0)},o.prototype.fftImpl=function(t,e){var n=this.texData.get(t.dataId),r=new pi(fi,t.shape,e),o=new pi(di,t.shape,e),a=[this.makeComplexComponentTensorInfo(t,n.complexTensors.real),this.makeComplexComponentTensorInfo(t,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(t.shape[0],t.shape[1]);return i.dispose(),s.dispose(),u},o.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=jr(t,e),a=o[0],i=o[1],s=o[2],u=o[3],c=e.reshape([i,r]),l=t.reshape([t.size/s,s]),h=new gi(r,u,[i,s]);return this.compileAndRun(h,[l,c]).reshape(a)},o.prototype.fill=function(t,e,n){if(\"string\"===(n=n||j(e))){var r=P(n,k(t));return r.fill(e),Lt.makeTensor(r,t,n,this)}var o=new vi(t,e),a=o.getCustomSetupFunc(e);return this.compileAndRun(o,[],n,a)},o.prototype.onesLike=function(t){if(\"string\"===t.dtype)throw new Error(\"onesLike is not supported under string dtype\");return this.fill(t.shape,1,t.dtype)},o.prototype.zerosLike=function(t){return this.fill(t.shape,\"string\"===t.dtype?\"\":0,t.dtype)},o.prototype.linspace=function(t,e,n){return Wo(t,e,n)},o.prototype.makeTensorInfo=function(t,e){var n=this.write(null,t,e);return this.texData.get(n).usage=null,{dataId:n,shape:t,dtype:e}},o.prototype.makeOutput=function(t,e){var n=this.makeTensorInfo(t,e).dataId;return Lt.makeTensorFromDataId(n,t,e,this)},o.prototype.unpackTensor=function(t){var e=new Ys(t.shape);return this.runWebGLProgram(e,[t],t.dtype)},o.prototype.packTensor=function(t){var e=new ji(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)},o.prototype.packedReshape=function(t,e){var n=[Ae(t.shape)].concat(De(t.shape)),r={dtype:t.dtype,shape:n,dataId:t.dataId},o=[Ae(e)].concat(De(e)),a=new Zi(o,n),i=this.runWebGLProgram(a,[r],t.dtype,null,!0);return{dataId:i.dataId,shape:e,dtype:i.dtype}},o.prototype.decode=function(t){var e,n=this.texData.get(t),r=n.isPacked,o=n.shape,a=n.dtype,i=Te(o);e=r?new ai(i):new oi(i);return{dtype:a,shape:o,dataId:this.runWebGLProgram(e,[{shape:i,dtype:a,dataId:t}],a,null,!0).dataId}},o.prototype.runWebGLProgram=function(t,e,n,r,o){var a=this;void 0===o&&(o=!1);var s=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(s.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Vt.DENSE){var c=Yt(t.outputShape);u.texShape=c.map((function(t){return 2*t}))}if(null!=t.outTexUsage&&(u.usage=t.outTexUsage),0===k(s.shape))return u.values=B(s.dtype,0),s;var l=[],h=e.map((function(e){if(\"complex64\"===e.dtype)throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");var n=a.texData.get(e.dataId);if(null==n.texture){if(!t.packedInputs&&k(e.shape)<=i().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:e.shape,texData:null,isUniform:!0,uniformValues:n.values};t.packedInputs&&(n.isPacked=!0,n.shape=e.shape)}else if(!!n.isPacked!=!!t.packedInputs)e=n.isPacked?a.unpackTensor(e):a.packTensor(e),l.push(e),n=a.texData.get(e.dataId);else if(n.isPacked&&!_e(n.shape,e.shape)){var r=e,o=e.shape;e.shape=n.shape,e=a.packedReshape(e,o),l.push(e),n=a.texData.get(e.dataId),r.shape=o}return a.uploadToGPU(e.dataId),{shape:e.shape,texData:n,isUniform:!1}}));this.uploadToGPU(s.dataId);var f,d={shape:s.shape,texData:u,isUniform:!1},p=function(t,e,n){var r=\"\";e.concat(n).forEach((function(t){var e=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0,n=t.isUniform?\"uniform\":t.texData.texShape;r+=t.shape+\"_\"+n+\"_\"+e}));var o=t.userCode,a=t.constructor.name;return a+=\"_\"+r+\"_\"+o}(t,h,d),v=this.getAndSaveBinary(p,(function(){return function(t,e,n,r){var o=e.userCode,a=n.map((function(t,n){var r={logicalShape:t.shape,texShape:t.isUniform?null:t.texData.texShape,isUniform:t.isUniform,isPacked:!t.isUniform&&t.texData.isPacked,flatOffset:null};return null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0&&(r.flatOffset=t.texData.slice.flatOffset),{name:e.variableNames[n],shapeInfo:r}})),s=a.map((function(t){return t.shapeInfo})),u={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},c=fa(a,u,o,e.packedInputs),l=t.createProgram(c),h=null,f=t.getUniformLocation(l,\"NAN\",!1);1===i().getNumber(\"WEBGL_VERSION\")&&(h=t.getUniformLocation(l,\"INFINITY\",!1));for(var d={},p=0;p<e.variableNames.length;p++){var v=e.variableNames[p];d[v]=t.getUniformLocation(l,v,!1),d[\"offset\"+v]=t.getUniformLocation(l,\"offset\"+v,!1)}return{program:e,source:c,webGLProgram:l,uniformLocations:d,inShapeInfos:s,outShapeInfo:u,infLoc:h,nanLoc:f}}(a.gpgpu,t,h,d)})),m=null!=this.activeTimers;if(m&&(f=this.startTimer()),function(t,e,n,r,o){Pi(e.inShapeInfos,n),Pi([e.outShapeInfo],[r]);var a=r.texData.texture,s=r.texData.texShape;r.texData.isPacked?t.setOutputPackedMatrixTexture(a,s[0],s[1]):t.setOutputMatrixTexture(a,s[0],s[1]),t.setProgram(e.webGLProgram),1===i().getNumber(\"WEBGL_VERSION\")&&null!==e.infLoc&&t.gl.uniform1f(e.infLoc,1/0),null!==e.nanLoc&&t.gl.uniform1f(e.nanLoc,NaN),n.forEach((function(n,r){var o=e.program.variableNames[r],a=e.uniformLocations[o],i=e.uniformLocations[\"offset\"+o];if(null!=a)if(n.isUniform)if(k(n.shape)<2)t.gl.uniform1f(a,n.uniformValues[0]);else{var s=n.uniformValues;s instanceof Float32Array||(s=new Float32Array(s)),t.gl.uniform1fv(a,s)}else null!=n.texData.slice&&null!=i&&t.gl.uniform1i(i,n.texData.slice.flatOffset),t.setInputMatrixTexture(n.texData.texture,a,r)})),null!=o&&o(t,e.webGLProgram),t.executeProgram()}(this.gpgpu,v,h,d,r),l.forEach((function(t){return a.disposeData(t.dataId)})),m&&(f=this.endTimer(f),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(f)})),!i().getBool(\"WEBGL_LAZILY_UNPACK\")&&u.isPacked&&!1===o){var g=this.unpackTensor(s);return this.disposeData(s.dataId),g}return s},o.prototype.compileAndRun=function(t,e,n,r,o){void 0===o&&(o=!1),n=n||e[0].dtype;var a=this.runWebGLProgram(t,e,n,r,o);return Lt.makeTensorFromDataId(a.dataId,a.shape,a.dtype)},o.prototype.getAndSaveBinary=function(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]},o.prototype.getTextureManager=function(){return this.textureManager},o.prototype.dispose=function(){var t=this;if(!this.disposed){if(!i().getBool(\"IS_TEST\"))Object.keys(this.binaryCache).forEach((function(e){t.gpgpu.deleteProgram(t.binaryCache[e].webGLProgram),delete t.binaryCache[e]}));this.textureManager.dispose(),null!=this.canvas&&\"undefined\"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}},o.prototype.floatPrecision=function(){var t=this;return null==this.floatPrecisionValue&&(this.floatPrecisionValue=Ze((function(){if(!i().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){var e=i().getBool(\"DEBUG\");i().set(\"DEBUG\",!1);var n=t.abs(On(1e-8)).dataSync()[0];if(i().set(\"DEBUG\",e),n>0)return 32}return 16}))),this.floatPrecisionValue},o.prototype.epsilon=function(){return 32===this.floatPrecision()?1e-7:1e-4},o.prototype.uploadToGPU=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,a=n.values,i=n.texture,s=n.usage,u=n.isPacked;if(null==i){var c,l=null!=this.activeTimers;l&&(c=et());var h=n.texShape;if(null==h&&(h=Ne(r,u),n.texShape=h),null!=a){var f=Te(r),d=void 0,p=h[1],v=h[0],m=a instanceof Uint8Array;u?(p=(e=$t(h[0],h[1]))[0],v=e[1],d=new hi(f,[v,p],m)):d=new li(f,[v,p],m);var g=this.makeTensorInfo([v,p],o);this.texData.get(g.dataId).usage=m?zt.PIXELS:zt.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),p,v,a);var y=this.runWebGLProgram(d,[g],o,null,!0),x=this.texData.get(y.dataId);n.texture=x.texture,n.texShape=x.texShape,n.isPacked=x.isPacked,n.usage=x.usage,this.disposeData(g.dataId),this.texData.delete(y.dataId),n.values=null,l&&(this.uploadWaitMs+=et()-c)}else{var b=this.acquireTexture(h,s,o,u);n.texture=b}}},o.prototype.convertAndCacheOnCPU=function(t,e){var n=this.texData.get(t),r=n.dtype;return this.releaseGPUData(t),null!=e&&(n.values=function(t,e){if(\"float32\"===e||\"complex64\"===e)return t;if(\"int32\"===e||\"bool\"===e){for(var n=\"int32\"===e?new Int32Array(t.length):new Uint8Array(t.length),r=0;r<n.length;++r)n[r]=Math.round(t[r]);return n}throw new Error(\"Unknown dtype \"+e)}(e,r)),n.values},o.prototype.acquireTexture=function(t,e,n,r){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>1024*this.numMBBeforeWarning*1024){var o=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(\"High memory usage in GPU: \"+o+\" MB, most likely due to a memory leak\")}return this.textureManager.acquireTexture(t,e,r)},o.prototype.computeBytes=function(t,e){return t[0]*t[1]*z(e)},o}(bo);Wt()&&Lt.registerBackend(\"webgl\",(function(){return new Zs}),2);var tu=An({square_:function(t){var e=mn(t,\"x\",\"square\"),n=[e];return Lt.runKernelFunc((function(t,n){return n([e]),t.square(e)}),{x:e},null,\"Square\",{},n,[])}}),eu=\"SquaredDifference\";var nu=An({squaredDifference_:function(t,e){var n,r=mn(t,\"a\",\"squaredDifference\"),o=mn(e,\"b\",\"squaredDifference\");n=Nt(r,o),r=n[0],o=n[1],Ro(r.shape,o.shape);var a={a:r,b:o},i=[r,o];return Lt.runKernelFunc((function(t,e){var n=t.squaredDifference(r,o);return e([r,o]),n}),a,(function(t,e){var n=e[0],r=e[1],o=On(2);return{a:function(){return t.mul(n.sub(r).mul(o))},b:function(){return t.mul(r.sub(n).mul(o))}}}),eu,{},i,[])}});var ru=An({abs_:function(t){var e=mn(t,\"x\",\"abs\");return\"complex64\"===e.dtype?Lt.runKernelFunc((function(t){return t.complexAbs(e)}),{$x:e}):Lt.runKernelFunc((function(t,n){var r=t.abs(e);return n([e]),r}),{x:e},(function(t,e){var n=e[0];return{x:function(){return t.mul(n.toFloat().step(-1))}}}),\"Abs\")}}),ou=An({acos_:function(t){var e=mn(t,\"x\",\"acos\");return Lt.runKernelFunc((function(t,n){var r=t.acos(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return{$x:function(){return t.divStrict(On(1).sub(n.toFloat().square()).sqrt()).neg()}}}))}}),au=An({acosh_:function(t){var e=mn(t,\"x\",\"acosh\");return Lt.runKernelFunc((function(t,n){var r=t.acosh(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return{$x:function(){return t.divStrict(n.toFloat().square().sub(1).sqrt())}}}))}}),iu=An({asin_:function(t){var e=mn(t,\"x\",\"asin\");return Lt.runKernelFunc((function(t,n){var r=t.asin(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return{$x:function(){return t.divStrict(On(1).sub(n.toFloat().square()).sqrt())}}}))}}),su=An({asinh_:function(t){var e=mn(t,\"x\",\"asinh\");return Lt.runKernelFunc((function(t,n){var r=t.asinh(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return{$x:function(){return t.divStrict(On(1).add(n.toFloat().square()).sqrt())}}}))}}),uu=An({atan_:function(t){var e=mn(t,\"x\",\"atan\");return Lt.runKernelFunc((function(t,n){var r=t.atan(e);return n([e]),r}),{$x:e},(function(t,e){var 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n=mn(t,\"a\",\"greaterStrict\"),r=mn(e,\"b\",\"greaterStrict\");return E(n.shape,r.shape,\"Error in greaterStrict: \"),n.greater(r)}}),Tc=An({less_:function(t,e){var n,r=mn(t,\"a\",\"less\"),o=mn(e,\"b\",\"less\");return n=Nt(r,o),r=n[0],o=n[1],Ro(r.shape,o.shape),Lt.runKernelFunc((function(t){return t.less(r,o)}),{a:r,b:o},null,\"Less\")}}),Nc=An({lessEqual_:function(t,e){var n,r=mn(t,\"a\",\"lessEqual\"),o=mn(e,\"b\",\"lessEqual\");return n=Nt(r,o),r=n[0],o=n[1],Ro(r.shape,o.shape),Lt.runKernelFunc((function(t,e){var n=t.lessEqual(r,o);return e([r,o]),n}),{a:r,b:o},null,\"LessEqual\")}}),Fc=An({lessEqualStrict_:function(t,e){var n=mn(t,\"a\",\"lessEqualStrict\"),r=mn(e,\"b\",\"lessEqualStrict\");return E(n.shape,r.shape,\"Error in lessEqualStrict: \"),n.lessEqual(r)}}),_c=An({lessStrict_:function(t,e){var n=mn(t,\"a\",\"lessStrict\"),r=mn(e,\"b\",\"lessStrict\");return E(n.shape,r.shape,\"Error in lessStrict: \"),n.less(r)}}),Oc=An({notEqual_:function(t,e){var n,r=mn(t,\"a\",\"notEqual\"),o=mn(e,\"b\",\"notEqual\");return n=Nt(r,o),r=n[0],o=n[1],Ro(r.shape,o.shape),Lt.runKernelFunc((function(t){return t.notEqual(r,o)}),{a:r,b:o},null,\"NotEqual\")}}),Mc=An({notEqualStrict_:function(t,e){var n=mn(t,\"a\",\"notEqualStrict\"),r=mn(e,\"b\",\"notEqualStrict\");return E(n.shape,r.shape,\"Error in notEqualStrict: \"),n.notEqual(r)}});function Bc(t,e){for(var n=[],r=t;r<e;++r)n.push(r);return n}function Pc(t){for(var e=[],n=0;n<t.length;++n)for(var r=0;r<t[n].length;++r)e.push(t[n][r]);return e}var Lc=An({gather_:function(t,e,n){void 0===n&&(n=0);var r=mn(t,\"x\",\"gather\"),o=mn(e,\"indices\",\"gather\",\"int32\");n=O(n,r.shape)[0];var a=function(t,e,n){for(var r=t.shape[n],o=[],a=1,i=1,s=0;s<n;s++)o.push(t.shape[s]),a*=t.shape[s];for(s=0;s<e.rank;s++)o.push(e.shape[s]);for(s=n+1;s<t.rank;s++)o.push(t.shape[s]),i*=t.shape[s];return{batchSize:a,sliceSize:i,dimSize:r,outputShape:o}}(r,o,n);return Lt.runKernelFunc((function(t,e){var 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must match input depth for filter \"+u.shape[1]+\".\"})),C(Mo(n,a),(function(){return\"Error in conv1D: Either stride or dilation must be 1. 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Got dilations '\"+a+\"'\"}));var o=e[0],i=e[1];return{x:function(){return Gc(o.shape,t,i,n,r)},$filter:function(){return function(t,e,n,r,o){var a=t;4===t.rank&&(a=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]));var i=e;4===i.rank&&(i=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));C(5===a.rank,(function(){return\"Error in conv3dDerFilter: input must be rank 5, but got shape \"+a.shape+\".\"})),C(5===i.rank,(function(){return\"Error in conv3dDerFilter: dy must be rank 5, but got shape \"+i.shape+\".\"})),C(5===n.length,(function(){return\"Error in conv3dDerFilter: filterShape must be length 5, but got \"+n+\".\"})),C(a.shape[4]===n[3],(function(){return\"Error in conv3dDerFilter: depth of input \"+a.shape[4]+\") must match input depth in filter (\"+n[3]+\".\"})),C(i.shape[4]===n[4],(function(){return\"Error in conv3dDerFilter: depth of dy (\"+i.shape[4]+\") must match output depth for filter (\"+n[4]+\").\"}));var s=Ao(a.shape,n,r,1,o);return Lt.runKernelFunc((function(t){return t.conv3dDerFilter(a,i,s)}),{x5D:a,dy5D:i})}(o,t,i.shape,n,r)}}}));return c?h.as4D(h.shape[1],h.shape[2],h.shape[3],h.shape[4]):h}}),jc=An({conv2dDerFilter_:function(t,e,n,r,o,a,i){void 0===a&&(a=\"NHWC\");var s=t;3===t.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var u=e;3===u.rank&&(u=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),C(4===s.rank,(function(){return\"Error in conv2dDerFilter: input must be rank 4, but got shape \"+s.shape+\".\"})),C(4===u.rank,(function(){return\"Error in conv2dDerFilter: dy must be rank 4, but got shape \"+u.shape+\".\"})),C(4===n.length,(function(){return\"Error in conv2dDerFilter: filterShape must be length 4, but got \"+n+\".\"}));var c=\"NHWC\"===a?s.shape[3]:s.shape[1],l=\"NHWC\"===a?u.shape[3]:u.shape[1];C(c===n[2],(function(){return\"Error in conv2dDerFilter: depth of input \"+c+\") must match input depth in filter (\"+n[2]+\".\"})),C(l===n[3],(function(){return\"Error in conv2dDerFilter: depth of dy (\"+l+\") must match output depth for filter (\"+n[3]+\").\"})),null!=i&&C(A(o),(function(){return\"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+o+\".\"}));var h=Bo(a),f=So(s.shape,n,r,1,o,i,!1,h);return Lt.runKernelFunc((function(t){return t.conv2dDerFilter(s,u,f)}),{x4D:s,dy4D:u})}}),Xc=An({conv2dDerInput_:Vc}),Yc=An({depthwiseConv2d_:function(t,e,n,r,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=mn(t,\"x\",\"depthwiseConv2d\"),u=mn(e,\"filter\",\"depthwiseConv2d\"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),C(4===c.rank,(function(){return\"Error in depthwiseConv2d: input must be rank 4, but got rank \"+c.rank+\".\"})),C(4===u.rank,(function(){return\"Error in depthwiseConv2d: filter must be rank 4, but got rank \"+u.rank+\".\"})),C(c.shape[3]===u.shape[2],(function(){return\"Error in depthwiseConv2d: number of input channels (\"+c.shape[3]+\") must match the inChannels dimension in filter \"+u.shape[2]+\".\"})),null==a&&(a=[1,1]),C(Mo(n,a),(function(){return\"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"})),null!=i&&C(A(r),(function(){return\"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\"}));var h=So(c.shape,u.shape,n,a,r,i,!0),f=[c,u],d=Lt.runKernelFunc((function(t,e){var n=t.depthwiseConv2D(c,u,h);return e([c,u]),n}),{x:c,filter:u},(function(t,e){C(Oo(a),(function(){return\"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '\"+a+\"'\"}));var n=e[0],r=e[1];return{x:function(){return $c(n.shape,t,r,h)},filter:function(){return Qc(n,t,r.shape,h)}}}),\"DepthwiseConv2dNative\",h,f);return l?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}}),$c=An({depthwiseConv2dDerInput_:function(t,e,n,r){var o=e,a=!1;3===e.rank&&(a=!0,o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var i=Lt.runKernelFunc((function(t){return t.depthwiseConv2DDerInput(o,n,r)}),{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}}),Qc=An({depthwiseConv2dDerFilter_:function(t,e,n,r){var o=t;3===t.rank&&(o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var a=e;return 3===a.rank&&(a=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),Lt.runKernelFunc((function(t){return t.depthwiseConv2DDerFilter(o,a,r)}),{x4D:o,dy4D:a})}}),Jc=An({separableConv2d_:function(t,e,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i=\"NHWC\");var s=mn(t,\"x\",\"separableConv2d\"),u=mn(e,\"depthwiseFilter\",\"separableConv2d\"),c=mn(n,\"pointwiseFilter\",\"separableConv2d\"),l=s,h=!1;if(3===s.rank&&(h=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),\"NCHW\"===i)throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");C(4===l.rank,(function(){return\"Error in separableConv2d: input must be rank 4, but got rank \"+l.rank+\".\"})),C(4===u.rank,(function(){return\"Error in separableConv2d: depthwise filter must be rank 4, but got rank \"+u.rank+\".\"})),C(4===c.rank,(function(){return\"Error in separableConv2d: pointwise filter must be rank 4, but got rank \"+u.rank+\".\"})),C(1===c.shape[0],(function(){return\"Error in separableConv2d: the first dimension of pointwise filter must be 1, but got \"+c.shape[0]+\".\"})),C(1===c.shape[1],(function(){return\"Error in separableConv2d: the second dimension of pointwise filter must be 1, but got \"+c.shape[1]+\".\"}));var f=u.shape[2],d=u.shape[3];C(c.shape[2]===f*d,(function(){return\"Error in separableConv2d: the third dimension of pointwise filter must be \"+f*d+\", but got \"+c.shape[2]+\".\"}));var p=Yc(l,u,r,o,i,a),v=qc(p,c,1,\"valid\",i);return h?v.as3D(v.shape[1],v.shape[2],v.shape[3]):v}}),Zc=An({conv2dTranspose_:function(t,e,n,r,o,a){return Vc(n,mn(t,\"x\",\"conv2dTranspose\"),mn(e,\"filter\",\"conv2dTranspose\"),r,o,\"NHWC\",a)}}),tl=An({conv3dTranspose_:function(t,e,n,r,o){return Gc(n,mn(t,\"x\",\"conv3dTranspose\"),mn(e,\"filter\",\"conv3dTranspose\"),r,o)}});var el=An({matMul_:function(t,e,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=mn(t,\"a\",\"matMul\"),i=mn(e,\"b\",\"matMul\");o=Nt(a,i),a=o[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],c=n?a.shape[a.rank-1]:a.shape[a.rank-2],l=r?i.shape[i.rank-2]:i.shape[i.rank-1],h=a.shape.slice(0,-2),f=i.shape.slice(0,-2),d=k(h),p=k(f);C(a.rank>=2&&i.rank>=2&&a.rank===i.rank,(function(){return\"Error in matMul: inputs must have the same rank of at least 2, got ranks \"+a.rank+\" and \"+i.rank+\".\"})),C(S(h,f),(function(){return\"Error in matMul: outer dimensions (\"+h+\") and (\"+f+\") of Tensors with shapes \"+a.shape+\" and \"+i.shape+\" must match.\"})),C(s===u,(function(){return\"Error in matMul: inner shapes (\"+s+\") and (\"+u+\") of Tensors with shapes \"+a.shape+\" and \"+i.shape+\" and transposeA=\"+n+\" and transposeB=\"+r+\" must match.\"}));var v=a.shape.slice(0,-2).concat([c,l]),m=n?a.as3D(d,s,c):a.as3D(d,c,s),g=r?i.as3D(p,l,u):i.as3D(p,u,l),y={transposeA:n,transposeB:r};return Lt.runKernelFunc((function(t,e){var o=t.batchMatMul(m,g,n,r);return e([m,g]),o}),{a:m,b:g},(function(t,e){var o=e,a=o[0],i=o[1];return n||r?!n&&r?{a:function(){return t.matMul(i,!1,!1)},b:function(){return t.matMul(a,!0,!1)}}:n&&!r?{a:function(){return i.matMul(t,!1,!0)},b:function(){return a.matMul(t,!1,!1)}}:{a:function(){return i.matMul(t,!0,!0)},b:function(){return t.matMul(a,!0,!0)}}:{a:function(){return t.matMul(i,!1,!0)},b:function(){return a.matMul(t,!0,!1)}}}),\"BatchMatMul\",y).reshape(v)}}),nl=An({dot_:function(t,e){var n=mn(t,\"t1\",\"dot\"),r=mn(e,\"t2\",\"dot\");C(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),(function(){return\"Error in dot: inputs must all be rank 1 or 2, but got ranks \"+n.rank+\" and \"+r.rank+\".\"}));var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return C(o===a,(function(){return\"Error in dot: inner dimensions of inputs must match, but got \"+o+\" and \"+a+\".\"})),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}}),rl=An({outerProduct_:function(t,e){var n=mn(t,\"v1\",\"outerProduct\"),r=mn(e,\"v2\",\"outerProduct\");return C(1===n.rank&&1===r.rank,(function(){return\"Error in outerProduct: inputs must be rank 1, but got ranks \"+n.rank+\" and \"+r.rank+\".\"})),n.as2D(-1,1).matMul(r.as2D(1,-1))}});var ol=An({reverse_:function(t,e){var n=mn(t,\"x\",\"reverse\");if(0===n.rank)return n.clone();var r=O(e,n.shape);return Lt.runKernelFunc((function(t){return t.reverse(n,r)}),{$x:n},(function(t){return{$x:function(){return t.reverse(r)}}})).reshapeAs(n)}}),al=An({reverse1d_:function(t){var e=mn(t,\"x\",\"reverse\");return C(1===e.rank,(function(){return\"Error in reverse1D: x must be rank 1 but got rank \"+e.rank+\".\"})),ol(e,0)}}),il=An({reverse2d_:function(t,e){var n=mn(t,\"x\",\"reverse\");return C(2===n.rank,(function(){return\"Error in reverse2D: x must be rank 2 but got rank \"+n.rank+\".\"})),ol(n,e)}}),sl=An({reverse3d_:function(t,e){var n=mn(t,\"x\",\"reverse\");return C(3===n.rank,(function(){return\"Error in reverse3D: x must be rank 3 but got rank \"+n.rank+\".\"})),ol(n,e)}}),ul=An({reverse4d_:function(t,e){var n=mn(t,\"x\",\"reverse\");return C(4===n.rank,(function(){return\"Error in reverse4D: x must be rank 4 but got rank \"+n.rank+\".\"})),ol(n,e)}});function cl(t,e,n,r,o,a){var i=mn(t,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),C(4===s.rank,(function(){return\"Error in maxPool: input must be rank 4 but got rank \"+s.rank+\".\"})),C(Mo(n,r),(function(){return\"Error in maxPool: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+r+\"'\"})),null!=a&&C(A(o),(function(){return\"Error in maxPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\"}));var c=Io(s.shape,e,n,r,o,a);if(1===c.filterWidth&&1===c.filterHeight&&S(c.inShape,c.outShape))return i.clone();var l=[s],h=Lt.runKernelFunc((function(t,e){var n=t.maxPool(s,c);return e([s,n]),n}),{x:s},(function(t,a){var i=a[0],s=a[1];return{x:function(){return function(t,e,n,r,o,a,i,s){var u=mn(t,\"dy\",\"maxPoolBackprop\"),c=mn(e,\"input\",\"maxPoolBackprop\"),l=mn(n,\"output\",\"maxPoolBackprop\");C(c.rank===u.rank,(function(){return\"Rank of input (\"+c.rank+\") does not match rank of dy (\"+u.rank+\")\"})),null==a&&(a=[1,1]);C(Mo(o,a),(function(){return\"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides \"+o+\" and dilations '\"+a+\"'\"})),C(4===u.rank,(function(){return\"Error in maxPoolBackprop: dy must be rank 4 but got rank \"+u.rank+\".\"})),C(4===c.rank,(function(){return\"Error in maxPoolBackprop: input must be rank 4 but got rank \"+c.rank+\".\"})),null!=s&&C(A(i),(function(){return\"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode \"+s+\" but got pad \"+i+\".\"}));var h=Io(c.shape,r,o,a,i,s);return Lt.runKernelFunc((function(t){return t.maxPoolBackprop(u,c,l,h)}),{$dy:u,$input:c})}(t,i,s,e,n,r,o)}}}),\"MaxPool\",c,l);return u?h.as3D(h.shape[1],h.shape[2],h.shape[3]):h}function ll(t,e,n,r,o,a){var i=mn(t,\"x\",\"avgPool\",\"float32\");null==r&&(r=[1,1]),C(Mo(n,r),(function(){return\"Error in avgPool: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+r+\"'\"}));var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),C(4===s.rank,(function(){return\"Error in avgPool: x must be rank 4 but got rank \"+s.rank+\".\"})),null!=a&&C(A(o),(function(){return\"Error in avgPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\"}));var c=Io(s.shape,e,n,r,o,a);if(1===c.filterWidth&&1===c.filterHeight&&S(c.inShape,c.outShape))return i.clone();var l=Lt.runKernelFunc((function(t){return t.avgPool(s,c)}),{x:s},(function(t){return{x:function(){return function(t,e,n,r,o,a){var i=mn(t,\"dy\",\"avgPoolBackprop\"),s=mn(e,\"input\",\"avgPoolBackprop\");C(s.rank===i.rank,(function(){return\"Rank of input (\"+s.rank+\") does not match rank of dy (\"+i.rank+\")\"})),null==o&&(o=[1,1]);C(Mo(r,o),(function(){return\"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+o+\"'\"}));var u=s,c=i,l=!1;3===s.rank&&(l=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),c=i.as4D(1,i.shape[0],i.shape[1],i.shape[2]));C(4===c.rank,(function(){return\"Error in avgPoolBackprop: dy must be rank 4 but got rank \"+c.rank+\".\"})),C(4===u.rank,(function(){return\"Error in avgPoolBackprop: input must be rank 4 but got rank \"+u.rank+\".\"}));var h=Io(u.shape,n,r,o,a),f=Lt.runKernelFunc((function(t){return t.avgPoolBackprop(c,u,h)}),{dy4D:c,input4D:u});if(l)return f.as3D(f.shape[1],f.shape[2],f.shape[3]);return f}(t,s,e,n,r,o)}}}),\"AvgPool\",c);return l=l.cast(i.dtype),u?l.as3D(l.shape[1],l.shape[2],l.shape[3]):l}var hl=An({maxPool_:function(t,e,n,r,o){return cl(t,e,n,1,r,o)}}),fl=An({avgPool_:function(t,e,n,r,o){return ll(t,e,n,1,r,o)}}),dl=An({pool_:function(t,e,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r=\"valid\");var i=mn(t,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),C(Mo(a,o),(function(){return\"Error in pool: Either strides or dilations must be 1. Got strides \"+a+\" and dilations '\"+o+\"'\"}));var c,l=Io(s.shape,e,a,o,r),h=[l.dilationHeight,l.dilationWidth];c=\"same\"===r?function(t,e){var n=t.map((function(t,n){return t+(t-1)*(e[n]-1)})).map((function(t){return t-1})),r=n.map((function(t){return Math.floor(t/2)})),o=n.map((function(t,e){return t-r[e]}));return n.map((function(t,e){return[r[e],o[e]]}))}([l.filterHeight,l.filterWidth],h):[[0,0],[0,0]];var f=1===h[0]&&1===h[1],d=function(t,e,n){var r=n.map((function(t){return t[0]})),o=n.map((function(t){return t[1]})),a=t.concat(r,o),i=e.map((function(t,e){return(t-a[e]%t)%t})),s=o.map((function(t,e){return t+i[e]})),u=e.map((function(t,e){return[r[e],s[e]]})),c=e.map((function(t,e){return[0,i[e]]}));return[u,c]}([l.inHeight,l.inWidth],h,c),p=d[0],v=d[1],m=f?r:\"valid\",g=f?s:Mr(s,h,p),y=(\"avg\"===n?function(){return ll(g,e,a,1,m)}:function(){return cl(g,e,a,1,m)})(),x=f?y:vr(y,h,v);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}}),pl=An({maxPool3d_:function(t,e,n,r,o,a,i){void 0===a&&(a=\"NDHWC\");var s=mn(t,\"x\",\"maxPool3d\"),u=s,c=!1;4===s.rank&&(c=!0,u=s.as5D(1,s.shape[0],s.shape[1],s.shape[2],s.shape[3])),null==i&&(i=[1,1,1]),C(5===u.rank,(function(){return\"Error in maxPool3d: x must be rank 5 but got rank \"+u.rank+\".\"})),C(\"NDHWC\"===a,(function(){return\"Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of \"+a})),C(Mo(n,i),(function(){return\"Error in maxPool3d: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+i+\"'\"})),null!=o&&C(A(r),(function(){return\"Error in maxPool3d: pad must be an integer when using, dimRoundingMode \"+o+\" but got pad \"+r+\".\"}));var l=ko(u.shape,e,n,i,r,o,a),h=Lt.runKernelFunc((function(t,e){var n=t.maxPool3d(u,l);return e([u,n]),n}),{x:u},(function(t,a){var s=a[0],u=a[1];return{x:function(){return function(t,e,n,r,o,a,i,s){var u=mn(t,\"dy\",\"maxPool3dBackprop\"),c=mn(e,\"input\",\"maxPool3dBackprop\"),l=mn(n,\"output\",\"maxPool3dBackprop\"),h=u,f=c,d=l,p=!1;4===c.rank&&(p=!0,h=u.as5D(1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]),f=c.as5D(1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]),d=l.as5D(1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]));C(5===h.rank,(function(){return\"Error in maxPool3dBackprop: dy must be rank 5 but got rank \"+h.rank+\".\"})),C(5===f.rank,(function(){return\"Error in maxPool3dBackprop: input must be rank 5 but got rank \"+f.rank+\".\"})),C(5===d.rank,(function(){return\"Error in maxPool3dBackprop: output must be rank 5 but got rank \"+d.rank+\".\"})),null==a&&(a=[1,1,1]);C(Mo(o,a),(function(){return\"Error in maxPool3dBackprop: Either strides or dilations must be 1. 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Array(a.rank-n.length).fill(-1)):n).map((function(t,e){return t>=0?t:(C(-1===t,(function(){return\"Negative size values should be exactly -1 but got \"+t+\" for the slice() size at index \"+e+\".\"})),a.shape[e]-r[e])})),eo(a,r,o);var i=a.shape,s={begin:r,size:o};return Lt.runKernelFunc((function(t){return t.slice(a,r,o)}),{x:a},(function(t){for(var e=[],n=0;n<t.rank;n++)e.push([r[n],i[n]-r[n]-o[n]]);return{x:function(){return t.pad(e)}}}),\"Slice\",s)}}),gl=An({slice1d_:function(t,e,n){var r=mn(t,\"x\",\"slice1d\");return C(1===r.rank,(function(){return\"slice1d expects a rank-1 tensor, but got a rank-\"+r.rank+\" tensor\"})),ml(r,[e],[n])}}),yl=An({slice2d_:function(t,e,n){var r=mn(t,\"x\",\"slice2d\");return C(2===r.rank,(function(){return\"slice2d expects a rank-2 tensor, but got a rank-\"+r.rank+\" tensor\"})),ml(r,e,n)}}),xl=An({slice3d_:function(t,e,n){var r=mn(t,\"x\",\"slice3d\");return C(3===r.rank,(function(){return\"slice3d expects a rank-3 tensor, but got a 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t.any(r,a)}),{$x:r});if(n){var u=wn(s.shape,o);return s.reshape(u)}return s}}),Rl=An({argMax_:function(t,e){void 0===e&&(e=0);var n=mn(t,\"x\",\"argMax\");null==e&&(e=0);var r=O(e,n.shape),o=En(r,n.rank);null!=o&&(n=n.transpose(o),r=In(r.length,n.rank));var a={axis:r[0]},i=[n];return Lt.runKernelFunc((function(t,e){var o=t.argMax(n,r[0]);return e([n]),o}),{x:n},(function(t,e){var n=e[0];return{x:function(){return Xn(n)}}}),\"ArgMax\",a,i)}}),Il=An({argMin_:function(t,e){void 0===e&&(e=0);var n=mn(t,\"x\",\"argMin\");null==e&&(e=0);var r=O(e,n.shape),o=En(r,n.rank);return null!=o&&(n=n.transpose(o),r=In(r.length,n.rank)),Lt.runKernelFunc((function(t,e){var o=t.argMin(n,r[0]);return e([n]),o}),{$x:n},(function(t,e){var n=e[0];return{$x:function(){return Xn(n)}}}))}}),kl=An({logSumExp_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=mn(t,\"x\",\"logSumExp\"),o=O(e,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=wn(s.shape,o);return s.reshape(u)}return s}}),Sl=An({max_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=mn(t,\"x\",\"max\"),o=r,a=O(e,r.shape),i=a,s=En(i,r.rank);null!=s&&(r=r.transpose(s),i=In(i.length,r.rank));var u=[r],c=Lt.runKernelFunc((function(t,e){var n=t.max(r,i);return e([o,n]),n}),{x:r},(function(t,e){return wl(t,e[1],e[0],a,s)}),\"Max\",{axes:i},u,[!0]);if(n){var l=wn(c.shape,a);c=c.reshape(l)}return c}}),Al=An({mean_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=mn(t,\"x\",\"mean\"),o=O(e,r.shape),a=k(bn(r.shape,o)[1]);return vo((function(t){var r=On(a);return{value:(r.dtype===t.dtype?t:t.cast(r.dtype)).div(r).sum(e,n),gradFunc:function(e){var n=t.shape.slice();return o.forEach((function(t){n[t]=1})),e.reshape(n).mul(zn(t.shape,\"float32\")).div(a)}}}))(r)}}),Dl=An({min_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=mn(t,\"x\",\"min\"),o=r,a=O(e,r.shape),i=a,s=En(i,r.rank);null!=s&&(r=r.transpose(s),i=In(i.length,r.rank));var u=[r],c=Lt.runKernelFunc((function(t,e){var n=t.min(r,i);return e([o,n]),n}),{x:r},(function(t,e){return wl(t,e[1],e[0],a,s)}),\"Min\",{axes:i},u,[!0]);if(n){var l=wn(c.shape,a);c=c.reshape(l)}return c}}),Tl=An({moments_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=O(e,(t=mn(t,\"x\",\"moments\")).shape),o=t.mean(r,n),a=o.shape;n||(a=wn(o.shape,r));var i=t.toFloat().sub(o.reshape(a)).square();return{mean:o,variance:i.mean(r,n)}}}),Nl=An({sum_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=mn(t,\"x\",\"sum\");\"bool\"===r.dtype&&(r=r.toInt());var o=O(e,r.shape);return vo((function(t){var e=En(o,t.rank),r=o,a=t;null!=e&&(a=t.transpose(e),r=In(r.length,t.rank));var i=function(e){var n=t.shape.slice();return o.forEach((function(t){n[t]=1})),e.reshape(n).mul(zn(t.shape,\"float32\"))},s={axes:r},u=Lt.runKernelFunc((function(t){return t.sum(a,r)}),{x:a},(function(t){return{x:function(){return i(t)}}}),\"Sum\",s);if(n){var c=wn(u.shape,o);u=u.reshape(c)}return{value:u,gradFunc:i}}))(r)}}),Fl=An({prod_:function(t,e,n){void 0===e&&(e=null),void 0===n&&(n=!1);var r=mn(t,\"x\",\"prod\");\"bool\"===r.dtype&&(r=r.toInt());var o=O(e,r.shape),a=En(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=In(i.length,r.rank));var u=Lt.runKernelFunc((function(t){return t.prod(s,i)}),{permutedX:s});if(n){var c=wn(u.shape,o);u=u.reshape(c)}return u}});var _l=An({elu_:function(t){var e=mn(t,\"x\",\"elu\");return Lt.runKernelFunc((function(t,n){var r=t.elu(e);return n([r]),r}),{$x:e},(function(t,e){var n=e[0];return{$x:function(){return Lt.runKernelFunc((function(e){return e.eluDer(t,n)}),{dy:t,y:n})}}}))}}),Ol=An({leakyRelu_:function(t,e){void 0===e&&(e=.2);var n=mn(t,\"x\",\"leakyRelu\");return hc(On(e).mul(n),n)}}),Ml=An({prelu_:function(t,e){var n=mn(t,\"x\",\"prelu\"),r=mn(e,\"alpha\",\"prelu\");return Lt.runKernelFunc((function(t,e){var o=t.prelu(n,r);return e([n,r]),o}),{x:n,alpha:r},(function(t,e){var n=e[0],r=e[1],o=n.greater(0);return{x:function(){return ec(o,t,t.mul(r))},alpha:function(){var e=ec(o,Xn(t),t.mul(n)),a=Eo(r.shape,t.shape);return a.length>0&&(e=e.sum(a)),e.reshape(r.shape)}}}),\"Prelu\")}}),Bl=An({relu_:function(t){var e=mn(t,\"x\",\"relu\");return\"bool\"===e.dtype?e.toInt():Lt.runKernelFunc((function(t,n){var r=t.relu(e);return n([e]),r}),{x:e},(function(t,e){var n=e[0];return{x:function(){return t.mulStrict(n.step().toFloat())}}}),\"Relu\")}}),Pl=An({relu6_:function(t){var e=mn(t,\"x\",\"relu6\");return\"bool\"===e.dtype?e.toInt():Lt.runKernelFunc((function(t,n){var r=t.relu6(e);return n([e]),r}),{x:e},(function(t,e){var n=e[0],r=n.lessEqual(6).mul(n.step());return{x:function(){return t.mulStrict(r.toFloat())}}}),\"Relu6\")}}),Ll=An({selu_:function(t){var e=mn(t,\"x\",\"selu\");return Lt.runKernelFunc((function(t,n){var r=t.selu(e);return n([e]),r}),{$x:e},(function(t,e){var n=e[0];return{$x:function(){var e=n.greater(On(0)),r=On(bs),o=On(ws),a=t.mul(o),i=t.mul(r).mul(n.toFloat().exp());return ec(e,a,i)}}}))}});var Wl=An({transpose_:function(t,e){var n=mn(t,\"x\",\"transpose\");if(null==e&&(e=n.shape.map((function(t,e){return e})).reverse()),C(n.rank===e.length,(function(){return\"Error in transpose: rank of input \"+n.rank+\" must match length of perm \"+e+\".\"})),e.forEach((function(t){C(t>=0&&t<n.rank,(function(){return\"All entries in 'perm' must be between 0 and \"+(n.rank-1)+\" but got \"+e}))})),n.rank<=1)return n.clone();var r={perm:e};return Lt.runKernelFunc((function(t){return t.transpose(n,e)}),{x:n},(function(t){var n=Rn(e);return{x:function(){return t.transpose(n)}}}),\"Transpose\",r)}});var Ul=An({localResponseNormalization_:function(t,e,n,r,o){void 0===e&&(e=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=mn(t,\"x\",\"localResponseNormalization\");C(4===a.rank||3===a.rank,(function(){return\"Error in localResponseNormalization: x must be rank 3 or 4 but got\\n rank \"+a.rank+\".\"})),C(A(e),(function(){return\"Error in localResponseNormalization: depthRadius must be an integer but got depthRadius \"+e+\".\"}));var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=Lt.runKernelFunc((function(t,a){var s=t.localResponseNormalization4D(i,e,n,r,o);return a([i,s]),s}),{x4D:i},(function(t,a){var i=a[0],s=a[1];return{x4D:function(){return Lt.runKernelFunc((function(a){return a.LRNGrad(t,i,s,e,n,r,o)}),{})}}}));return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}});var Vl=An({norm_:function(t,e,n,r){void 0===e&&(e=\"euclidean\"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=function t(e,n,r){void 0===r&&(r=null);if(0===e.rank)return e.abs();if(1!==e.rank&&null===r)return t(e.reshape([-1]),n,r);if(1===e.rank||\"number\"==typeof r||Array.isArray(r)&&1===r.length){if(1===n)return e.abs().sum(r);if(n===1/0)return e.abs().max(r);if(n===-1/0)return e.abs().min(r);if(\"euclidean\"===n||2===n)return e.abs().pow(On(2,\"int32\")).sum(r).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+n)}if(Array.isArray(r)&&2===r.length){if(1===n)return e.abs().sum(r[0]).max(r[1]-1);if(n===1/0)return e.abs().sum(r[1]).max(r[0]);if(n===-1/0)return e.abs().sum(r[1]).min(r[0]);if(\"fro\"===n||\"euclidean\"===n)return e.square().sum(r).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+n)}throw new Error(\"Error in norm: invalid axis: \"+r)}(t=mn(t,\"x\",\"norm\"),e,n),a=o.shape;if(r){var i=O(n,t.shape);a=wn(o.shape,i)}return o.reshape(a)}});var zl=An({basicLSTMCell_:function(t,e,n,r,o,a){var 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0===n&&(n=!1);var r=mn(t,\"images\",\"resizeNearestNeighbor\");C(3===r.rank||4===r.rank,(function(){return\"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank \"+r.rank+\".\"})),C(2===e.length,(function(){return\"Error in resizeNearestNeighbor: new shape must 2D, but got shape \"+e+\".\"})),C(\"float32\"===r.dtype||\"int32\"===r.dtype,(function(){return\"`images` must have `int32` or `float32` as dtype\"}));var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=e[0],s=e[1],u=Lt.runKernelFunc((function(t,e){return e([o]),t.resizeNearestNeighbor(o,i,s,n)}),{batchImages:o},(function(t,e){return{batchImages:function(){return Lt.runKernelFunc((function(r){return r.resizeNearestNeighborBackprop(t,e[0],n)}),{})}}}));return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}}),Dh=An({nonMaxSuppression_:function(t,e,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=mn(t,\"boxes\",\"nonMaxSuppression\"),i=mn(e,\"scores\",\"nonMaxSuppression\"),s=kh(a,i,n,r,o);n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold;var u={maxOutputSize:n,iouThreshold:r,scoreThreshold:o};return Lt.runKernelFunc((function(t){return t.nonMaxSuppression(a,i,n,r,o)}),{boxes:a,scores:i},null,\"NonMaxSuppressionV3\",u)}}),Th=function(t,e,o,a,i){return void 0===a&&(a=.5),void 0===i&&(i=Number.NEGATIVE_INFINITY),n(this,void 0,void 0,(function(){var n,s,u,c,l,h,f;return r(this,(function(r){switch(r.label){case 0:return n=mn(t,\"boxes\",\"nonMaxSuppressionAsync\"),s=mn(e,\"scores\",\"nonMaxSuppressionAsync\"),u=kh(n,s,o,a,i),o=u.maxOutputSize,a=u.iouThreshold,i=u.scoreThreshold,[4,Promise.all([n.data(),s.data()])];case 1:return c=r.sent(),l=c[0],h=c[1],f=jo(l,h,o,a,i),n!==t&&n.dispose(),s!==e&&s.dispose(),[2,f]}}))}))},Nh=An({nonMaxSuppressionWithScore_:function(t,e,n,r,o,a){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),void 0===a&&(a=0);var i=mn(t,\"boxes\",\"nonMaxSuppression\"),s=mn(e,\"scores\",\"nonMaxSuppression\"),u=kh(i,s,n,r,o,a),c={maxOutputSize:n=u.maxOutputSize,iouThreshold:r=u.iouThreshold,scoreThreshold:o=u.scoreThreshold,softNmsSigma:a=u.softNmsSigma},l=Lt.runKernel(\"NonMaxSuppressionV5\",{boxes:i,scores:s},c);return{selectedIndices:l[0],selectedScores:l[1]}}}),Fh=function(t,e,o,a,i,s){return void 0===a&&(a=.5),void 0===i&&(i=Number.NEGATIVE_INFINITY),void 0===s&&(s=0),n(this,void 0,void 0,(function(){var n,u,c,l,h,f,d;return r(this,(function(r){switch(r.label){case 0:return n=mn(t,\"boxes\",\"nonMaxSuppressionAsync\"),u=mn(e,\"scores\",\"nonMaxSuppressionAsync\"),c=kh(n,u,o,a,i,s),o=c.maxOutputSize,a=c.iouThreshold,i=c.scoreThreshold,s=c.softNmsSigma,[4,Promise.all([n.data(),u.data()])];case 1:return l=r.sent(),h=l[0],f=l[1],d=Xo(h,f,o,a,i,s),n!==t&&n.dispose(),u!==e&&u.dispose(),[2,d]}}))}))},_h=An({cropAndResize_:function(t,e,n,r,o,a){var i=mn(t,\"image\",\"cropAndResize\"),s=mn(e,\"boxes\",\"cropAndResize\",\"float32\"),u=mn(n,\"boxInd\",\"cropAndResize\",\"int32\");o=o||\"bilinear\",a=a||0;var c=s.shape[0];return C(4===i.rank,(function(){return\"Error in cropAndResize: image must be rank 4,but got rank \"+i.rank+\".\"})),C(2===s.rank&&4===s.shape[1],(function(){return\"Error in cropAndResize: boxes must be have size [\"+c+\",4] but had shape \"+s.shape+\".\"})),C(1===u.rank&&u.shape[0]===c,(function(){return\"Error in cropAndResize: boxInd must be have size [\"+c+\"] but had shape \"+s.shape+\".\"})),C(2===r.length,(function(){return\"Error in cropAndResize: cropSize must be of length 2, but got length \"+r.length+\".\"})),C(r[0]>=1&&r[1]>=1,(function(){return\"cropSize must be atleast [1,1], but was \"+r})),C(\"bilinear\"===o||\"nearest\"===o,(function(){return\"method must be bilinear or nearest, but was \"+o})),Lt.runKernelFunc((function(t,e){return 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and transposeB=\"+s+\" must match.\"}));var R,I,A=d.shape.slice(0,-2).concat([g,y]),D=a?d.as3D(w,v,g):d.as3D(w,g,v),T=s?p.as3D(E,y,m):p.as3D(E,m,y);null!=u&&Ro(A,(R=Nt(R=mn(u,\"bias\",\"fused matMul\"),d)[0]).shape),null!=h&&(I=mn(h,\"prelu weights\",\"fused matMul\"));var N={a:D,b:T};null!=u&&(N.bias=R),null!=h&&(N.preluActivationWeights=I);var F=[D,T];return Lt.runKernelFunc((function(t,e){var n=t.fusedBatchMatMul({a:D,b:T,transposeA:a,transposeB:s,bias:R,activation:l,preluActivationWeights:I});return e([D,T,n]),n}),N,(function(t,e){var n=e[0],r=e[1],o=e[2],i=Bh(t,o,l),c={};return null!=u&&(c={bias:function(){return Ph(R,i)}}),a||s?!a&&s?Object.assign({a:function(){return i.matMul(r,!1,!1)},b:function(){return i.matMul(n,!0,!1)}},c):a&&!s?Object.assign({a:function(){return r.matMul(i,!1,!0)},b:function(){return n.matMul(i,!1,!1)}},c):Object.assign({a:function(){return r.matMul(i,!0,!0)},b:function(){return i.matMul(n,!0,!0)}},c):Object.assign({a:function(){return 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\"+o+\".\"})),C(g.shape[3]===m.shape[2],(function(){return\"Error in conv2d: depth of input (\"+g.shape[3]+\") must match input depth for filter \"+m.shape[2]+\".\"})),C(Mo(r,u),(function(){return\"Error in conv2D: Either strides or dilations must be 1. 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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. 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Due to automatic garbage collection, the true allocated memory may be less.\"]}},o.prototype.complex=function(t,e){var n=this.makeOutput(null,t.shape,\"complex64\");return this.data.get(n.dataId).complexTensors={real:Lt.keep(t.clone()),imag:Lt.keep(e.clone())},n},o.prototype.real=function(t){return this.data.get(t.dataId).complexTensors.real.clone()},o.prototype.imag=function(t){return this.data.get(t.dataId).complexTensors.imag.clone()},o.prototype.slice=function(t,e,n){if(Hh(t,\"slice\"),io(t.shape,e,n)){var r=so(e,t.strides),o=k(n);return Fn(this.readSync(t.dataId).subarray(r,r+o),n,t.dtype)}for(var a=dr(n,t.dtype),i=this.bufferSync(t),s=0;s<a.size;++s){var u=a.indexToLoc(s).map((function(t,n){return t+e[n]}));a.values[s]=i.get.apply(i,u)}return a.toTensor()},o.prototype.stridedSlice=function(t,e,n,r){Hh(t,\"stridedSlice\");var o=ro(e,n,r);if(o.some((function(t){return 0===t})))return Fn([],o);for(var a=dr(o,t.dtype),i=this.bufferSync(t),s=0;s<a.size;s++){for(var u=a.indexToLoc(s),c=new Array(u.length),l=0;l<c.length;l++)c[l]=u[l]*r[l]+e[l];a.set.apply(a,[i.get.apply(i,c)].concat(u))}return a.toTensor()},o.prototype.diag=function(t){for(var e=this.readSync(t.dataId),n=dr([t.size,t.size],t.dtype),r=n.values,o=0;o<e.length;o++)r[o*t.size+o]=e[o];return n.toTensor()},o.prototype.unstack=function(t,e){for(var n=t.shape[e],r=new Array(t.rank-1),o=0,a=0;a<t.rank;a++)a!==e&&(r[o++]=t.shape[a]);var i=new Array(t.rank).fill(0),s=t.shape.slice();s[e]=1;var u=new Array(n);for(a=0;a<u.length;a++)i[e]=a,u[a]=this.slice(t,i,s).reshape(r);return u},o.prototype.reverse=function(t,e){Hh(t,\"reverse\");for(var n=dr(t.shape,t.dtype),r=this.bufferSync(t),o=function(o){var a=n.indexToLoc(o),i=a.slice();e.forEach((function(e){return i[e]=t.shape[e]-1-i[e]})),n.set.apply(n,[r.get.apply(r,i)].concat(a))},a=0;a<n.size;a++)o(a);return n.toTensor()},o.prototype.concat=function(t,e){var n=this;if(\"complex64\"===t[0].dtype){var r=t.map((function(t){return Tn(t)})),o=t.map((function(t){return Nn(t)}));return Dn(this.concat(r,e),this.concat(o,e))}var a=t.map((function(t){var n=k(t.shape.slice(e));return t.as2D(-1,n)})),i=Sn(a.map((function(t){return t.shape})),1),s=dr(i,t[0].dtype).values;if(1===a[0].shape[0]){var u=0;a.forEach((function(t){s.set(n.readSync(t.dataId),u),u+=t.size}))}else{var c=0;a.forEach((function(t){for(var e=n.readSync(t.dataId),r=0,o=0;o<t.shape[0];++o)for(var a=o*i[1]+c,u=0;u<t.shape[1];++u)s[a+u]=e[r++];c+=t.shape[1]}))}var l=Sn(t.map((function(t){return t.shape})),e);return Fn(s,l,t[0].dtype)},o.prototype.neg=function(t){return Hh(t,\"neg\"),this.multiply(On(-1),t)},o.prototype.add=function(t,e){return\"complex64\"===t.dtype||\"complex64\"===e.dtype?this.broadcastedBinaryComplexOp(t.cast(\"complex64\"),e.cast(\"complex64\"),(function(t,e,n,r){return{real:t+n,imag:e+r}})):this.broadcastedBinaryOp(t,e,Dt(t.dtype,e.dtype),(function(t,e){return t+e}))},o.prototype.addN=function(t){var e=this;Hh(t,\"addN\");for(var n=t.map((function(t){return e.readSync(t.dataId)})),r=dr(t[0].shape,t[0].dtype),o=r.values,a=0;a<t.length;a++)for(var i=n[a],s=0;s<o.length;s++)o[s]+=i[s];return r.toTensor()},o.prototype.softmax=function(t,e){var n=O([e],t.shape),r=this.max(t,n),o=wn(r.shape,n),a=this.subtract(t,r.reshape(o)),i=this.exp(a),s=this.sum(i,n).reshape(o);return this.realDivide(i,s)},o.prototype.subtract=function(t,e){return\"complex64\"===t.dtype||\"complex64\"===e.dtype?this.broadcastedBinaryComplexOp(t.cast(\"complex64\"),e.cast(\"complex64\"),(function(t,e,n,r){return{real:t-n,imag:e-r}})):this.broadcastedBinaryOp(t,e,Dt(t.dtype,e.dtype),(function(t,e){return t-e}))},o.prototype.pow=function(t,e){return Hh([t,e],\"pow\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){return Math.pow(t,e)}))},o.prototype.batchMatMul=function(t,e,n,r){Hh([t,e],\"matMul\");for(var o=n?t.shape[1]:t.shape[2],a=n?t.shape[2]:t.shape[1],i=r?e.shape[1]:e.shape[2],s=t.shape[0],u=this.readSync(t.dataId),c=this.readSync(e.dataId),l=n?[t.strides[0],1,t.strides[1]]:[t.strides[0],t.strides[1],1],h=l[0],f=l[1],d=l[2],p=r?[1,e.strides[1],e.strides[0]]:[e.strides[1],1,e.strides[0]],v=p[0],m=p[1],g=p[2],y=a*i,x=dr([s,a,i],t.dtype),b=x.values,w=this.blockSize,C=0;C<s;C++)for(var E=0;E<a;E+=w)for(var R=0;R<i;R+=w)for(var I=0;I<o;I+=w)for(var k=Math.min(E+w,a),S=Math.min(R+w,i),A=Math.min(I+w,o),D=E;D<k;D++)for(var T=R;T<S;T++){for(var N=0,F=I;F<A;F++)N+=u[C*h+D*f+F*d]*c[F*v+T*m+C*g];b[C*y+(D*i+T)]+=N}return x.toTensor()},o.prototype.fusedBatchMatMul=function(t){var e=t.a,n=t.b,r=t.transposeA,o=t.transposeB,a=t.bias,i=t.activation,s=t.preluActivationWeights,u=this.batchMatMul(e,n,r,o);return a&&(u=this.add(u,a)),i&&(u=qh(this,u,i,s)),u},o.prototype.multiply=function(t,e){return\"complex64\"===t.dtype||\"complex64\"===e.dtype?this.broadcastedBinaryComplexOp(t.cast(\"complex64\"),e.cast(\"complex64\"),(function(t,e,n,r){return{real:t*n-e*r,imag:t*r+e*n}})):this.broadcastedBinaryOp(t,e,Dt(t.dtype,e.dtype),(function(t,e){return t*e}))},o.prototype.realDivide=function(t,e){Hh([t,e],\"realDivide\");return this.broadcastedBinaryOp(t,e,\"float32\",(function(t,e){return t/e}))},o.prototype.floorDiv=function(t,e){Hh([t,e],\"floorDiv\");return this.broadcastedBinaryOp(t,e,\"int32\",(function(t,e){return Math.floor(t/e)}))},o.prototype.sum=function(t,e){Hh(t,\"sum\"),Cn(\"sum\",e,t.rank);for(var n=bn(t.shape,e),r=n[0],o=n[1],a=Gn(r,Dt(t.dtype,\"int32\")),i=k(o),s=this.readSync(a.dataId),u=this.readSync(t.dataId),c=0;c<s.length;++c){for(var l=c*i,h=0,f=0;f<i;++f)h+=u[l+f];s[c]=h}return a},o.prototype.prod=function(t,e){Hh(t,\"sum\");for(var n=bn(t.shape,e),r=n[0],o=n[1],a=Gn(r,Dt(t.dtype,\"int32\")),i=k(o),s=this.readSync(a.dataId),u=this.readSync(t.dataId),c=0;c<s.length;++c){for(var l=c*i,h=1,f=0;f<i;++f)h*=u[l+f];s[c]=h}return a},o.prototype.unsortedSegmentSum=function(t,e,n){Hh(t,\"unsortedSegmentSum\");for(var r=[],o=t.rank-e.rank,a=0;a<o;++a)e=e.expandDims(a+1);for(a=0;a<n;++a){var i=On(a,\"int32\"),s=Rc(i,e).asType(\"float32\").mul(t).sum(0);r.push(s)}return Pr(r)},o.prototype.argMin=function(t,e){Hh(t,\"argMin\");var n=[e];Cn(\"argMin\",n,t.rank);for(var r=bn(t.shape,n),o=r[0],a=r[1],i=Gn(o,\"int32\"),s=k(a),u=this.readSync(i.dataId),c=this.readSync(t.dataId),l=0;l<u.length;++l){for(var h=l*s,f=c[h],d=0,p=0;p<s;++p){var v=c[h+p];v<f&&(f=v,d=p)}u[l]=d}return i},o.prototype.argMax=function(t,e){Hh(t,\"argMax\");var n=[e];Cn(\"argMax\",n,t.rank);for(var r=bn(t.shape,n),o=r[0],a=r[1],i=Gn(o,\"int32\"),s=k(a),u=this.readSync(i.dataId),c=this.readSync(t.dataId),l=0;l<u.length;++l){for(var h=l*s,f=c[h],d=0,p=0;p<s;++p){var v=c[h+p];v>f&&(f=v,d=p)}u[l]=d}return i},o.prototype.cumsum=function(t,e,n,r){if(Hh(t,\"cumsum\"),e!==t.rank-1)throw new Error(\"backend.cumsum in CPU expects an inner-most axis=\"+(t.rank-1)+\" but got axis=\"+e);for(var o=Dt(t.dtype,\"int32\"),a=Gn(t.shape,o),i=this.readSync(a.dataId),s=this.readSync(t.dataId),u=t.shape[t.rank-1],c=r?function(t,e){return t+u-e-1}:function(t,e){return t+e},l=0;l<s.length;l+=u)for(var h=0;h<u;h++){var f=c(l,h);if(0===h)i[f]=n?0:s[f];else{var d=c(l,h-1);i[f]=n?s[d]+i[d]:s[f]+i[d]}}return a},o.prototype.equal=function(t,e){return Hh([t,e],\"equal\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t===e?1:0}))},o.prototype.notEqual=function(t,e){return Hh([t,e],\"notEqual\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t!==e?1:0}))},o.prototype.less=function(t,e){return Hh([t,e],\"less\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t<e?1:0}))},o.prototype.lessEqual=function(t,e){return Hh([t,e],\"lessEqual\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t<=e?1:0}))},o.prototype.greater=function(t,e){return Hh([t,e],\"greater\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t>e?1:0}))},o.prototype.greaterEqual=function(t,e){return Hh([t,e],\"greaterEqual\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t>=e?1:0}))},o.prototype.logicalNot=function(t){Hh(t,\"logicalNot\");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)n[r]=e[r]?0:1;return this.makeOutput(n,t.shape,\"bool\")},o.prototype.logicalAnd=function(t,e){return Hh([t,e],\"logicalAnd\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t&&e}))},o.prototype.logicalOr=function(t,e){return Hh([t,e],\"logicalOr\"),this.broadcastedBinaryOp(t,e,\"bool\",(function(t,e){return t||e}))},o.prototype.select=function(t,e,n){Hh([t,e,n],\"select\");for(var r=this.readSync(t.dataId),o=this.readSync(e.dataId),a=this.readSync(n.dataId),i=Gn(e.shape,Dt(e.dtype,n.dtype)),s=this.readSync(i.dataId),u=0,c=0===t.rank||t.rank>1||1===e.rank?1:k(e.shape.slice(1)),l=0;l<r.length;l++)for(var h=0;h<c;h++)1===r[l]?s[u++]=o[l]:s[u++]=a[l];return i},o.prototype.where=function(t){Hh([t],\"where\");var e=this.readSync(t.dataId);return na(t.shape,e)},o.prototype.topk=function(t,e,n){return Hh(t,\"topk\"),ea(this.readSync(t.dataId),t.shape,t.dtype,e)},o.prototype.min=function(t,e){Hh(t,\"min\"),Cn(\"min\",e,t.rank);for(var n=bn(t.shape,e),r=n[0],o=n[1],a=Gn(r,t.dtype),i=k(o),s=this.readSync(a.dataId),u=this.readSync(t.dataId),c=0;c<s.length;++c){for(var l=c*i,h=u[l],f=0;f<i;++f){var d=u[l+f];d<h&&(h=d)}s[c]=h}return a},o.prototype.minimum=function(t,e){return Hh([t,e],\"minimum\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){return Math.min(t,e)}))},o.prototype.mod=function(t,e){return Hh([t,e],\"mod\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){var n=t%e;return t<0&&e<0||t>=0&&e>=0?n:(n+e)%e}))},o.prototype.max=function(t,e){Hh(t,\"max\"),Cn(\"max\",e,t.rank);for(var n=bn(t.shape,e),r=n[0],o=n[1],a=Gn(r,t.dtype),i=k(o),s=this.readSync(a.dataId),u=this.readSync(t.dataId),c=0;c<s.length;++c){for(var l=c*i,h=u[l],f=0;f<i;++f){var d=u[l+f];d>h&&(h=d)}s[c]=h}return a},o.prototype.maximum=function(t,e){return Hh([t,e],\"maximum\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){return Math.max(t,e)}))},o.prototype.all=function(t,e){Hh(t,\"all\"),Cn(\"all\",e,t.rank);for(var n=bn(t.shape,e),r=n[0],o=n[1],a=Gn(r,t.dtype),i=k(o),s=this.readSync(a.dataId),u=this.readSync(t.dataId),c=0;c<s.length;++c){for(var l=c*i,h=u[l],f=0;f<i;++f){var d=u[l+f];h=h&&d}s[c]=h}return a},o.prototype.any=function(t,e){Hh(t,\"any\"),Cn(\"any\",e,t.rank);for(var n=bn(t.shape,e),r=n[0],o=n[1],a=Gn(r,t.dtype),i=k(o),s=this.readSync(a.dataId),u=this.readSync(t.dataId),c=0;c<s.length;++c){for(var l=c*i,h=u[l],f=0;f<i;++f){var d=u[l+f];h=h||d}s[c]=h}return a},o.prototype.squaredDifference=function(t,e){return Hh([t,e],\"squaredDifference\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){var n=t-e;return n*n}))},o.prototype.ceil=function(t){Hh(t,\"ceil\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.ceil(e[r]);return this.makeOutput(n,t.shape,\"float32\")},o.prototype.floor=function(t){Hh(t,\"floor\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.floor(e[r]);return this.makeOutput(n,t.shape,\"float32\")},o.prototype.sign=function(t){Hh(t,\"x\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)e[r]<0?n[r]=-1:e[r]>0?n[r]=1:n[r]=0;return this.makeOutput(n,t.shape,\"float32\")},o.prototype.isNaN=function(t){Hh(t,\"x\");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)Number.isNaN(e[r])&&(n[r]=1);return this.makeOutput(n,t.shape,\"bool\")},o.prototype.isInf=function(t){Hh(t,\"x\");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)Math.abs(e[r])===1/0&&(n[r]=1);return this.makeOutput(n,t.shape,\"bool\")},o.prototype.isFinite=function(t){Hh(t,\"x\");for(var e=this.readSync(t.dataId),n=new Uint8Array(e.length),r=0;r<e.length;++r)Number.isFinite(e[r])&&(n[r]=1);return this.makeOutput(n,t.shape,\"bool\")},o.prototype.round=function(t){Hh(t,\"round\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var o=Math.floor(e[r]);e[r]-o<.5?n[r]=Math.floor(e[r]):e[r]-o>.5?n[r]=Math.ceil(e[r]):n[r]=o%2==0?o:o+1}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.exp=function(t){Hh(t,\"exp\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.exp(e[r]);return this.makeOutput(n,t.shape,\"float32\")},o.prototype.expm1=function(t){Hh(t,\"expm1\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=Math.expm1(e[r]);return this.makeOutput(n,t.shape,\"float32\")},o.prototype.log=function(t){Hh(t,\"log\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var o=e[r];n[r]=Math.log(o)}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.log1p=function(t){Hh(t,\"log1p\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var o=e[r];n[r]=Math.log1p(o)}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.sqrt=function(t){Hh(t,\"sqrt\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var o=e[r];n[r]=Math.sqrt(o)}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.rsqrt=function(t){Hh(t,\"rsqrt\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r){var o=e[r];n[r]=1/Math.sqrt(o)}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.reciprocal=function(t){Hh(t,\"reciprocal\");for(var e=this.readSync(t.dataId),n=new Float32Array(e.length),r=0;r<e.length;++r)n[r]=1/e[r];return this.makeOutput(n,t.shape,\"float32\")},o.prototype.linear=function(t){return t},o.prototype.relu=function(t){Hh(t,\"relu\");for(var e=Gn(t.shape,t.dtype),n=this.readSync(e.dataId),r=this.readSync(t.dataId),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return e},o.prototype.relu6=function(t){Hh(t,\"relu\");for(var e=Gn(t.shape,t.dtype),n=this.readSync(e.dataId),r=this.readSync(t.dataId),o=0;o<r.length;++o)n[o]=Math.min(Math.max(0,r[o]),6);return e},o.prototype.prelu=function(t,e){return Hh([t,e],\"prelu\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){return t<0?e*t:t}))},o.prototype.elu=function(t){Hh(t,\"elu\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r){var o=n[r];e[r]=o>=0?o:Math.exp(o)-1}return this.makeOutput(e,t.shape,\"float32\")},o.prototype.eluDer=function(t,e){Hh([t,e],\"eluDer\");for(var n=new Float32Array(e.size),r=this.readSync(e.dataId),o=this.readSync(t.dataId),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1)}return this.makeOutput(n,e.shape,\"float32\")},o.prototype.selu=function(t){Hh(t,\"selu\");for(var e=bs,n=ws,r=new Float32Array(t.size),o=this.readSync(t.dataId),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:e*(Math.exp(i)-1)}return this.makeOutput(r,t.shape,\"float32\")},o.prototype.clip=function(t,e,n){Hh(t,\"clip\");for(var r=new Float32Array(t.size),o=this.readSync(t.dataId),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<e?e:i}return this.makeOutput(r,t.shape,\"float32\")},o.prototype.abs=function(t){for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.abs(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.complexAbs=function(t){for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<t.size;++r){var o=n[2*r],a=n[2*r+1];e[r]=Math.hypot(o,a)}return this.makeOutput(e,t.shape,\"float32\")},o.prototype.int=function(t){Hh(t,\"int\");for(var e=new Int32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=n[r];return this.makeOutput(e,t.shape,\"int32\")},o.prototype.sigmoid=function(t){Hh(t,\"sigmoid\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=1/(1+Math.exp(-n[r]));return this.makeOutput(e,t.shape,\"float32\")},o.prototype.softplus=function(t){Hh(t,\"softplus\");for(var e=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(t.size),r=this.readSync(t.dataId),o=0;o<r.length;++o){var a=r[o]>-e,i=r[o]<e,s=Math.exp(r[o]),u=void 0;u=i?s:a?r[o]:Math.log(1+s),n[o]=u}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.sin=function(t){Hh(t,\"sin\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.sin(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.cos=function(t){Hh(t,\"cos\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.cos(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.tan=function(t){Hh(t,\"tan\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.tan(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.asin=function(t){Hh(t,\"asin\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.asin(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.acos=function(t){Hh(t,\"acos\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.acos(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.atan=function(t){Hh(t,\"atan\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.atan(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.atan2=function(t,e){return Hh([t,e],\"atan2\"),this.broadcastedBinaryOp(t,e,t.dtype,(function(t,e){return Math.atan2(t,e)}))},o.prototype.sinh=function(t){Hh(t,\"sinh\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.sinh(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.cosh=function(t){Hh(t,\"cosh\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.cosh(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.tanh=function(t){Hh(t,\"tanh\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=D(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.asinh=function(t){Hh(t,\"asinh\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.asinh(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.acosh=function(t){Hh(t,\"acosh\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.acosh(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.atanh=function(t){Hh(t,\"atanh\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r)e[r]=Math.atanh(n[r]);return this.makeOutput(e,t.shape,\"float32\")},o.prototype.erf=function(t){Hh(t,\"erf\");for(var e=new Float32Array(t.size),n=this.readSync(t.dataId),r=0;r<n.length;++r){var o=Math.sign(n[r]),a=Math.abs(n[r]),i=1/(1+.3275911*a);e[r]=o*(1-((((1.061405429*i-1.453152027)*i+1.421413741)*i-.284496736)*i+.254829592)*i*Math.exp(-a*a))}return this.makeOutput(e,t.shape,\"float32\")},o.prototype.step=function(t,e){void 0===e&&(e=0),Hh(t,\"step\");for(var n=new Float32Array(t.size),r=this.readSync(t.dataId),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:e}return this.makeOutput(n,t.shape,\"float32\")},o.prototype.fusedConv2d=function(t){var e=t.input,n=t.filter,r=t.convInfo,o=t.bias,a=t.activation,i=t.preluActivationWeights,s=this.conv2d(e,n,r);return o&&(s=this.add(s,o)),a&&(s=qh(this,s,a,i)),s},o.prototype.conv2d=function(t,e,n){Hh([t,e],\"conv2d\");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=\"channelsLast\"===n.dataFormat,l=dr(n.outShape,t.dtype),h=t.strides[0],f=c?t.strides[1]:t.strides[2],d=c?t.strides[2]:1,p=c?1:t.strides[1],v=l.strides[0],m=c?l.strides[1]:l.strides[2],g=c?l.strides[2]:1,y=c?1:l.strides[1],x=this.readSync(t.dataId),b=this.readSync(e.dataId),w=l.values,C=0;C<n.batchSize;++C)for(var E=C*h,R=C*v,I=0;I<n.outHeight;++I)for(var k=R+I*m,S=I*n.strideHeight-u,A=0;A<r;A++){var D=S+A*a;if(!(D<0||D>=n.inHeight))for(var T=A*e.strides[0],N=E+D*f,F=0;F<n.outWidth;++F)for(var _=k+F*g,O=F*n.strideWidth-s,M=0;M<o;M++){var B=O+M*i;if(!(B<0||B>=n.inWidth))for(var P=N+B*d,L=T+M*e.strides[1],W=0;W<n.inChannels;++W){for(var U=x[P+W*p],V=0;V<n.outChannels;++V)w[_+V*y]+=U*b[L+V];L+=n.outChannels}}}return l.toTensor()},o.prototype.conv3d=function(t,e,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,c=n.padInfo.front,l=n.padInfo.left,h=n.padInfo.top,f=dr(n.outShape,t.dtype),d=this.readSync(t.dataId),p=this.readSync(e.dataId),v=f.values,m=0;m<n.batchSize;++m)for(var g=m*t.strides[0],y=m*f.strides[0],x=0;x<n.outDepth;++x)for(var b=y+x*f.strides[1],w=x*n.strideDepth-c,C=0;C<r;C++){var E=w+C*i;if(!(E<0||E>=n.inDepth))for(var R=C*e.strides[0],I=g+E*t.strides[1],k=0;k<n.outHeight;++k)for(var S=b+k*f.strides[2],A=k*n.strideHeight-h,D=0;D<o;D++){var T=A+D*s;if(!(T<0||T>=n.inHeight))for(var N=R+D*e.strides[1],F=I+T*t.strides[2],_=0;_<n.outWidth;++_)for(var O=S+_*n.outChannels,M=_*n.strideWidth-l,B=0;B<a;B++){var P=M+B*u;if(!(P<0||P>=n.inWidth))for(var L=N+B*e.strides[2],W=F+P*n.inChannels,U=L,V=0;V<n.inChannels;++V){for(var z=d[W+V],G=0;G<n.outChannels;++G)v[O+G]+=z*p[U+G];U+=n.outChannels}}}}return f.toTensor()},o.prototype.conv2dDerInput=function(t,e,n){Hh([t,e],\"conv2dDerInput\");for(var r=dr(n.inShape,\"float32\"),o=r.values,a=this.readSync(t.dataId),i=this.readSync(e.dataId),s=e.strides,u=s[0],c=s[1],l=s[2],h=n.batchSize,f=n.filterHeight,d=n.filterWidth,p=n.inChannels,v=n.inHeight,m=n.inWidth,g=n.outChannels,y=n.outHeight,x=n.outWidth,b=n.strideHeight,w=n.strideWidth,C=n.dataFormat,E=f-1-n.padInfo.top,R=d-1-n.padInfo.left,I=\"channelsLast\"===C,k=r.strides[0],S=I?r.strides[1]:r.strides[2],A=I?r.strides[2]:1,D=I?1:r.strides[1],T=t.strides[0],N=I?t.strides[1]:t.strides[2],F=I?t.strides[2]:1,_=I?1:t.strides[1],O=0;O<h;++O)for(var M=0;M<p;++M)for(var B=0;B<v;++B)for(var P=B-E,L=Math.max(0,Math.ceil(P/b)),W=Math.min(y,(f+P)/b),U=0;U<m;++U){for(var V=U-R,z=Math.max(0,Math.ceil(V/w)),G=Math.min(x,(d+V)/w),H=0,q=L;q<W;++q)for(var K=q*b-P,j=z;j<G;++j)for(var X=T*O+N*q+F*j,Y=u*(f-1-K)+c*(d-1-(j*w-V))+l*M,$=0;$<g;++$){H+=a[X+_*$]*i[Y+$]}o[k*O+S*B+A*U+D*M]=H}return r.toTensor()},o.prototype.conv3dDerInput=function(t,e,n){for(var r=dr(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],c=a[3],l=this.readSync(t.dataId),h=t.strides,f=h[0],d=h[1],p=h[2],v=h[3],m=this.readSync(e.dataId),g=e.strides,y=g[0],x=g[1],b=g[2],w=g[3],C=n.batchSize,E=n.filterDepth,R=n.filterHeight,I=n.filterWidth,k=n.inChannels,S=n.inDepth,A=n.inHeight,D=n.inWidth,T=n.outChannels,N=n.outDepth,F=n.outHeight,_=n.outWidth,O=n.strideDepth,M=n.strideHeight,B=n.strideWidth,P=E-1-n.padInfo.front,L=R-1-n.padInfo.top,W=I-1-n.padInfo.left,U=0;U<C;++U)for(var V=0;V<k;++V)for(var z=0;z<S;++z)for(var G=z-P,H=Math.max(0,Math.ceil(G/O)),q=Math.min(N,(E+G)/O),K=0;K<A;++K)for(var j=K-L,X=Math.max(0,Math.ceil(j/M)),Y=Math.min(F,(R+j)/M),$=0;$<D;++$){for(var Q=$-W,J=Math.max(0,Math.ceil(Q/B)),Z=Math.min(_,(I+Q)/B),tt=0,et=H;et<q;++et)for(var nt=et*O-G,rt=X;rt<Y;++rt)for(var ot=rt*M-j,at=J;at<Z;++at)for(var it=f*U+d*et+p*rt+v*at,st=y*(E-1-nt)+x*(R-1-ot)+b*(I-1-(at*B-Q))+w*V,ut=0;ut<T;++ut){tt+=l[it+ut]*m[st+ut]}o[i*U+s*z+u*K+c*$+V]=tt}return r.toTensor()},o.prototype.conv2dDerFilter=function(t,e,n){Hh([t,e],\"conv2dDerFilter\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=\"channelsLast\"===n.dataFormat,u=dr(n.filterShape,\"float32\"),c=n.padInfo.left,l=n.padInfo.top,h=this.bufferSync(t),f=this.bufferSync(e),d=0;d<a;++d)for(var p=Math.max(0,Math.ceil((l-d)/r)),v=Math.min(n.outHeight,(n.inHeight+l-d)/r),m=0;m<i;++m)for(var g=Math.max(0,Math.ceil((c-m)/o)),y=Math.min(n.outWidth,(n.inWidth+c-m)/o),x=0;x<n.inChannels;++x)for(var b=0;b<n.outChannels;++b){for(var w=0,C=0;C<n.batchSize;++C)for(var E=p;E<v;++E)for(var R=d+E*r-l,I=g;I<y;++I){var k=m+I*o-c;w+=s?h.get(C,R,k,x)*f.get(C,E,I,b):h.get(C,x,R,k)*f.get(C,b,E,I)}u.set(w,d,m,x,b)}return u.toTensor()},o.prototype.conv3dDerFilter=function(t,e,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,c=dr(n.filterShape,\"float32\"),l=c.values,h=c.strides,f=h[0],d=h[1],p=h[2],v=h[3],m=this.readSync(e.dataId),g=e.strides,y=g[0],x=g[1],b=g[2],w=g[3],C=this.readSync(t.dataId),E=t.strides,R=E[0],I=E[1],k=E[2],S=E[3],A=n.padInfo.front,D=n.padInfo.left,T=n.padInfo.top,N=0;N<i;++N)for(var F=Math.max(0,Math.ceil((A-N)/r)),_=Math.min(n.outDepth,(n.inDepth+A-N)/r),O=N*f,M=0;M<s;++M)for(var B=Math.max(0,Math.ceil((T-M)/o)),P=Math.min(n.outHeight,(n.inHeight+T-M)/o),L=M*d+O,W=0;W<u;++W)for(var U=Math.max(0,Math.ceil((D-W)/a)),V=Math.min(n.outWidth,(n.inWidth+D-W)/a),z=W*p+L,G=0;G<n.inChannels;++G)for(var H=G*v+z,q=0;q<n.outChannels;++q){for(var K=0,j=0;j<n.batchSize;++j)for(var X=j*R,Y=j*y,$=F;$<_;++$)for(var Q=(N+$*r-A)*I+X,J=$*x+Y,Z=B;Z<P;++Z)for(var tt=(M+Z*o-T)*k+Q,et=Z*b+J,nt=U;nt<V;++nt){var rt=nt*w+et;K+=C[(W+nt*a-D)*S+tt+G]*m[rt+q]}l[H+q]=K}return c.toTensor()},o.prototype.fusedDepthwiseConv2D=function(t){var e=t.input,n=t.filter,r=t.convInfo,o=t.bias,a=t.activation,i=t.preluActivationWeights,s=this.depthwiseConv2D(e,n,r);return o&&(s=this.add(s,o)),a&&(s=qh(this,s,a,i)),s},o.prototype.depthwiseConv2D=function(t,e,n){Hh([t,e],\"depthwiseConv2D\");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=n.outChannels/n.inChannels,l=dr(n.outShape,t.dtype),h=this.readSync(t.dataId),f=this.readSync(e.dataId),d=l.values,p=0;p<n.batchSize;++p)for(var v=p*t.strides[0],m=p*l.strides[0],g=0;g<n.outHeight;++g)for(var y=m+g*l.strides[1],x=g*n.strideHeight-s,b=0;b<r;++b){var w=x+b*a;if(!(w<0||w>=n.inHeight))for(var C=b*e.strides[0],E=v+w*t.strides[1],R=0;R<n.outWidth;++R)for(var I=y+R*l.strides[2],k=R*n.strideWidth-u,S=0;S<o;++S){var A=k+S*i;if(!(A<0||A>=n.inWidth))for(var D=C+S*e.strides[1],T=E+A*n.inChannels,N=I,F=D,_=0;_<n.inChannels;++_){for(var O=h[T+_],M=0;M<c;++M)d[N+M]+=O*f[F+M];N+=c,F+=c}}}return l.toTensor()},o.prototype.depthwiseConv2DDerInput=function(t,e,n){Hh([t,e],\"depthwiseConv2DDerInput\");for(var r=dr(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],c=this.readSync(t.dataId),l=t.strides,h=l[0],f=l[1],d=l[2],p=this.readSync(e.dataId),v=e.strides,m=v[0],g=v[1],y=v[2],x=n.batchSize,b=n.filterHeight,w=n.filterWidth,C=n.inChannels,E=n.inHeight,R=n.inWidth,I=n.outChannels,k=n.outHeight,S=n.outWidth,A=n.strideHeight,D=n.strideWidth,T=b-1-n.padInfo.top,N=w-1-n.padInfo.left,F=I/C,_=0;_<x;++_)for(var O=0;O<C;++O)for(var M=0;M<E;++M)for(var B=M-T,P=Math.max(0,Math.ceil(B/A)),L=Math.min(k,(b+B)/A),W=0;W<R;++W){for(var U=W-N,V=Math.max(0,Math.ceil(U/D)),z=Math.min(S,(w+U)/D),G=0,H=P;H<L;++H)for(var q=H*A-B,K=V;K<z;++K)for(var j=h*_+f*H+d*K,X=m*(b-1-q)+g*(w-1-(K*D-U))+y*O,Y=0;Y<F;++Y){G+=c[j+(O*F+Y)]*p[X+Y]}o[i*_+s*M+u*W+O]=G}return r.toTensor()},o.prototype.depthwiseConv2DDerFilter=function(t,e,n){Hh([t,e],\"depthwiseConv2DDerFilter\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=dr(n.filterShape,\"float32\"),u=n.padInfo.left,c=n.padInfo.top,l=n.outChannels/n.inChannels,h=this.bufferSync(t),f=this.bufferSync(e),d=0;d<a;++d)for(var p=Math.max(0,Math.ceil((c-d)/r)),v=Math.min(n.outHeight,(n.inHeight+c-d)/r),m=0;m<i;++m)for(var g=Math.max(0,Math.ceil((u-m)/o)),y=Math.min(n.outWidth,(n.inWidth+u-m)/o),x=0;x<n.outChannels;++x){for(var b=Math.trunc(x/l),w=x%l,C=0,E=0;E<n.batchSize;++E)for(var R=p;R<v;++R)for(var I=d+R*r-c,k=g;k<y;++k){var S=m+k*o-u;C+=h.get(E,I,S,b)*f.get(E,R,k,x)}s.set(C,d,m,b,w)}return s.toTensor()},o.prototype.tile=function(t,e){return Hh(t,\"tile\"),ta(this.bufferSync(t),e)},o.prototype.pad=function(t,e,n){Hh(t,\"pad\");var r=e.map((function(e,n){return e[0]+t.shape[n]+e[1]})),o=e.map((function(t){return t[0]})),a=this.bufferSync(t),i=dr(r,t.dtype);0!==n&&i.values.fill(n);for(var s=0;s<t.size;s++){var u=a.indexToLoc(s),c=u.map((function(t,e){return t+o[e]}));i.set.apply(i,[a.get.apply(a,u)].concat(c))}return i.toTensor()},o.prototype.transpose=function(t,e){Hh(t,\"transpose\");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[e[r]];var o=this.readSync(t.dataId),a=dr(n,t.dtype),i=this.bufferSync(t);for(r=0;r<t.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),c=0;c<u.length;c++)u[c]=s[e[c]];var l=a.locToIndex(u);a.values[l]=o[r]}return a.toTensor()},o.prototype.gather=function(t,e,n){Hh([t,e],\"gather\");var r=t.shape.slice(),o=this.readSync(e.dataId);r[n]=o.length;for(var a=dr(r,t.dtype),i=this.bufferSync(t),s=0;s<a.size;++s){var u=a.indexToLoc(s),c=u.slice();c[n]=o[u[n]];var l=i.locToIndex(c);a.values[s]=i.values[l]}return a.toTensor()},o.prototype.batchToSpaceND=function(t,e,n){Hh([t],\"batchToSpaceND\");var r=e.reduce((function(t,e){return t*e})),o=zr(t.shape,e,r),a=Gr(o.length,e.length),i=Hr(t.shape,e,r),s=qr(n,e.length),u=Kr(i,n,e.length);return t.reshape(o).transpose(a).reshape(i).slice(s,u)},o.prototype.spaceToBatchND=function(t,e,n){Hh([t],\"spaceToBatchND\");var r=e.reduce((function(t,e){return t*e})),o=[[0,0]];o.push.apply(o,n);for(var a=1+e.length;a<t.shape.length;++a)o.push([0,0]);var i=t.pad(o),s=zr(i.shape,e,r,!1),u=Gr(s.length,e.length,!1),c=Hr(i.shape,e,r,!1);return i.reshape(s).transpose(u).reshape(c)},o.prototype.pool=function(t,e,n){Hh(t,\"pool\");for(var r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h=\"max\"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,f=this.readSync(t.dataId),d=dr(e.outShape,t.dtype),p=d.values,v=e.outShape[1]*e.outShape[2]*e.outShape[3],m=e.outShape[2]*e.outShape[3],g=e.outShape[3],y=0;y<e.batchSize;++y)for(var x=y*v,b=y*t.strides[0],w=0;w<e.inChannels;++w)for(var C=0;C<e.outHeight;++C)for(var E=C*r-c,R=Math.max(0,E),I=Math.min(e.inHeight,s+E),k=x+C*m,S=0;S<e.outWidth;++S){for(var A=S*o-l,D=Math.max(0,A),T=Math.min(e.inWidth,u+A),N=h,F=0,_=0,O=R;O<I;O+=a){for(var M=b+O*t.strides[1],B=D;B<T;B+=i){var P=f[M+B*t.strides[2]+w];\"max\"===n&&P>N?N=P:\"avg\"===n&&(F+=P,_++)}if(isNaN(N))break}p[k+S*g+w]=\"avg\"===n?F/_:N}return d.toTensor()},o.prototype.maxPool=function(t,e){return this.pool(t,e,\"max\")},o.prototype.maxPoolPositions=function(t,e){for(var n=dr(e.outShape,\"int32\"),r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h=this.bufferSync(t),f=0;f<e.batchSize;++f)for(var d=0;d<e.inChannels;++d)for(var p=0;p<e.outHeight;++p){for(var v=p*r-c,m=v;m<0;)m+=a;for(var g=Math.min(e.inHeight,s+v),y=0;y<e.outWidth;++y){for(var x=y*o-l,b=x;b<0;)b+=i;for(var w=Math.min(e.inWidth,u+x),C=Number.NEGATIVE_INFINITY,E=-1,R=m;R<g;R+=a)for(var I=R-v,k=b;k<w;k+=i){var S=k-x,A=h.get(f,R,k,d);A>C&&(C=A,E=I*u+S)}n.set(E,f,p,y,d)}}return n.toTensor()},o.prototype.maxPoolBackprop=function(t,e,n,r){Hh([e,n],\"maxPoolBackprop\");for(var o=this.maxPoolPositions(e,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,l=r.effectiveFilterWidth,h=l-1-r.padInfo.left,f=c-1-r.padInfo.top,d=dr(e.shape,\"float32\"),p=this.bufferSync(o),v=this.bufferSync(t),m=0;m<r.batchSize;++m)for(var g=0;g<r.inChannels;++g)for(var y=0;y<r.inHeight;++y)for(var x=0;x<r.inWidth;++x){for(var b=y-f,w=x-h,C=0,E=0;E<c;E+=s){var R=(b+E)/a;if(!(R<0||R>=r.outHeight||Math.floor(R)!==R))for(var I=0;I<l;I+=u){var k=(w+I)/i;if(!(k<0||k>=r.outWidth||Math.floor(k)!==k)){var S=c*l-1-p.get(m,R,k,g)===E*l+I?1:0;if(0!==S)C+=v.get(m,R,k,g)*S}}}d.set(C,m,y,x,g)}return d.toTensor()},o.prototype.avgPoolBackprop=function(t,e,n){Hh([t,e],\"avgPoolBackprop\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,l=n.effectiveFilterWidth,h=l-1-n.padInfo.left,f=c-1-n.padInfo.top,d=dr(e.shape,\"float32\"),p=1/(a*i),v=this.bufferSync(t),m=0;m<n.batchSize;++m)for(var g=0;g<n.inChannels;++g)for(var y=0;y<n.inHeight;++y)for(var x=0;x<n.inWidth;++x){for(var b=y-f,w=x-h,C=0,E=0;E<c;E+=s){var R=(b+E)/r;if(!(R<0||R>=n.outHeight||Math.floor(R)!==R))for(var I=0;I<l;I+=u){var k=(w+I)/o;if(!(k<0||k>=n.outWidth||Math.floor(k)!==k))C+=v.get(m,R,k,g)}}d.set(C*p,m,y,x,g)}return d.toTensor()},o.prototype.pool3d=function(t,e,n){Hh(t,\"pool3d\");for(var r=e.strideDepth,o=e.strideHeight,a=e.strideWidth,i=e.dilationDepth,s=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,l=e.effectiveFilterHeight,h=e.effectiveFilterWidth,f=e.padInfo.front,d=e.padInfo.top,p=e.padInfo.left,v=\"max\"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=this.readSync(t.dataId),g=dr(e.outShape,t.dtype),y=g.values,x=e.outShape[1]*e.outShape[2]*e.outShape[3]*e.outShape[4],b=e.outShape[2]*e.outShape[3]*e.outShape[4],w=e.outShape[3]*e.outShape[4],C=e.outShape[4],E=0;E<e.batchSize;++E)for(var R=E*x,I=E*t.strides[0],k=0;k<e.inChannels;++k)for(var S=0;S<e.outDepth;++S){for(var A=S*r-f,D=A;D<0;)D+=i;for(var T=Math.min(e.inDepth,c+A),N=R+S*b,F=0;F<e.outHeight;++F){for(var _=F*o-d,O=_;O<0;)O+=s;for(var M=Math.min(e.inHeight,l+_),B=N+F*w,P=0;P<e.outWidth;++P){for(var L=P*a-p,W=L;W<0;)W+=u;for(var U=Math.min(e.inWidth,h+L),V=B+P*C,z=v,G=0,H=0,q=D;q<T;q+=i){for(var K=I+q*t.strides[1],j=O;j<M;j+=s){for(var X=K+j*t.strides[2],Y=W;Y<U;Y+=u){var $=m[X+Y*t.strides[3]+k];if(\"max\"===n&&$>z?z=$:\"avg\"===n&&(G+=$,H++),isNaN(z))break}if(isNaN(z))break}if(isNaN(z))break}y[V+k]=\"avg\"===n?G/H:z}}}return g.toTensor()},o.prototype.avgPool3d=function(t,e){return Hh(t,\"avgPool3d\"),this.pool3d(t,e,\"avg\").toFloat()},o.prototype.avgPool3dBackprop=function(t,e,n){Hh([t,e],\"avgPool3dBackprop\");for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,c=n.dilationDepth,l=n.dilationHeight,h=n.dilationWidth,f=n.effectiveFilterDepth,d=n.effectiveFilterHeight,p=n.effectiveFilterWidth,v=f-1-n.padInfo.front,m=p-1-n.padInfo.left,g=d-1-n.padInfo.top,y=dr(e.shape,\"float32\"),x=1/(i*s*u),b=this.bufferSync(t),w=0;w<n.batchSize;++w)for(var C=0;C<n.inChannels;++C)for(var E=0;E<n.inDepth;++E)for(var R=0;R<n.inHeight;++R)for(var I=0;I<n.inWidth;++I){for(var k=E-v,S=R-g,A=I-m,D=0,T=0;T<f;T+=c){var N=(k+T)/r;if(!(N<0||N>=n.outDepth||Math.floor(N)!==N))for(var F=0;F<d;F+=l){var _=(S+F)/o;if(!(_<0||_>=n.outHeight||Math.floor(_)!==_))for(var O=0;O<p;O+=h){var M=(A+O)/a;if(!(M<0||M>=n.outWidth||Math.floor(M)!==M))D+=b.get(w,N,_,M,C)}}}y.set(D*x,w,E,R,I,C)}return y.toTensor()},o.prototype.maxPool3d=function(t,e){return Hh(t,\"maxPool3d\"),this.pool3d(t,e,\"max\").toFloat()},o.prototype.maxPool3dPositions=function(t,e){for(var n=dr(e.outShape,\"int32\"),r=e.strideDepth,o=e.strideHeight,a=e.strideWidth,i=e.dilationDepth,s=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,l=e.effectiveFilterHeight,h=e.effectiveFilterWidth,f=e.padInfo.front,d=e.padInfo.top,p=e.padInfo.left,v=this.bufferSync(t),m=0;m<e.batchSize;++m)for(var g=0;g<e.inChannels;++g)for(var y=0;y<e.outDepth;++y){for(var x=y*r-f,b=x;b<0;)b+=i;for(var w=Math.min(e.inDepth,c+x),C=0;C<e.outHeight;++C){for(var E=C*o-d,R=E;R<0;)R+=s;for(var I=Math.min(e.inHeight,l+E),k=0;k<e.outWidth;++k){for(var S=k*a-p,A=S;A<0;)A+=u;for(var D=Math.min(e.inWidth,h+S),T=Number.NEGATIVE_INFINITY,N=-1,F=b;F<w;F+=i)for(var _=F-x,O=R;O<I;O+=s)for(var M=O-E,B=A;B<D;B+=u){var P=B-S,L=v.get(m,F,O,B,g);L>=T&&(T=L,N=_*l*h+M*l+P)}n.set(N,m,y,C,k,g)}}}return n.toTensor()},o.prototype.maxPool3dBackprop=function(t,e,n,r){Hh([e,n],\"maxPool3dBackprop\");for(var o=this.maxPool3dPositions(e,r),a=r.strideDepth,i=r.strideHeight,s=r.strideWidth,u=r.dilationDepth,c=r.dilationHeight,l=r.dilationWidth,h=r.effectiveFilterDepth,f=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=h-1-r.padInfo.front,v=d-1-r.padInfo.left,m=f-1-r.padInfo.top,g=dr(e.shape,\"float32\"),y=this.bufferSync(o),x=this.bufferSync(t),b=0;b<r.batchSize;++b)for(var w=0;w<r.inChannels;++w)for(var C=0;C<r.inDepth;++C)for(var E=0;E<r.inHeight;++E)for(var R=0;R<r.inWidth;++R){for(var I=C-p,k=E-m,S=R-v,A=0,D=0;D<h;D+=u){var T=(I+D)/a;if(!(T<0||T>=r.outDepth||Math.floor(T)!==T))for(var N=0;N<f;N+=c){var F=(k+N)/i;if(!(F<0||F>=r.outHeight||Math.floor(F)!==F))for(var _=0;_<d;_+=l){var O=(S+_)/s;if(!(O<0||O>=r.outWidth||Math.floor(O)!==O)){var M=h*f*d-1-y.get(b,T,F,O,w)===D*f*d+N*d+_?1:0;if(0!==M)A+=x.get(b,T,F,O,w)*M}}}}g.set(A,b,C,E,R,w)}return g.toTensor()},o.prototype.cast=function(t,e){return Po(t,e,this)},o.prototype.reshape=function(t,e){return Lo(t,e)},o.prototype.avgPool=function(t,e){return Hh(t,\"avgPool\"),this.pool(t,e,\"avg\").toFloat()},o.prototype.resizeBilinear=function(t,e,n,r){Hh(t,\"resizeBilinear\");for(var o=t.shape,a=o[0],i=o[1],s=o[2],u=o[3],c=this.readSync(t.dataId),l=new Float32Array(k([a,e,n,u])),h=[r&&e>1?i-1:i,r&&n>1?s-1:s],f=[r&&e>1?e-1:e,r&&n>1?n-1:n],d=0,p=h[0]/f[0],v=h[1]/f[1],m=0;m<a;m++)for(var g=0;g<e;g++)for(var y=p*g,x=Math.floor(y),b=y-x,w=Math.min(i-1,Math.ceil(y)),C=m*t.strides[0]+x*t.strides[1],E=m*t.strides[0]+w*t.strides[1],R=0;R<n;R++)for(var I=v*R,S=Math.floor(I),A=I-S,D=Math.min(s-1,Math.ceil(I)),T=C+S*t.strides[2],N=E+S*t.strides[2],F=C+D*t.strides[2],_=E+D*t.strides[2],O=0;O<u;O++){var M=c[T+O],B=c[N+O],P=M+(c[F+O]-M)*A,L=P+(B+(c[_+O]-B)*A-P)*b;l[d++]=L}return Fn(l,[a,e,n,u])},o.prototype.resizeBilinearBackprop=function(t,e,n){Hh([t,e],\"resizeBilinearBackprop\");for(var r=e.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=t.shape,c=u[1],l=u[2],h=new Float32Array(o*a*i*s),f=[n&&c>1?a-1:a,n&&l>1?i-1:i],d=[n&&c>1?c-1:c,n&&l>1?l-1:l],p=f[0]/d[0],v=f[1]/d[1],m=this.readSync(t.dataId),g=0,y=0;y<o;y++)for(var x=y*e.strides[0],b=0;b<c;b++)for(var w=b*p,C=Math.floor(w),E=Math.min(Math.ceil(w),a-1),R=x+C*e.strides[1],I=x+E*e.strides[1],k=w-C,S=1-k,A=0;A<l;A++)for(var D=A*v,T=Math.floor(D),N=Math.min(Math.ceil(D),i-1),F=D-T,_=1-F,O=R+T*e.strides[2],M=R+N*e.strides[2],B=I+T*e.strides[2],P=I+N*e.strides[2],L=S*_,W=S*F,U=k*_,V=k*F,z=0;z<s;z++){var G=m[g++];h[O+z]+=G*L,h[M+z]+=G*W,h[B+z]+=G*U,h[P+z]+=G*V}return Ln(h,[o,i,a,s],e.dtype)},o.prototype.resizeNearestNeighbor=function(t,e,n,r){Hh(t,\"resizeNearestNeighbor\");for(var o=t.shape,a=o[0],i=o[1],s=o[2],u=o[3],c=this.readSync(t.dataId),l=new Float32Array(a*e*n*u),h=[r&&e>1?i-1:i,r&&n>1?s-1:s],f=[r&&e>1?e-1:e,r&&n>1?n-1:n],d=h[0]/f[0],p=h[1]/f[1],v=0,m=0;m<a;m++)for(var g=m*t.strides[0],y=0;y<e;y++)for(var x=d*y,b=g+Math.min(i-1,r?Math.round(x):Math.floor(x))*t.strides[1],w=0;w<n;w++)for(var C=p*w,E=b+Math.min(s-1,r?Math.round(C):Math.floor(C))*t.strides[2],R=0;R<u;R++){var I=c[E+R];l[v++]=I}return Fn(l,[a,e,n,u],t.dtype)},o.prototype.resizeNearestNeighborBackprop=function(t,e,n){Hh([t,e],\"resizeNearestNeighborBackprop\");for(var r=e.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=t.shape,c=u[1],l=u[2],h=new Float32Array(o*a*i*s),f=this.readSync(t.dataId),d=[n&&c>1?a-1:a,n&&l>1?i-1:i],p=[n&&c>1?c-1:c,n&&l>1?l-1:l],v=d[0]/p[0],m=d[1]/p[1],g=1/v,y=1/m,x=2*Math.ceil(g)+2,b=2*Math.ceil(y)+2,w=0;w<o;w++)for(var C=w*e.strides[0],E=0;E<a;E++)for(var R=C+E*e.strides[1],I=Math.floor(E*g),k=Math.floor(I-x/2),S=0;S<i;S++)for(var A=R+S*e.strides[2],D=Math.floor(S*y),T=Math.floor(D-b/2),N=0;N<s;N++){for(var F=0,_=0;_<x;_++){var O=_+k;if(!(O<0||O>=c)){var M=C+O*t.strides[1],B=O*v;if(E===Math.min(a-1,n?Math.round(B):Math.floor(B)))for(var P=0;P<b;P++){var L=P+T;if(!(L<0||L>=l)){var W=M+L*t.strides[2],U=L*m;S===Math.min(i-1,n?Math.round(U):Math.floor(U))&&(F+=f[W+N])}}}}h[A+N]=F}return Ln(h,e.shape,e.dtype)},o.prototype.batchNormalization=function(t,e,n,r,o,a){Hh([t,e,n,o,a],\"batchNorm\");for(var i=this.readSync(t.dataId),s=this.readSync(e.dataId),u=this.readSync(n.dataId),c=o?this.readSync(o.dataId):new Float32Array([1]),l=a?this.readSync(a.dataId):new Float32Array([0]),h=new Float32Array(i.length),f=l.length,d=c.length,p=u.length,v=s.length,m=0,g=0,y=0,x=0,b=0;b<i.length;++b)h[b]=l[m++]+(i[b]-s[g++])*c[y++]/Math.sqrt(u[x++]+r),m>=f&&(m=0),g>=v&&(g=0),y>=d&&(y=0),x>=p&&(x=0);return Ln(h,t.shape)},o.prototype.localResponseNormalization4D=function(t,e,n,r,o){Hh(t,\"localResponseNormalization4D\");var a=t.shape[3],i=a-1,s=this.readSync(t.dataId),u=t.size,c=new Float32Array(u);function l(t){for(var n=t%a,r=t-n+Math.max(0,n-e),o=t-n+Math.min(n+e,i),u=0;r<=o;r++){var c=s[r];u+=c*c}return u}for(var h=0;h<u;h++){var f=l(h),d=s[h]*Math.pow(n+r*f,-o);c[h]=d}return Ln(c,t.shape)},o.prototype.LRNGrad=function(t,e,n,r,o,a,i){Hh(t,\"LRNGrad\");for(var s=t.shape[3],u=this.readSync(t.dataId),c=this.readSync(e.dataId),l=this.readSync(n.dataId),h=new Float32Array(t.size),f=t.size,d=0;d<f;d++){for(var p=d%s,v=d-p+Math.max(0,p-r),m=d-p+Math.min(s,p+r+1),g=0,y=v;y<m;y++)g+=Math.pow(c[y],2);g=a*g+o;for(y=v;y<m;y++){var x=-2*a*i*c[y]*l[d]/g;d===y&&(x+=Math.pow(g,-i)),x*=u[d],h[y]+=x}}return Ln(h,t.shape)},o.prototype.multinomial=function(t,e,n,r){Hh(t,\"multinomial\");for(var o=e?t:go(t),a=o.shape[0],i=o.shape[1],s=Gn([a,n],\"int32\"),u=this.readSync(s.dataId),c=this.readSync(o.dataId),l=0;l<a;++l){var h=l*i,f=new Float32Array(i-1);f[0]=c[h];for(var d=1;d<f.length;++d)f[d]=f[d-1]+c[h+d];for(var p=cr(r.toString()),v=l*n,m=0;m<n;++m){var g=p();u[v+m]=f.length;for(var y=0;y<f.length;y++)if(g<f[y]){u[v+m]=y;break}}}return s},o.prototype.oneHot=function(t,e,n,r){Hh(t,\"oneHot\");var o=new Float32Array(t.size*e);o.fill(r);for(var a=this.readSync(t.dataId),i=0;i<t.size;++i)a[i]>=0&&a[i]<e&&(o[i*e+a[i]]=n);return Bn(o,[t.size,e],\"int32\")},o.prototype.nonMaxSuppression=function(t,e,n,r,o){return Hh(t,\"nonMaxSuppression\"),jo(this.readSync(t.dataId),this.readSync(e.dataId),n,r,o)},o.prototype.fft=function(t){return this.fftBatch(t,!1)},o.prototype.ifft=function(t){return this.fftBatch(t,!0)},o.prototype.fftBatch=function(t,e){for(var n=t.shape[0],r=t.shape[1],o=dr(t.shape,\"float32\"),a=dr(t.shape,\"float32\"),i=Tn(t).as2D(n,r),s=Nn(t).as2D(n,r),u=0;u<n;u++)for(var c=i.slice([u,0],[1,r]),l=s.slice([u,0],[1,r]),h=Dn(c,l),f=this.readSync(this.fftImpl(h,e).dataId),d=0;d<r;d++){var p=zo(f,d);o.values[u*r+d]=p.real,a.values[u*r+d]=p.imag}return Dn(o.toTensor(),a.toTensor()).as2D(n,r)},o.prototype.fftImpl=function(t,e){var n=t.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,e).as2D(t.shape[0],t.shape[1]);return e&&(o=Dn(Tn(o).div(On(r)),Nn(o).div(On(r)))),o}var a=this.readSync(t.dataId),i=function(t){for(var e=new Float32Array(t.length/2),n=new Float32Array(t.length/2),r=0;r<t.length;r+=2)e[r/2]=t[r],n[r/2]=t[r+1];return{real:e,imag:n}}(this.fourierTransformByMatmul(a,r,e));return Dn(i.real,i.imag).as2D(t.shape[0],t.shape[1])},o.prototype.isExponentOf2=function(t){return 0==(t&t-1)},o.prototype.fftRadix2=function(t,e,n){if(1===e)return t;var r=this.readSync(t.dataId),o=e/2,a=function(t){for(var e=Math.ceil(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=0;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),i=Dn(a.real,a.imag).as1D(),s=function(t){for(var e=Math.floor(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=2;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),u=Dn(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var c=function(t,e){for(var n=new Float32Array(t/2),r=new Float32Array(t/2),o=0;o<Math.ceil(t/2);o++){var a=(e?2:-2)*Math.PI*(o/t);n[o]=Math.cos(a),r[o]=Math.sin(a)}return{real:n,imag:r}}(e,n),l=Dn(c.real,c.imag).mul(u),h=i.add(l),f=i.sub(l),d=Tn(h).concat(Tn(f)),p=Nn(h).concat(Nn(f));return Dn(d,p).as1D()},o.prototype.fourierTransformByMatmul=function(t,e,n){for(var r=new Float32Array(2*e),o=0;o<e;o++){for(var a=0,i=0,s=0;s<e;s++){var u=Ho(o*s,e,n),c=zo(t,s);a+=c.real*u.real-c.imag*u.imag,i+=c.real*u.imag+c.imag*u.real}n&&(a/=e,i/=e),Go(r,a,i,o)}return r},o.prototype.depthToSpace=function(t,e,n){C(\"NHWC\"===n,(function(){return\"Only NHWC dataFormat supported on CPU for depthToSpace. Got \"+n})),C(e>1,(function(){return\"blockSize should be > 1 for depthToSpace, but was: \"+e}));for(var r=t.shape[0],o=t.shape[1],a=t.shape[2],i=t.shape[3],s=o*e,u=a*e,c=i/(e*e),l=this.readSync(t.dataId),h=new Float32Array(r*s*u*c),f=0,d=0;d<r;++d)for(var p=0;p<s;++p)for(var v=Math.floor(p/e),m=p%e,g=0;g<u;++g)for(var y=Math.floor(g/e),x=(m*e+g%e)*c,b=0;b<c;++b){var w=b+x+i*(y+a*(v+o*d));h[f++]=l[w]}return Ln(h,[r,s,u,c])},o.prototype.broadcastedBinaryOp=function(t,e,n,r){var o=Ro(t.shape,e.shape),a=dr(o,n),i=this.readSync(t.dataId),s=this.readSync(e.dataId),u=Co(t.shape,o),c=Co(e.shape,o),l=a.values;if(u.length+c.length===0)for(var h=0;h<l.length;++h)l[h]=r(i[h%i.length],s[h%s.length]);else{var f=this.bufferSync(t),d=this.bufferSync(e),p=function(n){var o=a.indexToLoc(n),h=o.slice(-t.rank);u.forEach((function(t){return h[t]=0}));var p=f.locToIndex(h),v=o.slice(-e.rank);c.forEach((function(t){return v[t]=0}));var m=d.locToIndex(v);l[n]=r(i[p],s[m])};for(h=0;h<l.length;++h)p(h)}return a.toTensor()},o.prototype.broadcastedBinaryComplexOp=function(t,e,n){var r=Ro(t.shape,e.shape),o=dr(r,\"float32\"),a=dr(r,\"float32\"),i=this.readSync(t.dataId),s=this.readSync(e.dataId),u=Co(t.shape,r),c=Co(e.shape,r),l=o.values,h=a.values;if(u.length+c.length===0)for(var f=0;f<l.length;f++){var d=f%i.length,p=f%s.length,v=n(i[2*d],i[2*d+1],s[2*p],s[2*p+1]);l[f]=v.real,h[f]=v.imag}else{var m=this.bufferSync(this.data.get(t.dataId).complexTensors.real),g=this.bufferSync(this.data.get(e.dataId).complexTensors.real),y=function(r){var a=o.indexToLoc(r),f=a.slice(-t.rank);u.forEach((function(t){return f[t]=0}));var d=m.locToIndex(f),p=a.slice(-e.rank);c.forEach((function(t){return p[t]=0}));var v=g.locToIndex(p),y=n(i[2*d],i[2*d+1],s[2*v],s[2*v+1]);l[r]=y.real,h[r]=y.imag};for(f=0;f<l.length;f++)y(f)}return this.complex(o.toTensor(),a.toTensor())},o.prototype.split=function(t,e,n){return Zo(t,e,n)},o.prototype.dispose=function(){},o.prototype.floatPrecision=function(){return 32},o.prototype.epsilon=function(){return 1e-7},o.prototype.cropAndResize=function(t,e,n,r,o,a){for(var i=t.shape,s=i[0],u=i[1],c=i[2],l=i[3],h=e.shape[0],f=r[0],d=r[1],p=dr([h,f,d,l],\"float32\"),v=this.readSync(e.dataId),m=this.readSync(n.dataId),g=this.readSync(t.dataId),y=t.strides,x=p.strides,b=0;b<h;b++){var w=4*b,C=v[w],E=v[w+1],R=v[w+2],I=v[w+3],k=m[b];if(!(k>=s))for(var S=f>1?(R-C)*(u-1)/(f-1):0,A=d>1?(I-E)*(c-1)/(d-1):0,D=0;D<f;D++){var T=f>1?C*(u-1)+D*S:.5*(C+R)*(u-1);if(T<0||T>u-1)for(var N=0;N<d;N++)for(var F=0;F<l;F++){var _=F+N*x[2]+D*x[1]+b*x[0];p.values[_]=a}else if(\"bilinear\"===o){var O=Math.floor(T),M=Math.ceil(T),B=T-O;for(N=0;N<d;N++){if((q=d>1?E*(c-1)+N*A:.5*(E+I)*(c-1))<0||q>c-1)for(F=0;F<l;F++){_=F+N*x[2]+D*x[1]+b*x[0];p.values[_]=a}else{var P=Math.floor(q),L=Math.ceil(q),W=q-P;for(F=0;F<l;F++){var U=g[_=F+P*y[2]+O*y[1]+k*y[0]],V=g[_=F+L*y[2]+O*y[1]+k*y[0]],z=g[_=F+P*y[2]+M*y[1]+k*y[0]],G=U+(V-U)*W,H=z+(g[_=F+L*y[2]+M*y[1]+k*y[0]]-z)*W;_=F+N*x[2]+D*x[1]+b*x[0],p.values[_]=G+(H-G)*B}}}}else for(N=0;N<d;++N){var q;if((q=d>1?E*(c-1)+N*A:.5*(E+I)*(c-1))<0||q>c-1)for(F=0;F<l;F++){_=F+N*x[2]+D*x[1]+b*x[0];p.values[_]=a}else{var K=Math.round(q),j=Math.round(T);for(F=0;F<l;F++){var X=F+K*y[2]+j*y[1]+k*y[0],Y=F+N*x[2]+D*x[1]+b*x[0];p.values[Y]=g[X]}}}}}return p.toTensor()},o.prototype.sparseToDense=function(t,e,n,r){var o=Zr(0,t,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,c=o.outputSize;return this.scatter(t,e,n,c,s,i,a,u,r,!1)},o.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=jr(t,e),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return Fn([],a,t.dtype);for(var c=new gt([i,s],t.dtype),l=this.readSync(e.dataId),h=this.readSync(t.dataId),f=0;f<i;f++){for(var d=[],p=0,v=0;v<r;v++){var m=l[f*r+v];p+=m*u[v],d.push(m)}if(p<0||p>=t.size/s)throw new Error(\"Invalid indices: \"+d+\" does not index into \"+t.shape);for(var g=0;g<s;g++)c.values[f*s+g]=h[p*s+g]}return c.toTensor().reshape(a)},o.prototype.scatterND=function(t,e,n){var r=Zr(0,t,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,c=On(0);return this.scatter(t,e,n,u,i,a,o,s,c,!0)},o.prototype.fill=function(t,e,n){var r=P(n=n||j(e),k(t));return r.fill(e),Lt.makeTensor(r,t,n,this)},o.prototype.onesLike=function(t){if(\"string\"===t.dtype)throw new Error(\"onesLike is not supported for string tensors\");return this.fill(t.shape,1,t.dtype)},o.prototype.zerosLike=function(t){var e=P(t.dtype,k(t.shape));return this.makeOutput(e,t.shape,t.dtype)},o.prototype.linspace=function(t,e,n){return Wo(t,e,n)},o.prototype.scatter=function(t,e,n,r,o,a,i,s,u,c){var l=[r/o,o],h=this.readSync(t.dataId),f=this.readSync(e.dataId);if(0===r)return Fn([],n,e.dtype);var d=new gt(l,e.dtype);d.values.fill(this.readSync(u.dataId)[0]);for(var p=0;p<a;p++){for(var v=[],m=0,g=0;g<i;g++){var y=h[p*i+g];v.push(y),m+=y*s[g]}if(m<0||m>=r/o)throw new Error(\"Invalid indices: \"+v+\" does not index into \"+n);for(var x=0;x<o;x++)c?d.values[m*o+x]+=f[p*o+x]:d.values[m*o+x]=0===e.rank?f[0]:f[p*o+x]}return d.toTensor().reshape(n)},o}(bo);Lt.registerBackend(\"cpu\",(function(){return new Kh}),1);for(var jh=0,Xh=[{kernelName:\"NonMaxSuppressionV5\",backendName:\"cpu\",kernelFunc:function(t){var e=t.inputs,n=t.backend,r=t.attrs,o=e,a=o.boxes,i=o.scores,s=r,u=s.maxOutputSize,c=s.iouThreshold,l=s.scoreThreshold,h=s.softNmsSigma,f=n;Hh(a,\"NonMaxSuppressionWithScore\");var d=Xo(f.data.get(a.dataId).values,f.data.get(i.dataId).values,u,c,l,h);return[d.selectedIndices,d.selectedScores]}},{kernelName:\"Square\",backendName:\"cpu\",kernelFunc:function(t){var e=t.inputs,n=t.backend,r=e.x,o=n;Hh(r,\"square\");for(var a=o.data.get(r.dataId).values,i=new Float32Array(a.length),s=0;s<a.length;++s){var 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col;\\n\\n vec2 uv = (vec2(texC, texR) + halfCR) /\\n vec2(\"+r+\".0, \"+n+\".0);\\n vec4 values = \"+e.texture2D+\"(A, uv);\\n float value;\\n if (depth == 0) {\\n value = values.r;\\n } else if (depth == 1) {\\n value = values.g;\\n } else if (depth == 2) {\\n value = values.b;\\n } else if (depth == 3) {\\n value = values.a;\\n }\\n\\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\\n }\\n }\\n\\n \"+e.output+\" = result;\\n }\\n \"};for(var Jh=0,Zh=[{kernelName:\"FromPixels\",backendName:\"webgl\",kernelFunc:function(t){var e=t.inputs,n=t.backend,r=t.attrs,o=e.pixels,a=r.numChannels,s=\"undefined\"!=typeof HTMLVideoElement&&o instanceof HTMLVideoElement,u=\"undefined\"!=typeof HTMLImageElement&&o instanceof HTMLImageElement,c=s?[o.videoWidth,o.videoHeight]:[o.width,o.height],l=c[0],h=c[1],f=[h,l],d=[h,l,a];(u||s)&&(null==Yh&&(Yh=document.createElement(\"canvas\").getContext(\"2d\")),Yh.canvas.width=l,Yh.canvas.height=h,Yh.drawImage(o,0,0,l,h),o=Yh.canvas);var 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h=r.sent(),o&&l?[4,yf.getManager(u).removeModel(c)]:[3,3];case 2:r.sent(),r.label=3;case 3:return[4,s.save(h)];case 4:return f=r.sent(),!o||l?[3,6]:[4,yf.getManager(u).removeModel(c)];case 5:r.sent(),r.label=6;case 6:return[2,f.modelArtifactsInfo]}}))}))}var wf=\"models_store\",Cf=\"model_info_store\";function Ef(){if(!i().getBool(\"IS_BROWSER\"))throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");var t=window||self,e=t.indexedDB||t.mozIndexedDB||t.webkitIndexedDB||t.msIndexedDB||t.shimIndexedDB;if(null==e)throw new Error(\"The current browser does not appear to support IndexedDB.\");return e}function Rf(t){var e=t.result;e.createObjectStore(wf,{keyPath:\"modelPath\"}),e.createObjectStore(Cf,{keyPath:\"modelPath\"})}var If=function(){function t(t){if(this.indexedDB=Ef(),null==t||!t)throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");this.modelPath=t}return t.prototype.save=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(e){if(t.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");return[2,this.databaseAction(this.modelPath,t)]}))}))},t.prototype.load=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){return[2,this.databaseAction(this.modelPath)]}))}))},t.prototype.databaseAction=function(t,e){var n=this;return new Promise((function(t,r){var o=n.indexedDB.open(\"tensorflowjs\",1);o.onupgradeneeded=function(){return Rf(o)},o.onsuccess=function(){var a=o.result;if(null==e){var i=a.transaction(wf,\"readonly\"),s=i.objectStore(wf).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error(\"Cannot find model with path '\"+n.modelPath+\"' in IndexedDB.\"));t(s.result.modelArtifacts)},s.onerror=function(t){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()}}else{var u,c=vf(e),l=a.transaction(Cf,\"readwrite\"),h=l.objectStore(Cf),f=h.put({modelPath:n.modelPath,modelArtifactsInfo:c});f.onsuccess=function(){var o=(u=a.transaction(wf,\"readwrite\")).objectStore(wf).put({modelPath:n.modelPath,modelArtifacts:e,modelArtifactsInfo:c});o.onsuccess=function(){return t({modelArtifactsInfo:c})},o.onerror=function(t){var e=(h=l.objectStore(Cf)).delete(n.modelPath);e.onsuccess=function(){return a.close(),r(o.error)},e.onerror=function(t){return a.close(),r(o.error)}}},f.onerror=function(t){return a.close(),r(f.error)},l.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()}}}},o.onerror=function(t){return r(o.error)}}))},t.URL_SCHEME=\"indexeddb://\",t}(),kf=function(t){return i().getBool(\"IS_BROWSER\")&&!Array.isArray(t)&&t.startsWith(If.URL_SCHEME)?(e=t.slice(If.URL_SCHEME.length),new If(e)):null;var e};mf.registerSaveRouter(kf),mf.registerLoadRouter(kf);var Sf=function(){function t(){this.indexedDB=Ef()}return t.prototype.listModels=function(){return n(this,void 0,void 0,(function(){var t=this;return r(this,(function(e){return[2,new Promise((function(e,n){var r=t.indexedDB.open(\"tensorflowjs\",1);r.onupgradeneeded=function(){return Rf(r)},r.onsuccess=function(){var t=r.result,o=t.transaction(Cf,\"readonly\"),a=o.objectStore(Cf).getAll();a.onsuccess=function(){for(var t={},n=0,r=a.result;n<r.length;n++){var o=r[n];t[o.modelPath]=o.modelArtifactsInfo}e(t)},a.onerror=function(e){return t.close(),n(a.error)},o.oncomplete=function(){return t.close()}},r.onerror=function(t){return n(r.error)}}))]}))}))},t.prototype.removeModel=function(t){return n(this,void 0,void 0,(function(){var e=this;return r(this,(function(n){var r;return t=(r=t).startsWith(If.URL_SCHEME)?r.slice(If.URL_SCHEME.length):r,[2,new Promise((function(n,r){var o=e.indexedDB.open(\"tensorflowjs\",1);o.onupgradeneeded=function(){return Rf(o)},o.onsuccess=function(){var e,a=o.result,i=a.transaction(Cf,\"readwrite\"),s=i.objectStore(Cf),u=s.get(t);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error(\"Cannot find model with path '\"+t+\"' in IndexedDB.\"));var o=s.delete(t),i=function(){var o=(e=a.transaction(wf,\"readwrite\")).objectStore(wf).delete(t);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(t){return r(u.error)}};o.onsuccess=i,o.onerror=function(t){return i(),a.close(),r(u.error)}},u.onerror=function(t){return a.close(),r(u.error)},i.oncomplete=function(){null==e?a.close():e.oncomplete=function(){return a.close()}}},o.onerror=function(t){return r(o.error)}}))]}))}))},t}();if(i().getBool(\"IS_BROWSER\"))try{yf.registerManager(If.URL_SCHEME,new Sf)}catch(t){}var Af=\"/\",Df=\"tensorflowjs_models\",Tf=\"info\",Nf=\"model_topology\",Ff=\"weight_specs\",_f=\"weight_data\",Of=\"model_metadata\";function Mf(t){return{info:[Df,t,Tf].join(Af),topology:[Df,t,Nf].join(Af),weightSpecs:[Df,t,Ff].join(Af),weightData:[Df,t,_f].join(Af),modelMetadata:[Df,t,Of].join(Af)}}function Bf(t){var e=t.split(Af);if(e.length<3)throw new Error(\"Invalid key format: \"+t);return e.slice(1,e.length-1).join(Af)}var Pf=function(){function t(t){if(!i().getBool(\"IS_BROWSER\")||\"undefined\"==typeof window||void 0===window.localStorage)throw new Error(\"The current environment does not support local storage.\");if(this.LS=window.localStorage,null==t||!t)throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");this.modelPath=t,this.keys=Mf(this.modelPath)}return t.prototype.save=function(t){return n(this,void 0,void 0,(function(){var e,n,o;return r(this,(function(r){if(t.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats 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modelTopologyBytes=\"+o.modelTopologyBytes+\", weightSpecsBytes=\"+o.weightSpecsBytes+\", weightDataBytes=\"+o.weightDataBytes+\".\")}return[2]}))}))},t.prototype.load=function(){return n(this,void 0,void 0,(function(){var t,e,n,o,a,i,s;return r(this,(function(r){if(null==(t=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error(\"In local storage, there is no model with name '\"+this.modelPath+\"'\");if(\"JSON\"!==t.modelTopologyType)throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");if(e={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error(\"In local storage, the topology of model '\"+this.modelPath+\"' is missing.\");if(e.modelTopology=n,null==(o=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error(\"In local storage, the weight specs of model '\"+this.modelPath+\"' are missing.\");if(e.weightSpecs=o,null!=(a=this.LS.getItem(this.keys.modelMetadata))&&(i=JSON.parse(a),e.format=i.format,e.generatedBy=i.generatedBy,e.convertedBy=i.convertedBy,e.userDefinedMetadata=i.userDefinedMetadata),null==(s=this.LS.getItem(this.keys.weightData)))throw new Error(\"In local storage, the binary weight values of model '\"+this.modelPath+\"' are missing.\");return e.weightData=function(t){if(hf){var e=Buffer.from(t,\"base64\");return e.buffer.slice(e.byteOffset,e.byteOffset+e.byteLength)}for(var n=atob(t),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}(s),[2,e]}))}))},t.URL_SCHEME=\"localstorage://\",t}(),Lf=function(t){return i().getBool(\"IS_BROWSER\")&&!Array.isArray(t)&&t.startsWith(Pf.URL_SCHEME)?(e=t.slice(Pf.URL_SCHEME.length),new Pf(e)):null;var e};mf.registerSaveRouter(Lf),mf.registerLoadRouter(Lf);var Wf=function(){function t(){C(i().getBool(\"IS_BROWSER\"),(function(){return\"Current environment is not a web 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n=JSON.parse(this.LS.getItem(e.info)),this.LS.removeItem(e.info),this.LS.removeItem(e.topology),this.LS.removeItem(e.weightSpecs),this.LS.removeItem(e.weightData),[2,n]}))}))},t}();if(i().getBool(\"IS_BROWSER\"))try{yf.registerManager(Pf.URL_SCHEME,new Wf)}catch(t){}var Uf=\"model\",Vf=\".json\",zf=\".weights.bin\";function Gf(t){return new Promise((function(t){return setTimeout(t)})).then(t)}var Hf=function(){function t(e){if(!i().getBool(\"IS_BROWSER\"))throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");e.startsWith(t.URL_SCHEME)&&(e=e.slice(t.URL_SCHEME.length)),null!=e&&0!==e.length||(e=Uf),this.modelTopologyFileName=e+Vf,this.weightDataFileName=e+zf}return t.prototype.save=function(t){return n(this,void 0,void 0,(function(){var e,n,o,a,i,s;return r(this,(function(r){switch(r.label){case 0:if(\"undefined\"==typeof document)throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");if(e=window.URL.createObjectURL(new Blob([t.weightData],{type:\"application/octet-stream\"})),!(t.modelTopology instanceof ArrayBuffer))return[3,1];throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");case 1:return n=[{paths:[\"./\"+this.weightDataFileName],weights:t.weightSpecs}],o={modelTopology:t.modelTopology,format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy,weightsManifest:n},a=window.URL.createObjectURL(new Blob([JSON.stringify(o)],{type:\"application/json\"})),(i=null==this.jsonAnchor?document.createElement(\"a\"):this.jsonAnchor).download=this.modelTopologyFileName,i.href=a,[4,Gf((function(){return i.dispatchEvent(new MouseEvent(\"click\"))}))];case 2:return r.sent(),null==t.weightData?[3,4]:((s=null==this.weightDataAnchor?document.createElement(\"a\"):this.weightDataAnchor).download=this.weightDataFileName,s.href=e,[4,Gf((function(){return s.dispatchEvent(new MouseEvent(\"click\"))}))]);case 3:r.sent(),r.label=4;case 4:return[2,{modelArtifactsInfo:vf(t)}]}}))}))},t.URL_SCHEME=\"downloads://\",t}(),qf=function(){function t(t){if(null==t||t.length<1)throw new Error(\"When calling browserFiles, at least 1 file is required, but received \"+t);this.files=t}return t.prototype.load=function(){return n(this,void 0,void 0,(function(){var t,e,n=this;return r(this,(function(r){return t=this.files[0],e=this.files.slice(1),[2,new Promise((function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===e.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var c;try{c=n.checkManifestAndWeightFiles(u,e)}catch(t){return void o(t)}var l=[],h=[],f=[];u.forEach((function(t){t.paths.forEach((function(t){h.push(t),f.push(null)})),l.push.apply(l,t.weights)})),u.forEach((function(t){t.paths.forEach((function(t){var e=new FileReader;e.onload=function(e){var n=e.target.result,o=h.indexOf(t);f[o]=n,-1===f.indexOf(null)&&r({modelTopology:s,weightSpecs:l,weightData:df(f),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy,userDefinedMetadata:i.userDefinedMetadata})},e.onerror=function(e){return o(\"Failed to weights data from file of path '\"+t+\"'.\")},e.readAsArrayBuffer(c[t])}))}))}else o(new Error(\"weightManifest field is missing from file \"+t.name))}else o(new Error(\"modelTopology field is missing from file \"+t.name))},a.onerror=function(e){return o(\"Failed to read model topology and weights manifest JSON from file '\"+t.name+\"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.\")},a.readAsText(t)}))]}))}))},t.prototype.checkManifestAndWeightFiles=function(t,e){for(var n=[],r=e.map((function(t){return pf(t.name)})),o={},a=0,i=t;a<i.length;a++){i[a].paths.forEach((function(t){var a=pf(t);if(-1!==n.indexOf(a))throw new Error(\"Duplicate file basename found in weights manifest: '\"+a+\"'\");if(n.push(a),-1===r.indexOf(a))throw new Error(\"Weight file with basename '\"+a+\"' is not provided.\");o[t]=e[r.indexOf(a)]}))}if(n.length!==e.length)throw new Error(\"Mismatch in the number of files in weights manifest (\"+n.length+\") and the number of weight files provided (\"+e.length+\").\");return o},t}();function Kf(t,e,n,r){!function(t){C(null!=t&&Array.isArray(t)&&t.length>0,(function(){return\"promises must be a none empty array\"}))}(t),function(t,e){C(t>=0&&t<=1,(function(){return\"Progress fraction must be in range [0, 1], but got startFraction \"+t})),C(e>=0&&e<=1,(function(){return\"Progress fraction must be in range [0, 1], but got endFraction \"+e})),C(e>=t,(function(){return\"startFraction must be no more than endFraction, but got startFraction \"+t+\" and endFraction \"+e}))}(n=null==n?0:n,r=null==r?1:r);var o=0;return Promise.all(t.map((function(a){return a.then((function(a){var i=n+ ++o/t.length*(r-n);return e(i),a})),a})))}function jf(t,e){return n(this,void 0,void 0,(function(){var n,o,a,s,u,c,l,h,f;return r(this,(function(r){switch(r.label){case 0:return null==e&&(e={}),n=null==e.fetchFunc?i().platform.fetch:e.fetchFunc,o=t.map((function(t){return n(t,e.requestInit,{isBinary:!0})})),a=0,s=.5,null!=e.onProgress?[3,2]:[4,Promise.all(o)];case 1:return u=r.sent(),[3,4];case 2:return[4,Kf(o,e.onProgress,a,s)];case 3:u=r.sent(),r.label=4;case 4:return c=u.map((function(t){return t.arrayBuffer()})),l=.5,h=1,null!=e.onProgress?[3,6]:[4,Promise.all(c)];case 5:return f=r.sent(),[3,8];case 6:return[4,Kf(c,e.onProgress,l,h)];case 7:f=r.sent(),r.label=8;case 8:return[2,f]}}))}))}function Xf(t){var e=this;return function(o,a,i){return void 0===a&&(a=\"\"),n(e,void 0,void 0,(function(){var e,n,s,u,c,l,h,f,d,p;return r(this,(function(r){switch(r.label){case 0:if(e=o.map((function(){return!1})),n={},s=null!=i?i.map((function(){return!1})):[],u=[],o.forEach((function(t,r){var o=0;t.weights.forEach((function(t){var a=\"quantization\"in t?t.quantization.dtype:t.dtype,c=sf[a]*k(t.shape),l=function(){e[r]=!0,null==n[r]&&(n[r]=[]),n[r].push({manifestEntry:t,groupOffset:o,sizeBytes:c})};null!=i?i.forEach((function(e,n){e===t.name&&(l(),s[n]=!0)})):l(),u.push(t.name),o+=c}))})),!s.every((function(t){return t})))throw c=i.filter((function(t,e){return!s[e]})),new Error(\"Could not find weights in manifest with names: \"+c.join(\", \")+\". \\nManifest JSON has weights with names: \"+u.join(\", \")+\".\");return l=e.reduce((function(t,e,n){return e&&t.push(n),t}),[]),h=[],l.forEach((function(t){o[t].paths.forEach((function(t){var e=a+(a.endsWith(\"/\")?\"\":\"/\")+t;h.push(e)}))})),[4,t(h)];case 1:return f=r.sent(),d={},p=0,l.forEach((function(t){for(var e=o[t].paths.length,r=0,a=0;a<e;a++)r+=f[p+a].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,c=0;c<e;c++){var l=new Uint8Array(f[p+c]);s.set(l,u),u+=l.byteLength}n[t].forEach((function(t){var e=cf(i.slice(t.groupOffset,t.groupOffset+t.sizeBytes),[t.manifestEntry]);for(var n in e)d[n]=e[n]})),p+=e})),[2,d]}}))}))}}mf.registerSaveRouter((function(t){return i().getBool(\"IS_BROWSER\")&&!Array.isArray(t)&&t.startsWith(Hf.URL_SCHEME)?function(t){void 0===t&&(t=\"model\");return new Hf(t)}(t.slice(Hf.URL_SCHEME.length)):null}));var Yf=function(){function t(t,e){if(this.DEFAULT_METHOD=\"POST\",null==e&&(e={}),this.weightPathPrefix=e.weightPathPrefix,this.onProgress=e.onProgress,null!=e.fetchFunc?(C(\"function\"==typeof e.fetchFunc,(function(){return\"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\"})),this.fetch=e.fetchFunc):this.fetch=i().platform.fetch,C(null!=t&&t.length>0,(function(){return\"URL path for http must not be null, undefined or empty.\"})),Array.isArray(t)&&C(2===t.length,(function(){return\"URL paths for http must have a length of 2, (actual length is \"+t.length+\").\"})),this.path=t,null!=e.requestInit&&null!=e.requestInit.body)throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");this.requestInit=e.requestInit||{}}return t.prototype.save=function(t){return n(this,void 0,void 0,(function(){var e,n,o,a;return r(this,(function(r){switch(r.label){case 0:if(t.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");return(e=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:[\"./model.weights.bin\"],weights:t.weightSpecs}],o={modelTopology:t.modelTopology,format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy,userDefinedMetadata:t.userDefinedMetadata,weightsManifest:n},e.body.append(\"model.json\",new Blob([JSON.stringify(o)],{type:\"application/json\"}),\"model.json\"),null!=t.weightData&&e.body.append(\"model.weights.bin\",new Blob([t.weightData],{type:\"application/octet-stream\"}),\"model.weights.bin\"),[4,this.fetch(this.path,e)];case 1:if((a=r.sent()).ok)return[2,{modelArtifactsInfo:vf(t),responses:[a]}];throw new Error(\"BrowserHTTPRequest.save() failed due to HTTP response status \"+a.status+\".\")}}))}))},t.prototype.load=function(){return n(this,void 0,void 0,(function(){var t,e,n,o,a,i,s,u,c,l,h,f;return r(this,(function(r){switch(r.label){case 0:return[4,this.fetch(this.path,this.requestInit)];case 1:if(!(t=r.sent()).ok)throw new Error(\"Request to \"+this.path+\" failed with status code \"+t.status+\". Please verify this URL points to the model JSON of the model to load.\");r.label=2;case 2:return r.trys.push([2,4,,5]),[4,t.json()];case 3:return e=r.sent(),[3,5];case 4:throw r.sent(),n=\"Failed to parse model JSON of response from \"+this.path+\".\",this.path.endsWith(\".pb\")?n+=\" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.\":n+=\" Please make sure the server is serving valid JSON for this request.\",new Error(n);case 5:if(o=e.modelTopology,a=e.weightsManifest,i=e.generatedBy,s=e.convertedBy,u=e.format,c=e.userDefinedMetadata,null==o&&null==a)throw new Error(\"The JSON from HTTP path \"+this.path+\" contains neither model topology or manifest for weights.\");return null==a?[3,7]:[4,this.loadWeights(a)];case 6:f=r.sent(),l=f[0],h=f[1],r.label=7;case 7:return[2,{modelTopology:o,weightSpecs:l,weightData:h,userDefinedMetadata:c,generatedBy:i,convertedBy:s,format:u}]}}))}))},t.prototype.loadWeights=function(t){return n(this,void 0,void 0,(function(){var e,n,o,a,i,s,u,c,l,h,f;return r(this,(function(r){switch(r.label){case 0:for(e=Array.isArray(this.path)?this.path[1]:this.path,n=function(t){var e=t.lastIndexOf(\"/\"),n=t.lastIndexOf(\"?\"),r=t.substring(0,e),o=n>e?t.substring(n):\"\";return[r+\"/\",o]}(e),o=n[0],a=n[1],i=this.weightPathPrefix||o,s=[],u=0,c=t;u<c.length;u++)l=c[u],s.push.apply(s,l.weights);return h=[],t.forEach((function(t){t.paths.forEach((function(t){h.push(i+t+a)}))})),[4,jf(h,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress})];case 1:return f=r.sent(),[2,[s,df(f)]]}}))}))},t.URL_SCHEME_REGEX=/^https?:\\/\\//,t}();function $f(t){return null!=t.match(Yf.URL_SCHEME_REGEX)}var Qf=function(t,e){if(\"undefined\"==typeof fetch)return null;return(Array.isArray(t)?t.every((function(t){return $f(t)})):$f(t))?Jf(t,{onProgress:e}):null};function Jf(t,e){return new Yf(t,e)}mf.registerSaveRouter(Qf),mf.registerLoadRouter(Qf);var Zf=function(){function t(t){this.modelArtifacts=t}return t.prototype.load=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){return[2,this.modelArtifacts]}))}))},t}(),td=function(){function t(t){this.saveHandler=t}return t.prototype.save=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(e){return[2,this.saveHandler(t)]}))}))},t}();var ed=Object.freeze({browserFiles:function(t){return new qf(t)},browserHTTPRequest:function(t,e){return Jf(t,e)},concatenateArrayBuffers:df,decodeWeights:cf,encodeWeights:function(t,e){return n(this,void 0,void 0,(function(){var o,a,i,s,u,c=this;return r(this,(function(l){switch(l.label){case 0:for(o=[],a=[],i=Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t),s=function(s){var u=i[s],l=Array.isArray(t)?t[s].tensor:t[u];if(\"float32\"!==l.dtype&&\"int32\"!==l.dtype&&\"bool\"!==l.dtype&&\"string\"!==l.dtype)throw new Error(\"Unsupported dtype in weight '\"+u+\"': \"+l.dtype);var h={name:u,shape:l.shape,dtype:l.dtype};if(\"string\"===l.dtype){var f=new Promise((function(t){return n(c,void 0,void 0,(function(){var e,n,o,a,i,s,u;return r(this,(function(r){switch(r.label){case 0:return[4,l.bytes()];case 1:for(e=r.sent(),n=e.reduce((function(t,e){return t+e.length}),0)+uf*e.length,o=new Uint8Array(n),a=0,i=0;i<e.length;i++)s=e[i],u=new Uint8Array(new Uint32Array([s.length]).buffer),o.set(u,a),a+=uf,o.set(s,a),a+=s.length;return t(o),[2]}}))}))}));a.push(f)}else a.push(l.data());null!=e&&(h.group=e),o.push(h)},u=0;u<i.length;++u)s(u);return[4,Promise.all(a)];case 1:return[2,{data:lf(l.sent()),specs:o}]}}))}))},fromMemory:function(t,e,n,r){return 1===arguments.length?null!=t.modelTopology||null!=t.weightSpecs?new Zf(t):(console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\"),new Zf({modelTopology:t})):(console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\"),new Zf({modelTopology:t,weightSpecs:e,weightData:n,trainingConfig:r}))},getLoadHandlers:function(t,e){return mf.getLoadHandlers(t,e)},getModelArtifactsInfoForJSON:vf,getSaveHandlers:function(t){return mf.getSaveHandlers(t)},http:Jf,isHTTPScheme:$f,loadWeights:function(t,e,o,a){return void 0===e&&(e=\"\"),n(this,void 0,void 0,(function(){return r(this,(function(n){return[2,Xf((function(t){return jf(t,{requestInit:a})}))(t,e,o)]}))}))},registerLoadRouter:function(t){return mf.registerLoadRouter(t)},registerSaveRouter:function(t){return mf.registerSaveRouter(t)},weightsLoaderFactory:Xf,withSaveHandler:function(t){return new td(t)},copyModel:function(t,e){return n(this,void 0,void 0,(function(){return r(this,(function(n){return!1,[2,bf(t,e,!1)]}))}))},listModels:function(){return n(this,void 0,void 0,(function(){var t,e,n,o,a,i,s;return r(this,(function(r){switch(r.label){case 0:t=yf.getSchemes(),e={},n=0,o=t,r.label=1;case 1:return n<o.length?(a=o[n],[4,yf.getManager(a).listModels()]):[3,4];case 2:for(s in i=r.sent())e[a+gf+s]=i[s];r.label=3;case 3:return n++,[3,1];case 4:return[2,e]}}))}))},moveModel:function(t,e){return n(this,void 0,void 0,(function(){return r(this,(function(n){return!0,[2,bf(t,e,!0)]}))}))},removeModel:function(t){return n(this,void 0,void 0,(function(){var e;return r(this,(function(n){return e=xf(t),[2,yf.getManager(e.scheme).removeModel(e.path)]}))}))}});var nd,rd=An({confusionMatrix_:function(t,e,n){var r=mn(t,\"labels\",\"confusionMatrix\"),o=mn(e,\"predictions\",\"confusionMatrix\");C(null==n||n>0&&Number.isInteger(n),(function(){return\"If provided, numClasses must be a positive integer, but got \"+n})),C(1===r.rank,(function(){return\"Expected the rank of labels to be 1, but got \"+r.rank})),C(1===o.rank,(function(){return\"Expected the rank of predictions to be 1, but got \"+o.rank})),C(r.shape[0]===o.shape[0],(function(){return\"Mismatch in the number of examples: \"+r.shape[0]+\" vs. \"+o.shape[0]+\". Labels and predictions should have the same number of elements.\"})),C(n>0&&Number.isInteger(n),(function(){return\"numClasses is required to be a positive integer, but got \"+n}));var a=Rr(r.asType(\"int32\"),n),i=Rr(o.asType(\"int32\"),n);return a.transpose().matMul(i).asType(\"int32\")}}),od=Object.freeze({confusionMatrix:rd});var ad=An({fromPixels_:function(t,e){if(void 0===e&&(e=3),e>4)throw new Error(\"Cannot construct Tensor with more than 4 channels from pixels.\");if(null==t)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");var n=!1,r=!1,o=!1,a=!1,i=!1;if(t.data instanceof Uint8Array)n=!0;else if(\"undefined\"!=typeof ImageData&&t instanceof ImageData)r=!0;else if(\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement)o=!0;else if(\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement)a=!0;else{if(null==t.getContext)throw new Error(\"pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was \"+t.constructor.name);i=!0}if(o){if(o&&t.readyState<2)throw new Error(\"The video element has not loaded data yet. 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Actual: \"+o+\". Expected: \"+a)}if(Array.isArray(t)&&Array.isArray(e)){var i=pn(t),s=pn(e);if(!S(i,s))throw new Error(\"Arrays have different shapes. Actual: [\"+i+\"]. Expected: [\"+s+\"]\")}var u=V(t)?t:I(t),c=V(e)?e:I(e);if(u.length!==c.length)throw new Error(\"Arrays have different lengths actual: \"+u.length+\" vs expected: \"+c.length+\".\\nActual: \"+u+\".\\nExpected: \"+c+\".\");for(var l=0;l<c.length;++l){var h=u[l],f=c[l];if(!n(h,f))throw new Error(\"Arrays differ: actual[\"+l+\"] = \"+h+\", expected[\"+l+\"] = \"+f+\".\\nActual: \"+u+\".\\nExpected: \"+c+\".\")}}function vd(t,e,n){return!isFinite(t)&&!isFinite(e)||!(isNaN(t)||isNaN(e)||Math.abs(t-e)>n)}var md=Object.freeze({TEST_EPSILON_FLOAT16:fd,expectArraysClose:function(t,e,n){return null==n&&(n=dd()),pd(t,e,(function(t,e){return vd(t,e,n)}))},testEpsilon:dd,expectPromiseToFail:function(t,e){t().then((function(){return e.fail()}),(function(){return e()}))},expectArraysEqual:function(t,e){var n=\"string\"==typeof e||\"number\"==typeof e||\"boolean\"==typeof e?[e]:e;return H(t)||H(t[0])||H(e)||H(e[0])?pd(t,n,(function(t,e){return t==e})):pd(t,e,(function(t,e){return vd(t,e,0)}))},expectNumbersClose:function(t,e,n){if(null==n&&(n=dd()),!vd(t,e,n))throw new Error(\"Numbers differ: actual === \"+t+\", expected === \"+e)},expectValuesInRange:function(t,e,n){for(var r=0;r<t.length;r++)if(t[r]<e||t[r]>n)throw new Error(\"Value out of range:\"+t[r]+\" low: \"+e+\", high: \"+n)},expectArrayBuffersEqual:function(t,e){expect(new Float32Array(t)).toEqual(new Float32Array(e))}}),gd=\"1.7.0\";var yd=Object.freeze({gpgpu_util:Mi,webgl_util:Ge,forceHalfFloat:function(){i().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)},MathBackendWebGL:Zs,setWebGLContext:Kt,GPGPUContext:Bi}),xd=function(t){function o(){return null!==t&&t.apply(this,arguments)||this}return e(o,t),o.prototype.minimize=function(t,e,n){void 0===e&&(e=!1);var r=this.computeGradients(t,n),o=r.value,a=r.grads;if(null!=n){var i=n.map((function(t){return{name:t.name,tensor:a[t.name]}}));this.applyGradients(i)}else this.applyGradients(a);return tn(a),e?o:(o.dispose(),null)},Object.defineProperty(o.prototype,\"iterations\",{get:function(){return null==this.iterations_&&(this.iterations_=0),this.iterations_},enumerable:!0,configurable:!0}),o.prototype.incrementIterations=function(){this.iterations_=this.iterations+1},o.prototype.computeGradients=function(t,e){return po(t,e)},o.prototype.dispose=function(){null!=this.iterations_&&tn(this.iterations_)},o.prototype.saveIterations=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){return null==this.iterations_&&(this.iterations_=0),[2,{name:\"iter\",tensor:On(this.iterations_,\"int32\")}]}))}))},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(t){throw new Error(\"setWeights() is not implemented for this optimizer class 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Xn(o).variable(!1)}))}),null==e.accumulatedUpdates[r]&&(e.accumulatedUpdates[r]={originalName:n+\"/accum_var\",variable:Ze((function(){return Xn(o).variable(!1)}))});var a=Array.isArray(t)?t[r].tensor:t[n];if(null!=a){var i=e.accumulatedGrads[r].variable,s=e.accumulatedUpdates[r].variable;Ze((function(){var t=i.mul(e.rho).add(a.square().mul(1-e.rho)),n=s.add(e.epsilon).sqrt().div(i.add(e.epsilon).sqrt()).mul(a),r=s.mul(e.rho).add(n.square().mul(1-e.rho));i.assign(t),s.assign(r);var u=n.mul(-e.learningRate).add(o);o.assign(u)}))}})),this.incrementIterations()},o.prototype.dispose=function(){null!=this.accumulatedUpdates&&(tn(this.accumulatedGrads.map((function(t){return t.variable}))),tn(this.accumulatedUpdates.map((function(t){return t.variable}))))},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){var t;return r(this,(function(e){switch(e.label){case 0:return t=this.accumulatedGrads.concat(this.accumulatedUpdates),[4,this.saveIterations()];case 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e=this;(Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t)).forEach((function(n,r){var o=Lt.registeredVariables[n];if(null==e.accumulatedGrads[r]){e.accumulatedGrads[r]={originalName:n+\"/accumulator\",variable:Ze((function(){return Hn(o.shape,e.initialAccumulatorValue).variable(!1)}))}}var a=Array.isArray(t)?t[r].tensor:t[n];if(null!=a){var i=e.accumulatedGrads[r].variable;Ze((function(){var t=i.add(a.square());i.assign(t);var n=a.div(t.add(Lt.backend.epsilon()).sqrt()).mul(-e.learningRate).add(o);o.assign(n)}))}})),this.incrementIterations()},o.prototype.dispose=function(){null!=this.accumulatedGrads&&tn(this.accumulatedGrads.map((function(t){return t.variable})))},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){switch(t.label){case 0:return[4,this.saveIterations()];case 1:return[2,[t.sent()].concat(this.accumulatedGrads.map((function(t){return{name:t.originalName,tensor:t.variable}})))]}}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(e){switch(e.label){case 0:return[4,this.extractIterations(t)];case 1:return t=e.sent(),!1,this.accumulatedGrads=t.map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),[2]}}))}))},o.prototype.getConfig=function(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},o.fromConfig=function(t,e){return new t(e.learningRate,e.initialAccumulatorValue)},o.className=\"Adagrad\",o}(xd);cd(wd);var Cd=function(t){function o(e,n,r,o){void 0===o&&(o=null);var a=t.call(this)||this;return a.learningRate=e,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment=[],a.accumulatedSecondMoment=[],Ze((function(){a.accBeta1=On(n).variable(),a.accBeta2=On(r).variable()})),null==o&&(a.epsilon=Lt.backend.epsilon()),a}return e(o,t),o.prototype.applyGradients=function(t){var e=this,n=Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t);Ze((function(){var r=Cc(1,e.accBeta1),o=Cc(1,e.accBeta2);n.forEach((function(n,a){var i=Lt.registeredVariables[n];null==e.accumulatedFirstMoment[a]&&(e.accumulatedFirstMoment[a]={originalName:n+\"/m\",variable:Ze((function(){return Xn(i).variable(!1)}))}),null==e.accumulatedSecondMoment[a]&&(e.accumulatedSecondMoment[a]={originalName:n+\"/v\",variable:Ze((function(){return Xn(i).variable(!1)}))});var s=Array.isArray(t)?t[a].tensor:t[n];if(null!=s){var u=e.accumulatedFirstMoment[a].variable,c=e.accumulatedSecondMoment[a].variable,l=u.mul(e.beta1).add(s.mul(1-e.beta1)),h=c.mul(e.beta2).add(s.square().mul(1-e.beta2)),f=l.div(r),d=h.div(o);u.assign(l),c.assign(h);var p=f.div(d.sqrt().add(e.epsilon)).mul(-e.learningRate).add(i);i.assign(p)}})),e.accBeta1.assign(e.accBeta1.mul(e.beta1)),e.accBeta2.assign(e.accBeta2.mul(e.beta2))})),this.incrementIterations()},o.prototype.dispose=function(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&tn(this.accumulatedFirstMoment.map((function(t){return t.variable}))),null!=this.accumulatedSecondMoment&&tn(this.accumulatedSecondMoment.map((function(t){return t.variable})))},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){var t;return r(this,(function(e){switch(e.label){case 0:return t=this.accumulatedFirstMoment.concat(this.accumulatedSecondMoment),[4,this.saveIterations()];case 1:return[2,[e.sent()].concat(t.map((function(t){return{name:t.originalName,tensor:t.variable}})))]}}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){var e,n=this;return r(this,(function(r){switch(r.label){case 0:return[4,this.extractIterations(t)];case 1:return t=r.sent(),Ze((function(){n.accBeta1.assign(xc(n.beta1,n.iterations_+1)),n.accBeta2.assign(xc(n.beta2,n.iterations_+1))})),e=t.length/2,!1,this.accumulatedFirstMoment=t.slice(0,e).map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),this.accumulatedSecondMoment=t.slice(e,2*e).map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),[2]}}))}))},o.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},o.fromConfig=function(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)},o.className=\"Adam\",o}(xd);cd(Cd);var Ed=function(t){function o(e,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=t.call(this)||this;return i.learningRate=e,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment=[],i.accumulatedWeightedInfNorm=[],Ze((function(){i.iteration=On(0).variable(),i.accBeta1=On(n).variable()})),null==o&&(i.epsilon=Lt.backend.epsilon()),i}return e(o,t),o.prototype.applyGradients=function(t){var e=this,n=Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t);Ze((function(){var r=Cc(1,e.accBeta1),o=sc(-e.learningRate,e.iteration.mul(e.decay).add(1));n.forEach((function(n,a){var i=Lt.registeredVariables[n];null==e.accumulatedFirstMoment[a]&&(e.accumulatedFirstMoment[a]={originalName:n+\"/m\",variable:Xn(i).variable(!1)}),null==e.accumulatedWeightedInfNorm[a]&&(e.accumulatedWeightedInfNorm[a]={originalName:n+\"/v\",variable:Xn(i).variable(!1)});var s=Array.isArray(t)?t[a].tensor:t[n];if(null!=s){var u=e.accumulatedFirstMoment[a].variable,c=e.accumulatedWeightedInfNorm[a].variable,l=u.mul(e.beta1).add(s.mul(1-e.beta1)),h=c.mul(e.beta2),f=s.abs(),d=h.maximum(f);u.assign(l),c.assign(d);var p=o.div(r).mul(l.div(d.add(e.epsilon))).add(i);i.assign(p)}})),e.iteration.assign(e.iteration.add(1)),e.accBeta1.assign(e.accBeta1.mul(e.beta1))})),this.incrementIterations()},o.prototype.dispose=function(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&tn(this.accumulatedFirstMoment.map((function(t){return t.variable}))),null!=this.accumulatedWeightedInfNorm&&tn(this.accumulatedWeightedInfNorm.map((function(t){return t.variable})))},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(t){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}))}))},o.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},o.fromConfig=function(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)},o.className=\"Adamax\",o}(xd);cd(Ed);var Rd=function(t){function o(e){var n=t.call(this)||this;return n.learningRate=e,n.setLearningRate(e),n}return e(o,t),o.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t)).forEach((function(n,r){var o=Array.isArray(t)?t[r].tensor:t[n];if(null!=o){var a=Lt.registeredVariables[n];Ze((function(){var t=e.c.mul(o).add(a);a.assign(t)}))}})),this.incrementIterations()},o.prototype.setLearningRate=function(t){this.learningRate=t,null!=this.c&&this.c.dispose(),this.c=en(On(-t))},o.prototype.dispose=function(){this.c.dispose()},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){switch(t.label){case 0:return[4,this.saveIterations()];case 1:return[2,[t.sent()]]}}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(e){switch(e.label){case 0:return[4,this.extractIterations(t)];case 1:if(0!==(t=e.sent()).length)throw new Error(\"SGD optimizer does not have settable weights.\");return[2]}}))}))},o.prototype.getConfig=function(){return{learningRate:this.learningRate}},o.fromConfig=function(t,e){return new t(e.learningRate)},o.className=\"SGD\",o}(xd);cd(Rd);var Id=function(t){function o(e,n,r){void 0===r&&(r=!1);var o=t.call(this,e)||this;return o.learningRate=e,o.momentum=n,o.useNesterov=r,o.accumulations=[],o.m=On(o.momentum),o}return e(o,t),o.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t)).forEach((function(n,r){var o=Lt.registeredVariables[n];if(null==e.accumulations[r]){e.accumulations[r]={originalName:n+\"/momentum\",variable:Ze((function(){return Xn(o).variable(!1)}))}}var a=e.accumulations[r].variable,i=Array.isArray(t)?t[r].tensor:t[n];null!=i&&Ze((function(){var t,n=e.m.mul(a).add(i);t=e.useNesterov?e.c.mul(i.add(n.mul(e.m))).add(o):e.c.mul(n).add(o),a.assign(n),o.assign(t)}))})),this.incrementIterations()},o.prototype.dispose=function(){this.m.dispose(),null!=this.accumulations&&tn(this.accumulations.map((function(t){return t.variable})))},o.prototype.setMomentum=function(t){this.momentum=t},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){return r(this,(function(t){switch(t.label){case 0:return[4,this.saveIterations()];case 1:return[2,[t.sent()].concat(this.accumulations.map((function(t){return{name:t.originalName,tensor:t.variable}})))]}}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){return r(this,(function(e){switch(e.label){case 0:return[4,this.extractIterations(t)];case 1:return t=e.sent(),!1,this.accumulations=t.map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),[2]}}))}))},o.prototype.getConfig=function(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},o.fromConfig=function(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)},o.className=\"Momentum\",o}(Rd);cd(Id);var kd=function(t){function o(e,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=t.call(this)||this;if(i.learningRate=e,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares=[],i.accumulatedMoments=[],i.accumulatedMeanGrads=[],i.centered=a,null==o&&(i.epsilon=Lt.backend.epsilon()),null==e)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\");return i}return e(o,t),o.prototype.applyGradients=function(t){var e=this;(Array.isArray(t)?t.map((function(t){return t.name})):Object.keys(t)).forEach((function(n,r){var o=Lt.registeredVariables[n];null==e.accumulatedMeanSquares[r]&&(e.accumulatedMeanSquares[r]={originalName:n+\"/rms\",variable:Ze((function(){return Xn(o).variable(!1)}))}),null==e.accumulatedMoments[r]&&(e.accumulatedMoments[r]={originalName:n+\"/momentum\",variable:Ze((function(){return Xn(o).variable(!1)}))}),null==e.accumulatedMeanGrads[r]&&e.centered&&(e.accumulatedMeanGrads[r]={originalName:n+\"/mg\",variable:Ze((function(){return Xn(o).variable(!1)}))});var a=Array.isArray(t)?t[r].tensor:t[n];if(null!=a){var i=e.accumulatedMeanSquares[r].variable,s=e.accumulatedMoments[r].variable;Ze((function(){var t=i.mul(e.decay).add(a.square().mul(1-e.decay));if(e.centered){var n=e.accumulatedMeanGrads[r].variable,u=n.mul(e.decay).add(a.mul(1-e.decay)),c=s.mul(e.momentum).add(a.mul(e.learningRate).div(t.sub(u.square().add(e.epsilon)).sqrt()));i.assign(t),n.assign(u),s.assign(c);var l=o.sub(c);o.assign(l)}else{var h=i.mul(e.decay).add(a.square().mul(1-e.decay));c=s.mul(e.momentum).add(a.mul(e.learningRate).div(h.add(e.epsilon).sqrt()));i.assign(h),s.assign(c);l=o.sub(c);o.assign(l)}}))}})),this.incrementIterations()},o.prototype.dispose=function(){null!=this.accumulatedMeanSquares&&tn(this.accumulatedMeanSquares.map((function(t){return t.variable}))),null!=this.accumulatedMeanGrads&&this.centered&&tn(this.accumulatedMeanGrads.map((function(t){return t.variable}))),null!=this.accumulatedMoments&&tn(this.accumulatedMoments.map((function(t){return t.variable})))},o.prototype.getWeights=function(){return n(this,void 0,void 0,(function(){var t;return r(this,(function(e){switch(e.label){case 0:return t=this.accumulatedMeanSquares.concat(this.accumulatedMoments),this.centered&&t.push.apply(t,this.accumulatedMeanGrads),[4,this.saveIterations()];case 1:return[2,[e.sent()].concat(t.map((function(t){return{name:t.originalName,tensor:t.variable}})))]}}))}))},o.prototype.setWeights=function(t){return n(this,void 0,void 0,(function(){var e;return r(this,(function(n){switch(n.label){case 0:return[4,this.extractIterations(t)];case 1:return t=n.sent(),e=this.centered?t.length/3:t.length/2,!1,this.accumulatedMeanSquares=t.slice(0,e).map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),this.accumulatedMoments=t.slice(e,2*e).map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}})),this.centered&&(this.accumulatedMeanGrads=t.slice(2*e,3*e).map((function(t){return{originalName:t.name,variable:t.tensor.variable(!1)}}))),[2]}}))}))},o.prototype.getConfig=function(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},o.fromConfig=function(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)},o.className=\"RMSProp\",o}(xd);cd(kd);var Sd=function(){function t(){}return t.sgd=function(t){return new Rd(t)},t.momentum=function(t,e,n){return void 0===n&&(n=!1),new Id(t,e,n)},t.rmsprop=function(t,e,n,r,o){return void 0===e&&(e=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new kd(t,e,n,r,o)},t.adam=function(t,e,n,r){return void 0===t&&(t=.001),void 0===e&&(e=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new Cd(t,e,n,r)},t.adadelta=function(t,e,n){return void 0===t&&(t=.001),void 0===e&&(e=.95),void 0===n&&(n=null),new bd(t,e,n)},t.adamax=function(t,e,n,r,o){return void 0===t&&(t=.002),void 0===e&&(e=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new Ed(t,e,n,r,o)},t.adagrad=function(t,e){return void 0===e&&(e=.1),new wd(t,e)},t}(),Ad={sgd:Sd.sgd,momentum:Sd.momentum,adadelta:Sd.adadelta,adagrad:Sd.adagrad,rmsprop:Sd.rmsprop,adamax:Sd.adamax,adam:Sd.adam},Dd=\"undefined\"!=typeof requestAnimationFrame?requestAnimationFrame:\"undefined\"!=typeof setImmediate?setImmediate:function(t){return t()};function Td(){return new Promise((function(t){return Dd((function(){return t()}))}))}wt.prototype.squaredDifference=function(t){return nu(this,t)},xt=Gh;export{bd as AdadeltaOptimizer,wd as AdagradOptimizer,Cd as AdamOptimizer,Ed as AdamaxOptimizer,xo as DataStorage,s as ENV,o as Environment,bo as KernelBackend,Id as MomentumOptimizer,xd as Optimizer,kd as RMSPropOptimizer,Ct as Rank,ch as Reduction,Rd as SGDOptimizer,wt as Tensor,gt as TensorBuffer,St as Variable,ru as abs,ou as acos,au as acosh,rc as add,oc as addN,ac as addStrict,Cl as all,El as any,Rl as argMax,Il as argMin,iu as asin,su as asinh,uu as atan,ic as atan2,cu as atanh,fl as avgPool,vl as avgPool3d,hn as backend,Uo as backend_util,zl as basicLSTMCell,ju as batchNorm,Xu as batchNorm2d,Yu as batchNorm3d,$u as batchNorm4d,Ku as batchNormalization,Gu as batchNormalization2d,Hu as batchNormalization3d,qu as batchNormalization4d,vr as batchToSpaceND,Uc as booleanMaskAsync,mr as broadcastTo,id as browser,dr as buffer,gr as cast,lu as ceil,hu as clipByValue,yr as clone,Dn as complex,Yn as concat,$n as concat1d,Qn as concat2d,Jn as concat3d,Zn as concat4d,Hc as conv1d,qc as conv2d,Zc as conv2dTranspose,Kc as conv3d,tl as conv3dTranspose,fu as cos,du as cosh,xr as cumsum,vo as customGrad,Xe as deprecationWarn,br as depthToSpace,Yc as depthwiseConv2d,eh as diag,je as disableDeprecationWarnings,tn as dispose,Ye as disposeVariables,sc as div,uc as divNoNan,cc as divStrict,nl as dot,nh as dropout,_l as elu,Ke as enableDebugMode,qe as enableProdMode,$e as engine,i as env,Rc as equal,Ic as equalStrict,pu as erf,vu as exp,wr as expandDims,mu as expm1,Cr as eye,Xl as fft,Hn as fill,un as findBackend,cn as findBackendFactory,gu as floor,lc as floorDiv,ih as frame,zh as fused,Lc as gather,th as gatherND,Xr as gather_util,an as getBackend,h as getGradient,l as getKernel,f as getKernelsForBackend,co as grad,lo as grads,kc as greater,Sc as greaterEqual,Ac as greaterEqualStrict,Dc as greaterStrict,ah as hammingWindow,oh as hannWindow,Yl as ifft,Nn as imag,Oh as image,lh as inTopKAsync,ed as io,Ql as irfft,Du as isFinite,Au as isInf,Su as isNaN,en as keep,Ol as leakyRelu,Tc as less,Nc as lessEqual,Fc as lessEqualStrict,_c as lessStrict,Ih as linalg,qn as linspace,Ul as localResponseNormalization,yu as log,xu as log1p,bu as logSigmoid,yo as logSoftmax,kl as logSumExp,Qu as logicalAnd,Ju as logicalNot,Zu as logicalOr,tc as logicalXor,bh as losses,el as matMul,od as math,Sl as max,hl as maxPool,pl as maxPool3d,hc as maximum,fc as maximumStrict,Al as mean,Qe as memory,Dl as min,dc as minimum,pc as minimumStrict,vc as mod,mc as modStrict,Tl as moments,Hl as movingAverage,gc as mul,yc as mulStrict,Gl as multiRNNCell,Er as multinomial,wu as neg,Td as nextFrame,Vl as norm,Oc as notEqual,Mc as notEqualStrict,Rr as oneHot,zn as ones,jn as onesLike,An as op,rl as outerProduct,Ir as pad,kr as pad1d,Sr as pad2d,Ar as pad3d,Dr as pad4d,dl as pool,xc as pow,bc as powStrict,Ml as prelu,pr as print,Fl as prod,Je as profile,Tr as rand,Fr as randomGamma,Nr as randomNormal,_r as randomUniform,Kn as range,on as ready,Tn as real,Cu as reciprocal,ln as registerBackend,p as registerGradient,d as registerKernel,Bl as relu,Pl as relu6,sn as removeBackend,Or as reshape,ol as reverse,al as reverse1d,il as reverse2d,sl as reverse3d,ul as reverse4d,$l as rfft,Eu as round,Ru as rsqrt,On as scalar,jl as scatterND,to as scatter_util,Ll as selu,Jc as separableConv2d,ld as serialization,rn as setBackend,fn as setPlatform,Vr as setdiff1dAsync,Iu as sigmoid,ku as sign,uh as signal,Tu as sin,Nu as sinh,ml as slice,gl as slice1d,yl as slice2d,xl as slice3d,bl as slice4d,uo as slice_util,go as softmax,Fu as softplus,Mr as spaceToBatchND,Zl as sparseToDense,Jl as spectral,tr as split,_u as sqrt,tu as square,nu as squaredDifference,wc as squaredDifferenceStrict,Br as squeeze,Pr as stack,Ou as step,sh as stft,ql as stridedSlice,Cc as sub,Ec as subStrict,Nl as sum,Tt as sumOutType,Mu as tan,Bu as tanh,Fn as tensor,Mn as tensor1d,Bn as tensor2d,Pn as tensor3d,Ln as tensor4d,Wn as tensor5d,Un as tensor6d,Mt as tensor_util,md as test_util,Ze as tidy,Lr as tile,nn as time,Kl as topk,Ad as train,Wl as transpose,Wr as truncatedNormal,m as unregisterGradient,v as unregisterKernel,Wc as unsortedSegmentSum,Ur as unstack,st as util,ho as valueAndGrad,fo as valueAndGrads,Vn as variable,po as variableGrads,gd as version_core,yd as webgl,ec as where,nc as whereAsync,Gn as zeros,Xn as zerosLike};\n//# sourceMappingURL=tf-core.esm.js.map\n","import { Point } from '../classes';\r\n\r\nexport function drawContour(\r\n ctx: CanvasRenderingContext2D,\r\n points: Point[],\r\n isClosed: boolean = false\r\n) {\r\n ctx.beginPath()\r\n\r\n points.slice(1).forEach(({ x, y }, prevIdx) => {\r\n const from = points[prevIdx]\r\n ctx.moveTo(from.x, from.y)\r\n ctx.lineTo(x, y)\r\n })\r\n\r\n if (isClosed) {\r\n const from = points[points.length - 1]\r\n const to = points[0]\r\n if (!from || !to) {\r\n return\r\n }\r\n\r\n ctx.moveTo(from.x, from.y)\r\n ctx.lineTo(to.x, to.y)\r\n }\r\n\r\n ctx.stroke()\r\n}","import { isValidNumber } from '../utils';\r\n\r\nexport interface IDimensions {\r\n width: number\r\n height: number\r\n}\r\n\r\nexport class Dimensions implements IDimensions {\r\n\r\n private _width: number\r\n private _height: number\r\n\r\n constructor(width: number, height: number) {\r\n if (!isValidNumber(width) || !isValidNumber(height)) {\r\n throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)\r\n }\r\n\r\n this._width = width\r\n this._height = height\r\n }\r\n\r\n public get width(): number { return this._width }\r\n public get height(): number { return this._height }\r\n\r\n public reverse(): Dimensions {\r\n return new Dimensions(1 / this.width, 1 / this.height)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Point } from '../classes';\r\nimport { Dimensions, IDimensions } from '../classes/Dimensions';\r\n\r\nexport function isTensor(tensor: any, dim: number) {\r\n return tensor instanceof tf.Tensor && tensor.shape.length === dim\r\n}\r\n\r\nexport function isTensor1D(tensor: any): tensor is tf.Tensor1D {\r\n return isTensor(tensor, 1)\r\n}\r\n\r\nexport function isTensor2D(tensor: any): tensor is tf.Tensor2D {\r\n return isTensor(tensor, 2)\r\n}\r\n\r\nexport function isTensor3D(tensor: any): tensor is tf.Tensor3D {\r\n return isTensor(tensor, 3)\r\n}\r\n\r\nexport function isTensor4D(tensor: any): tensor is tf.Tensor4D {\r\n return isTensor(tensor, 4)\r\n}\r\n\r\nexport function isFloat(num: number) {\r\n return num % 1 !== 0\r\n}\r\n\r\nexport function isEven(num: number) {\r\n return num % 2 === 0\r\n}\r\n\r\nexport function round(num: number, prec: number = 2) {\r\n const f = Math.pow(10, prec)\r\n return Math.floor(num * f) / f\r\n}\r\n\r\nexport function isDimensions(obj: any): boolean {\r\n return obj && obj.width && obj.height\r\n}\r\n\r\nexport function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {\r\n const scale = inputSize / Math.max(height, width)\r\n return new Dimensions(Math.round(width * scale), Math.round(height * scale))\r\n}\r\n\r\nexport function getCenterPoint(pts: Point[]): Point {\r\n return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))\r\n .div(new Point(pts.length, pts.length))\r\n}\r\n\r\nexport function range(num: number, start: number, step: number): number[] {\r\n return Array(num).fill(0).map((_, i) => start + (i * step))\r\n}\r\n\r\nexport function isValidNumber(num: any) {\r\n return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0\r\n}\r\n\r\nexport function isValidProbablitiy(num: any) {\r\n return isValidNumber(num) && 0 <= num && num <= 1.0\r\n}","export interface IPoint {\r\n x: number\r\n y: number\r\n}\r\n\r\nexport class Point implements IPoint {\r\n private _x: number\r\n private _y: number\r\n\r\n constructor(x: number, y: number) {\r\n this._x = x\r\n this._y = y\r\n }\r\n\r\n get x(): number { return this._x }\r\n get y(): number { return this._y }\r\n\r\n public add(pt: IPoint): Point {\r\n return new Point(this.x + pt.x, this.y + pt.y)\r\n }\r\n\r\n public sub(pt: IPoint): Point {\r\n return new Point(this.x - pt.x, this.y - pt.y)\r\n }\r\n\r\n public mul(pt: IPoint): Point {\r\n return new Point(this.x * pt.x, this.y * pt.y)\r\n }\r\n\r\n public div(pt: IPoint): Point {\r\n return new Point(this.x / pt.x, this.y / pt.y)\r\n }\r\n\r\n public abs(): Point {\r\n return new Point(Math.abs(this.x), Math.abs(this.y))\r\n }\r\n\r\n public magnitude(): number {\r\n return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))\r\n }\r\n\r\n public floor(): Point {\r\n return new Point(Math.floor(this.x), Math.floor(this.y))\r\n }\r\n}","import { isDimensions, isValidNumber } from '../utils';\r\nimport { IBoundingBox } from './BoundingBox';\r\nimport { IDimensions } from './Dimensions';\r\nimport { Point } from './Point';\r\nimport { IRect } from './Rect';\r\n\r\nexport class Box<BoxType = any> implements IBoundingBox, IRect {\r\n\r\n public static isRect(rect: any): boolean {\r\n return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)\r\n }\r\n\r\n public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {\r\n if (!Box.isRect(box)) {\r\n throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)\r\n }\r\n\r\n if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\r\n throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)\r\n }\r\n }\r\n\r\n private _x: number\r\n private _y: number\r\n private _width: number\r\n private _height: number\r\n\r\n constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = true) {\r\n const box = (_box || {}) as any\r\n\r\n const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)\r\n const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)\r\n\r\n if (!isRect && !isBbox) {\r\n throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)\r\n }\r\n\r\n const [x, y, width, height] = isRect\r\n ? [box.x, box.y, box.width, box.height]\r\n : [box.left, box.top, box.right - box.left, box.bottom - box.top]\r\n\r\n Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)\r\n\r\n this._x = x\r\n this._y = y\r\n this._width = width\r\n this._height = height\r\n }\r\n\r\n public get x(): number { return this._x }\r\n public get y(): number { return this._y }\r\n public get width(): number { return this._width }\r\n public get height(): number { return this._height }\r\n public get left(): number { return this.x }\r\n public get top(): number { return this.y }\r\n public get right(): number { return this.x + this.width }\r\n public get bottom(): number { return this.y + this.height }\r\n public get area(): number { return this.width * this.height }\r\n public get topLeft(): Point { return new Point(this.left, this.top) }\r\n public get topRight(): Point { return new Point(this.right, this.top) }\r\n public get bottomLeft(): Point { return new Point(this.left, this.bottom) }\r\n public get bottomRight(): Point { return new Point(this.right, this.bottom) }\r\n\r\n public round(): Box<BoxType> {\r\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\r\n .map(val => Math.round(val))\r\n return new Box({ x, y, width, height })\r\n }\r\n\r\n public floor(): Box<BoxType> {\r\n const [x, y, width, height] = [this.x, this.y, this.width, this.height]\r\n .map(val => Math.floor(val))\r\n return new Box({ x, y, width, height })\r\n }\r\n\r\n public toSquare(): Box<BoxType> {\r\n let { x, y, width, height } = this\r\n const diff = Math.abs(width - height)\r\n if (width < height) {\r\n x -= (diff / 2)\r\n width += diff\r\n }\r\n if (height < width) {\r\n y -= (diff / 2)\r\n height += diff\r\n }\r\n\r\n return new Box({ x, y, width, height })\r\n }\r\n\r\n public rescale(s: IDimensions | number): Box<BoxType> {\r\n const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number\r\n const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number\r\n return new Box({\r\n x: this.x * scaleX,\r\n y: this.y * scaleY,\r\n width: this.width * scaleX,\r\n height: this.height * scaleY\r\n })\r\n }\r\n\r\n public pad(padX: number, padY: number): Box<BoxType> {\r\n let [x, y, width, height] = [\r\n this.x - (padX / 2),\r\n this.y - (padY / 2),\r\n this.width + padX,\r\n this.height + padY\r\n ]\r\n return new Box({ x, y, width, height })\r\n }\r\n\r\n public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {\r\n const { x, y, right, bottom } = this\r\n const clippedX = Math.max(x, 0)\r\n const clippedY = Math.max(y, 0)\r\n\r\n const newWidth = right - clippedX\r\n const newHeight = bottom - clippedY\r\n const clippedWidth = Math.min(newWidth, imgWidth - clippedX)\r\n const clippedHeight = Math.min(newHeight, imgHeight - clippedY)\r\n\r\n return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()\r\n }\r\n\r\n public shift(sx: number, sy: number): Box<BoxType> {\r\n const { width, height } = this\r\n const x = this.x + sx\r\n const y = this.y + sy\r\n\r\n return new Box({ x, y, width, height })\r\n }\r\n\r\n public padAtBorders(imageHeight: number, imageWidth: number) {\r\n const w = this.width + 1\r\n const h = this.height + 1\r\n\r\n let dx = 1\r\n let dy = 1\r\n let edx = w\r\n let edy = h\r\n\r\n let x = this.left\r\n let y = this.top\r\n let ex = this.right\r\n let ey = this.bottom\r\n\r\n if (ex > imageWidth) {\r\n edx = -ex + imageWidth + w\r\n ex = imageWidth\r\n }\r\n if (ey > imageHeight) {\r\n edy = -ey + imageHeight + h\r\n ey = imageHeight\r\n }\r\n if (x < 1) {\r\n edy = 2 - x\r\n x = 1\r\n }\r\n if (y < 1) {\r\n edy = 2 - y\r\n y = 1\r\n }\r\n\r\n return { dy, edy, dx, edx, y, ey, x, ex, w, h }\r\n }\r\n\r\n public calibrate(region: Box) {\r\n return new Box({\r\n left: this.left + (region.left * this.width),\r\n top: this.top + (region.top * this.height),\r\n right: this.right + (region.right * this.width),\r\n bottom: this.bottom + (region.bottom * this.height)\r\n }).toSquare().round()\r\n }\r\n}","import { Box } from './Box';\r\n\r\nexport interface IBoundingBox {\r\n left: number\r\n top: number\r\n right: number\r\n bottom: number\r\n}\r\n\r\nexport class BoundingBox extends Box<BoundingBox> implements IBoundingBox {\r\n constructor(left: number, top: number, right: number, bottom: number, allowNegativeDimensions: boolean = false) {\r\n super({ left, top, right, bottom }, allowNegativeDimensions)\r\n }\r\n}","import { Box } from './Box';\r\nimport { Dimensions, IDimensions } from './Dimensions';\r\nimport { IRect, Rect } from './Rect';\r\n\r\nexport class ObjectDetection {\r\n private _score: number\r\n private _classScore: number\r\n private _className: string\r\n private _box: Rect\r\n private _imageDims: Dimensions\r\n\r\n constructor(\r\n score: number,\r\n classScore: number,\r\n className: string,\r\n relativeBox: IRect,\r\n imageDims: IDimensions\r\n ) {\r\n this._imageDims = new Dimensions(imageDims.width, imageDims.height)\r\n this._score = score\r\n this._classScore = classScore\r\n this._className = className\r\n this._box = new Box(relativeBox).rescale(this._imageDims)\r\n }\r\n\r\n public get score(): number { return this._score }\r\n public get classScore(): number { return this._classScore }\r\n public get className(): string { return this._className }\r\n public get box(): Box { return this._box }\r\n public get imageDims(): Dimensions { return this._imageDims }\r\n public get imageWidth(): number { return this.imageDims.width }\r\n public get imageHeight(): number { return this.imageDims.height }\r\n public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }\r\n\r\n public forSize(width: number, height: number): ObjectDetection {\r\n return new ObjectDetection(\r\n this.score,\r\n this.classScore,\r\n this.className,\r\n this.relativeBox,\r\n { width, height}\r\n )\r\n }\r\n}","import { Box } from './Box';\r\nimport { IDimensions } from './Dimensions';\r\nimport { ObjectDetection } from './ObjectDetection';\r\nimport { Rect } from './Rect';\r\n\r\nexport interface IFaceDetecion {\r\n score: number\r\n box: Box\r\n}\r\n\r\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\r\n constructor(\r\n score: number,\r\n relativeBox: Rect,\r\n imageDims: IDimensions\r\n ) {\r\n super(score, score, '', relativeBox, imageDims)\r\n }\r\n\r\n public forSize(width: number, height: number): FaceDetection {\r\n const { score, relativeBox, imageDims } = super.forSize(width, height)\r\n return new FaceDetection(score, relativeBox, imageDims)\r\n }\r\n}","import { Box } from '../classes/Box';\r\n\r\nexport function iou(box1: Box, box2: Box, isIOU: boolean = true) {\r\n const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))\r\n const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))\r\n const interSection = width * height\r\n\r\n return isIOU\r\n ? interSection / (box1.area + box2.area - interSection)\r\n : interSection / Math.min(box1.area, box2.area)\r\n}","import { BoundingBox, IPoint } from '../classes';\r\n\r\nexport function minBbox(pts: IPoint[]): BoundingBox {\r\n const xs = pts.map(pt => pt.x)\r\n const ys = pts.map(pt => pt.y)\r\n const minX = xs.reduce((min, x) => x < min ? x : min, Infinity)\r\n const minY = ys.reduce((min, y) => y < min ? y : min, Infinity)\r\n const maxX = xs.reduce((max, x) => max < x ? x : max, 0)\r\n const maxY = ys.reduce((max, y) => max < y ? y : max, 0)\r\n\r\n return new BoundingBox(minX, minY, maxX, maxY)\r\n}\r\n","import { Box } from '../classes/Box';\r\nimport { iou } from './iou';\r\n\r\nexport function nonMaxSuppression(\r\n boxes: Box[],\r\n scores: number[],\r\n iouThreshold: number,\r\n isIOU: boolean = true\r\n): number[] {\r\n\r\n let indicesSortedByScore = scores\r\n .map((score, boxIndex) => ({ score, boxIndex }))\r\n .sort((c1, c2) => c1.score - c2.score)\r\n .map(c => c.boxIndex)\r\n\r\n const pick: number[] = []\r\n\r\n while(indicesSortedByScore.length > 0) {\r\n const curr = indicesSortedByScore.pop() as number\r\n pick.push(curr)\r\n\r\n const indices = indicesSortedByScore\r\n\r\n const outputs: number[] = []\r\n for (let i = 0; i < indices.length; i++) {\r\n const idx = indices[i]\r\n\r\n const currBox = boxes[curr]\r\n const idxBox = boxes[idx]\r\n\r\n outputs.push(iou(currBox, idxBox, isIOU))\r\n }\r\n\r\n indicesSortedByScore = indicesSortedByScore.filter(\r\n (_, j) => outputs[j] <= iouThreshold\r\n )\r\n }\r\n\r\n return pick\r\n\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n const [r, g, b] = meanRgb\r\n const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)\r\n const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)\r\n const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)\r\n const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)\r\n\r\n return tf.sub(x, avg_rgb)\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\n/**\r\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\r\n *\r\n * @param imgTensor The image tensor.\r\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n * both sides of the minor dimension oof the image.\r\n * @returns The padded tensor with width === height.\r\n */\r\nexport function padToSquare(\r\n imgTensor: tf.Tensor4D,\r\n isCenterImage: boolean = false\r\n): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n\r\n const [height, width] = imgTensor.shape.slice(1)\r\n if (height === width) {\r\n return imgTensor\r\n }\r\n\r\n const dimDiff = Math.abs(height - width)\r\n const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))\r\n const paddingAxis = height > width ? 2 : 1\r\n\r\n const createPaddingTensor = (paddingAmount: number): tf.Tensor => {\r\n const paddingTensorShape = imgTensor.shape.slice()\r\n paddingTensorShape[paddingAxis] = paddingAmount\r\n return tf.fill(paddingTensorShape, 0)\r\n }\r\n\r\n const paddingTensorAppend = createPaddingTensor(paddingAmount)\r\n const remainingPaddingAmount = dimDiff - (paddingTensorAppend.shape[paddingAxis] as number)\r\n\r\n const paddingTensorPrepend = isCenterImage && remainingPaddingAmount\r\n ? createPaddingTensor(remainingPaddingAmount)\r\n : null\r\n\r\n const tensorsToStack = [\r\n paddingTensorPrepend,\r\n imgTensor,\r\n paddingTensorAppend\r\n ]\r\n .filter(t => !!t)\r\n .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]\r\n return tf.concat(tensorsToStack, paddingAxis)\r\n })\r\n}","export function shuffleArray(inputArray: any[]) {\r\n const array = inputArray.slice()\r\n for (let i = array.length - 1; i > 0; i--) {\r\n const j = Math.floor(Math.random() * (i + 1))\r\n const x = array[i]\r\n array[i] = array[j]\r\n array[j] = x\r\n }\r\n return array\r\n}","export * from './iou'\r\nexport * from './minBbox'\r\nexport * from './nonMaxSuppression'\r\nexport * from './normalize'\r\nexport * from './padToSquare'\r\nexport * from './shuffleArray'\r\n\r\nexport function sigmoid(x: number) {\r\n return 1 / (1 + Math.exp(-x))\r\n}\r\n\r\nexport function inverseSigmoid(x: number) {\r\n return Math.log(x / (1 - x))\r\n}","import { Box } from './Box';\r\n\r\nexport interface IRect {\r\n x: number\r\n y: number\r\n width: number\r\n height: number\r\n}\r\n\r\nexport class Rect extends Box<Rect> implements IRect {\r\n constructor(x: number, y: number, width: number, height: number, allowNegativeDimensions: boolean = false) {\r\n super({ x, y, width, height }, allowNegativeDimensions)\r\n }\r\n}","import { minBbox } from '../ops';\r\nimport { getCenterPoint } from '../utils';\r\nimport { IBoundingBox } from './BoundingBox';\r\nimport { Box } from './Box';\r\nimport { Dimensions, IDimensions } from './Dimensions';\r\nimport { FaceDetection } from './FaceDetection';\r\nimport { Point } from './Point';\r\nimport { IRect, Rect } from './Rect';\r\n\r\n// face alignment constants\r\nconst relX = 0.5\r\nconst relY = 0.43\r\nconst relScale = 0.45\r\n\r\nexport interface IFaceLandmarks {\r\n positions: Point[]\r\n shift: Point\r\n}\r\n\r\nexport class FaceLandmarks implements IFaceLandmarks {\r\n protected _shift: Point\r\n protected _positions: Point[]\r\n protected _imgDims: Dimensions\r\n\r\n constructor(\r\n relativeFaceLandmarkPositions: Point[],\r\n imgDims: IDimensions,\r\n shift: Point = new Point(0, 0)\r\n ) {\r\n const { width, height } = imgDims\r\n this._imgDims = new Dimensions(width, height)\r\n this._shift = shift\r\n this._positions = relativeFaceLandmarkPositions.map(\r\n pt => pt.mul(new Point(width, height)).add(shift)\r\n )\r\n }\r\n\r\n public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\r\n public get imageWidth(): number { return this._imgDims.width }\r\n public get imageHeight(): number { return this._imgDims.height }\r\n public get positions(): Point[] { return this._positions }\r\n public get relativePositions(): Point[] {\r\n return this._positions.map(\r\n pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\r\n )\r\n }\r\n\r\n public forSize<T extends FaceLandmarks>(width: number, height: number): T {\r\n return new (this.constructor as any)(\r\n this.relativePositions,\r\n { width, height }\r\n )\r\n }\r\n\r\n public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\r\n return new (this.constructor as any)(\r\n this.relativePositions,\r\n this._imgDims,\r\n new Point(x, y)\r\n )\r\n }\r\n\r\n public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\r\n return this.shiftBy(pt.x, pt.y)\r\n }\r\n\r\n /**\r\n * Aligns the face landmarks after face detection from the relative positions of the faces\r\n * bounding box, or it's current shift. This function should be used to align the face images\r\n * after face detection has been performed, before they are passed to the face recognition net.\r\n * This will make the computed face descriptor more accurate.\r\n *\r\n * @param detection (optional) The bounding box of the face or the face detection result. If\r\n * no argument was passed the position of the face landmarks are assumed to be relative to\r\n * it's current shift.\r\n * @returns The bounding box of the aligned face.\r\n */\r\n public align(\r\n detection?: FaceDetection | IRect | IBoundingBox | null,\r\n options: { useDlibAlignment?: boolean, minBoxPadding?: number } = { }\r\n ): Box {\r\n if (detection) {\r\n const box = detection instanceof FaceDetection\r\n ? detection.box.floor()\r\n : new Box(detection)\r\n\r\n return this.shiftBy(box.x, box.y).align(null, options)\r\n }\r\n\r\n const { useDlibAlignment, minBoxPadding } = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options)\r\n\r\n if (useDlibAlignment) {\r\n return this.alignDlib()\r\n }\r\n\r\n return this.alignMinBbox(minBoxPadding)\r\n }\r\n\r\n private alignDlib(): Box {\r\n\r\n const centers = this.getRefPointsForAlignment()\r\n\r\n const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\r\n const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\r\n const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\r\n\r\n const size = Math.floor(eyeToMouthDist / relScale)\r\n\r\n const refPoint = getCenterPoint(centers)\r\n // TODO: pad in case rectangle is out of image bounds\r\n const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\r\n const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\r\n\r\n return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\r\n }\r\n\r\n private alignMinBbox(padding: number): Box {\r\n const box = minBbox(this.positions)\r\n return box.pad(box.width * padding, box.height * padding)\r\n }\r\n\r\n protected getRefPointsForAlignment(): Point[] {\r\n throw new Error('getRefPointsForAlignment not implemented by base class')\r\n }\r\n}","import { getCenterPoint } from '../utils';\r\nimport { FaceLandmarks } from './FaceLandmarks';\r\nimport { Point } from './Point';\r\n\r\n\r\nexport class FaceLandmarks5 extends FaceLandmarks {\r\n\r\n protected getRefPointsForAlignment(): Point[] {\r\n const pts = this.positions\r\n return [\r\n pts[0],\r\n pts[1],\r\n getCenterPoint([pts[3], pts[4]])\r\n ]\r\n }\r\n}","import { getCenterPoint } from '../utils';\r\nimport { FaceLandmarks } from './FaceLandmarks';\r\nimport { Point } from './Point';\r\n\r\nexport class FaceLandmarks68 extends FaceLandmarks {\r\n public getJawOutline(): Point[] {\r\n return this.positions.slice(0, 17)\r\n }\r\n\r\n public getLeftEyeBrow(): Point[] {\r\n return this.positions.slice(17, 22)\r\n }\r\n\r\n public getRightEyeBrow(): Point[] {\r\n return this.positions.slice(22, 27)\r\n }\r\n\r\n public getNose(): Point[] {\r\n return this.positions.slice(27, 36)\r\n }\r\n\r\n public getLeftEye(): Point[] {\r\n return this.positions.slice(36, 42)\r\n }\r\n\r\n public getRightEye(): Point[] {\r\n return this.positions.slice(42, 48)\r\n }\r\n\r\n public getMouth(): Point[] {\r\n return this.positions.slice(48, 68)\r\n }\r\n\r\n protected getRefPointsForAlignment(): Point[] {\r\n return [\r\n this.getLeftEye(),\r\n this.getRightEye(),\r\n this.getMouth()\r\n ].map(getCenterPoint)\r\n }\r\n}","import { round } from '../utils';\r\n\r\nexport interface IFaceMatch {\r\n label: string\r\n distance: number\r\n}\r\n\r\nexport class FaceMatch implements IFaceMatch {\r\n private _label: string\r\n private _distance: number\r\n\r\n constructor(label: string, distance: number) {\r\n this._label = label\r\n this._distance = distance\r\n }\r\n\r\n public get label(): string { return this._label }\r\n public get distance(): number { return this._distance }\r\n\r\n public toString(withDistance: boolean = true): string {\r\n return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\r\n }\r\n}","import { isValidNumber } from '../utils';\r\nimport { IBoundingBox } from './BoundingBox';\r\nimport { Box } from './Box';\r\nimport { IRect } from './Rect';\r\n\r\nexport class LabeledBox extends Box<LabeledBox> {\r\n\r\n public static assertIsValidLabeledBox(box: any, callee: string) {\r\n Box.assertIsValidBox(box, callee)\r\n\r\n if (!isValidNumber(box.label)) {\r\n throw new Error(`${callee} - expected property label (${box.label}) to be a number`)\r\n }\r\n }\r\n\r\n private _label: number\r\n\r\n constructor(box: IBoundingBox | IRect | any, label: number) {\r\n super(box)\r\n this._label = label\r\n }\r\n\r\n public get label(): number { return this._label }\r\n\r\n}","export class LabeledFaceDescriptors {\r\n private _label: string\r\n private _descriptors: Float32Array[]\r\n\r\n constructor(label: string, descriptors: Float32Array[]) {\r\n if (!(typeof label === 'string')) {\r\n throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\r\n }\r\n\r\n if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\r\n throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\r\n }\r\n\r\n this._label = label\r\n this._descriptors = descriptors\r\n }\r\n\r\n public get label(): string { return this._label }\r\n public get descriptors(): Float32Array[] { return this._descriptors }\r\n\r\n public toJSON(): any {\r\n return {\r\n label: this.label,\r\n descriptors: this.descriptors.map((d) => Array.from(d))\r\n };\r\n }\r\n\r\n public static fromJSON(json: any): LabeledFaceDescriptors {\r\n const descriptors = json.descriptors.map((d: any) => {\r\n return new Float32Array(d);\r\n });\r\n return new LabeledFaceDescriptors(json.label, descriptors);\r\n }\r\n\r\n}","import { isValidProbablitiy } from '../utils';\r\nimport { IBoundingBox } from './BoundingBox';\r\nimport { LabeledBox } from './LabeledBox';\r\nimport { IRect } from './Rect';\r\n\r\nexport class PredictedBox extends LabeledBox {\r\n\r\n public static assertIsValidPredictedBox(box: any, callee: string) {\r\n LabeledBox.assertIsValidLabeledBox(box, callee)\r\n\r\n if (\r\n !isValidProbablitiy(box.score)\r\n || !isValidProbablitiy(box.classScore)\r\n ) {\r\n throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)\r\n }\r\n }\r\n\r\n private _score: number\r\n private _classScore: number\r\n\r\n constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {\r\n super(box, label)\r\n this._score = score\r\n this._classScore = classScore\r\n }\r\n\r\n public get score(): number { return this._score }\r\n public get classScore(): number { return this._classScore }\r\n\r\n}","import { FaceDetection } from '../classes/FaceDetection';\r\n\r\nexport type WithFaceDetection<TSource> = TSource & {\r\n detection: FaceDetection\r\n}\r\n\r\nexport function isWithFaceDetection(obj: any): obj is WithFaceDetection<{}> {\r\n return obj['detection'] instanceof FaceDetection\r\n}\r\n\r\nexport function extendWithFaceDetection<\r\n TSource\r\n> (\r\n sourceObj: TSource,\r\n detection: FaceDetection\r\n): WithFaceDetection<TSource> {\r\n\r\n const extension = { detection }\r\n return Object.assign({}, sourceObj, extension)\r\n}\r\n","import { Environment } from './types';\r\n\r\nexport function createBrowserEnv(): Environment {\r\n\r\n const fetch = window['fetch'] || function() {\r\n throw new Error('fetch - missing fetch implementation for browser environment')\r\n }\r\n\r\n const readFile = function() {\r\n throw new Error('readFile - filesystem not available for browser environment')\r\n }\r\n\r\n return {\r\n Canvas: HTMLCanvasElement,\r\n CanvasRenderingContext2D: CanvasRenderingContext2D,\r\n Image: HTMLImageElement,\r\n ImageData: ImageData,\r\n Video: HTMLVideoElement,\r\n createCanvasElement: () => document.createElement('canvas'),\r\n createImageElement: () => document.createElement('img'),\r\n fetch,\r\n readFile\r\n }\r\n}","import { FileSystem } from './types';\r\n\r\nexport function createFileSystem(fs?: any): FileSystem {\r\n\r\n let requireFsError = ''\r\n\r\n if (!fs) {\r\n try {\r\n fs = require('fs')\r\n } catch (err) {\r\n requireFsError = err.toString()\r\n }\r\n }\r\n\r\n const readFile = fs\r\n ? function(filePath: string) {\r\n return new Promise<Buffer>((res, rej) => {\r\n fs.readFile(filePath, function(err: any, buffer: Buffer) {\r\n return err ? rej(err) : res(buffer)\r\n })\r\n })\r\n }\r\n : function() {\r\n throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)\r\n }\r\n\r\n return {\r\n readFile\r\n }\r\n}","import { createFileSystem } from './createFileSystem';\r\nimport { Environment } from './types';\r\n\r\nexport function createNodejsEnv(): Environment {\r\n\r\n const Canvas = global['Canvas'] || global['HTMLCanvasElement']\r\n const Image = global['Image'] || global['HTMLImageElement']\r\n\r\n const createCanvasElement = function() {\r\n if (Canvas) {\r\n return new Canvas()\r\n }\r\n throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')\r\n }\r\n\r\n const createImageElement = function() {\r\n if (Image) {\r\n return new Image()\r\n }\r\n throw new Error('createImageElement - missing Image implementation for nodejs environment')\r\n }\r\n\r\n const fetch = global['fetch'] || function() {\r\n throw new Error('fetch - missing fetch implementation for nodejs environment')\r\n }\r\n\r\n const fileSystem = createFileSystem()\r\n\r\n return {\r\n Canvas: Canvas || class {},\r\n CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || class {},\r\n Image: Image || class {},\r\n ImageData: global['ImageData'] || class {},\r\n Video: global['HTMLVideoElement'] || class {},\r\n createCanvasElement,\r\n createImageElement,\r\n fetch,\r\n ...fileSystem\r\n }\r\n}","export function isBrowser(): boolean {\r\n return typeof window === 'object'\r\n && typeof document !== 'undefined'\r\n && typeof HTMLImageElement !== 'undefined'\r\n && typeof HTMLCanvasElement !== 'undefined'\r\n && typeof HTMLVideoElement !== 'undefined'\r\n && typeof ImageData !== 'undefined'\r\n && typeof CanvasRenderingContext2D !== 'undefined'\r\n}","export function isNodejs(): boolean {\r\n return typeof global === 'object'\r\n && typeof require === 'function'\r\n && typeof module !== 'undefined'\r\n // issues with gatsby.js: module.exports is undefined\r\n // && !!module.exports\r\n && typeof process !== 'undefined' && !!process.version\r\n}","import { createBrowserEnv } from './createBrowserEnv';\r\nimport { createFileSystem } from './createFileSystem';\r\nimport { createNodejsEnv } from './createNodejsEnv';\r\nimport { isBrowser } from './isBrowser';\r\nimport { isNodejs } from './isNodejs';\r\nimport { Environment } from './types';\r\n\r\nlet environment: Environment | null\r\n\r\nfunction getEnv(): Environment {\r\n if (!environment) {\r\n throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')\r\n }\r\n return environment\r\n}\r\n\r\nfunction setEnv(env: Environment) {\r\n environment = env\r\n}\r\n\r\nfunction initialize() {\r\n // check for isBrowser() first to prevent electron renderer process\r\n // to be initialized with wrong environment due to isNodejs() returning true\r\n if (isBrowser()) {\r\n setEnv(createBrowserEnv())\r\n }\r\n if (isNodejs()) {\r\n setEnv(createNodejsEnv())\r\n }\r\n}\r\n\r\nfunction monkeyPatch(env: Partial<Environment>) {\r\n if (!environment) {\r\n initialize()\r\n }\r\n\r\n if (!environment) {\r\n throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')\r\n }\r\n\r\n const { Canvas = environment.Canvas, Image = environment.Image } = env\r\n environment.Canvas = Canvas\r\n environment.Image = Image\r\n environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())\r\n environment.createImageElement = env.createImageElement || (() => new Image())\r\n\r\n environment.ImageData = env.ImageData || environment.ImageData\r\n environment.Video = env.Video || environment.Video\r\n environment.fetch = env.fetch || environment.fetch\r\n environment.readFile = env.readFile || environment.readFile\r\n}\r\n\r\nexport const env = {\r\n getEnv,\r\n setEnv,\r\n initialize,\r\n createBrowserEnv,\r\n createFileSystem,\r\n createNodejsEnv,\r\n monkeyPatch,\r\n isBrowser,\r\n isNodejs\r\n}\r\n\r\ninitialize()\r\n\r\nexport * from './types'\r\n","import { env } from '../env';\r\n\r\nexport function resolveInput(arg: string | any) {\r\n if (!env.isNodejs() && typeof arg === 'string') {\r\n return document.getElementById(arg)\r\n }\r\n return arg\r\n}","import { env } from '../env';\r\nimport { resolveInput } from './resolveInput';\r\n\r\nexport function getContext2dOrThrow(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D): CanvasRenderingContext2D {\r\n\r\n const { Canvas, CanvasRenderingContext2D } = env.getEnv()\r\n\r\n if (canvasArg instanceof CanvasRenderingContext2D) {\r\n return canvasArg\r\n }\r\n\r\n const canvas = resolveInput(canvasArg)\r\n\r\n if (!(canvas instanceof Canvas)) {\r\n throw new Error('resolveContext2d - expected canvas to be of instance of Canvas')\r\n }\r\n\r\n const ctx = canvas.getContext('2d')\r\n if (!ctx) {\r\n throw new Error('resolveContext2d - canvas 2d context is null')\r\n }\r\n\r\n return ctx\r\n}","import { IDimensions, IPoint } from '../classes';\r\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\r\nimport { resolveInput } from '../dom/resolveInput';\r\n\r\nexport enum AnchorPosition {\r\n TOP_LEFT = 'TOP_LEFT',\r\n TOP_RIGHT = 'TOP_RIGHT',\r\n BOTTOM_LEFT = 'BOTTOM_LEFT',\r\n BOTTOM_RIGHT = 'BOTTOM_RIGHT'\r\n}\r\n\r\nexport interface IDrawTextFieldOptions {\r\n anchorPosition?: AnchorPosition\r\n backgroundColor?: string\r\n fontColor?: string\r\n fontSize?: number\r\n fontStyle?: string\r\n padding?: number\r\n}\r\n\r\nexport class DrawTextFieldOptions implements IDrawTextFieldOptions {\r\n public anchorPosition: AnchorPosition\r\n public backgroundColor: string\r\n public fontColor: string\r\n public fontSize: number\r\n public fontStyle: string\r\n public padding: number\r\n\r\n constructor(options: IDrawTextFieldOptions = {}) {\r\n const { anchorPosition, backgroundColor, fontColor, fontSize, fontStyle, padding } = options\r\n this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT\r\n this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)'\r\n this.fontColor = fontColor || 'rgba(255, 255, 255, 1)'\r\n this.fontSize = fontSize || 14\r\n this.fontStyle = fontStyle || 'Georgia'\r\n this.padding = padding || 4\r\n }\r\n}\r\n\r\nexport class DrawTextField {\r\n public text: string[]\r\n public anchor : IPoint\r\n public options: DrawTextFieldOptions\r\n\r\n constructor(\r\n text: string | string[] | DrawTextField,\r\n anchor: IPoint,\r\n options: IDrawTextFieldOptions = {}\r\n ) {\r\n this.text = typeof text === 'string'\r\n ? [text]\r\n : (text instanceof DrawTextField ? text.text : text)\r\n this.anchor = anchor\r\n this.options = new DrawTextFieldOptions(options)\r\n }\r\n\r\n measureWidth(ctx: CanvasRenderingContext2D): number {\r\n const { padding } = this.options\r\n return this.text.map(l => ctx.measureText(l).width).reduce((w0, w1) => w0 < w1 ? w1 : w0, 0) + (2 * padding)\r\n }\r\n\r\n measureHeight(): number {\r\n const { fontSize, padding } = this.options\r\n return this.text.length * fontSize + (2 * padding)\r\n }\r\n\r\n getUpperLeft(ctx: CanvasRenderingContext2D, canvasDims?: IDimensions): IPoint {\r\n const { anchorPosition } = this.options\r\n const isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT\r\n const isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT\r\n\r\n const textFieldWidth = this.measureWidth(ctx)\r\n const textFieldHeight = this.measureHeight()\r\n const x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x)\r\n const y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y\r\n\r\n // adjust anchor if text box exceeds canvas borders\r\n if (canvasDims) {\r\n const { width, height } = canvasDims\r\n const newX = Math.max(Math.min(x, width - textFieldWidth), 0)\r\n const newY = Math.max(Math.min(y, height - textFieldHeight), 0)\r\n return { x: newX, y: newY }\r\n }\r\n return { x, y }\r\n }\r\n\r\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\r\n const canvas = resolveInput(canvasArg)\r\n const ctx = getContext2dOrThrow(canvas)\r\n\r\n const { backgroundColor, fontColor, fontSize, fontStyle, padding } = this.options\r\n\r\n ctx.font = `${fontSize}px ${fontStyle}`\r\n const maxTextWidth = this.measureWidth(ctx)\r\n const textHeight = this.measureHeight()\r\n\r\n ctx.fillStyle = backgroundColor\r\n const upperLeft = this.getUpperLeft(ctx, canvas)\r\n ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight)\r\n\r\n ctx.fillStyle = fontColor;\r\n this.text.forEach((textLine, i) => {\r\n const x = padding + upperLeft.x\r\n const y = padding + upperLeft.y + ((i + 1) * fontSize)\r\n ctx.fillText(textLine, x, y)\r\n })\r\n }\r\n}","import { Box, IBoundingBox, IRect } from '../classes';\r\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\r\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions, IDrawTextFieldOptions } from './DrawTextField';\r\n\r\nexport interface IDrawBoxOptions {\r\n boxColor?: string\r\n lineWidth?: number\r\n drawLabelOptions?: IDrawTextFieldOptions\r\n label?: string\r\n}\r\n\r\nexport class DrawBoxOptions {\r\n public boxColor: string\r\n public lineWidth: number\r\n public drawLabelOptions: DrawTextFieldOptions\r\n public label?: string\r\n\r\n constructor(options: IDrawBoxOptions = {}) {\r\n const { boxColor, lineWidth, label, drawLabelOptions } = options\r\n this.boxColor = boxColor || 'rgba(0, 0, 255, 1)'\r\n this.lineWidth = lineWidth || 2\r\n this.label = label\r\n\r\n const defaultDrawLabelOptions = {\r\n anchorPosition: AnchorPosition.BOTTOM_LEFT,\r\n backgroundColor: this.boxColor\r\n }\r\n this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions))\r\n }\r\n}\r\n\r\nexport class DrawBox {\r\n public box: Box\r\n public options: DrawBoxOptions\r\n\r\n constructor(\r\n box: IBoundingBox | IRect,\r\n options: IDrawBoxOptions = {}\r\n ) {\r\n this.box = new Box(box)\r\n this.options = new DrawBoxOptions(options)\r\n }\r\n\r\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\r\n const ctx = getContext2dOrThrow(canvasArg)\r\n\r\n const { boxColor, lineWidth } = this.options\r\n\r\n const { x, y, width, height } = this.box\r\n ctx.strokeStyle = boxColor\r\n ctx.lineWidth = lineWidth\r\n ctx.strokeRect(x, y, width, height)\r\n\r\n const { label } = this.options\r\n if (label) {\r\n new DrawTextField([label], { x: x - (lineWidth / 2), y }, this.options.drawLabelOptions).draw(canvasArg)\r\n }\r\n }\r\n}","import { Box, IBoundingBox, IRect } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { isWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { round } from '../utils';\r\nimport { DrawBox } from './DrawBox';\r\n\r\nexport type TDrawDetectionsInput = IRect | IBoundingBox | FaceDetection | WithFaceDetection<{}>\r\n\r\nexport function drawDetections(\r\n canvasArg: string | HTMLCanvasElement,\r\n detections: TDrawDetectionsInput | Array<TDrawDetectionsInput>\r\n) {\r\n const detectionsArray = Array.isArray(detections) ? detections : [detections]\r\n\r\n detectionsArray.forEach(det => {\r\n const score = det instanceof FaceDetection\r\n ? det.score\r\n : (isWithFaceDetection(det) ? det.detection.score : undefined)\r\n\r\n const box = det instanceof FaceDetection\r\n ? det.box\r\n : (isWithFaceDetection(det) ? det.detection.box : new Box(det))\r\n\r\n const label = score ? `${round(score)}` : undefined\r\n new DrawBox(box, { label }).draw(canvasArg)\r\n })\r\n}","import { env } from '../env';\r\n\r\nexport function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {\r\n\r\n const { Image, Video } = env.getEnv()\r\n\r\n return (media instanceof Image && media.complete)\r\n || (media instanceof Video && media.readyState >= 3)\r\n}\r\n","import { env } from '../env';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\n\r\nexport function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {\r\n\r\n return new Promise((resolve, reject) => {\r\n if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\r\n return resolve()\r\n }\r\n\r\n function onLoad(e: Event) {\r\n if (!e.currentTarget) return\r\n e.currentTarget.removeEventListener('load', onLoad)\r\n e.currentTarget.removeEventListener('error', onError)\r\n resolve(e)\r\n }\r\n\r\n function onError(e: Event) {\r\n if (!e.currentTarget) return\r\n e.currentTarget.removeEventListener('load', onLoad)\r\n e.currentTarget.removeEventListener('error', onError)\r\n reject(e)\r\n }\r\n\r\n media.addEventListener('load', onLoad)\r\n media.addEventListener('error', onError)\r\n })\r\n}","import { env } from '../env';\r\n\r\nexport function bufferToImage(buf: Blob): Promise<HTMLImageElement> {\r\n return new Promise((resolve, reject) => {\r\n if (!(buf instanceof Blob)) {\r\n return reject('bufferToImage - expected buf to be of type: Blob')\r\n }\r\n\r\n const reader = new FileReader()\r\n reader.onload = () => {\r\n if (typeof reader.result !== 'string') {\r\n return reject('bufferToImage - expected reader.result to be a string, in onload')\r\n }\r\n\r\n const img = env.getEnv().createImageElement()\r\n img.onload = () => resolve(img)\r\n img.onerror = reject\r\n img.src = reader.result\r\n }\r\n reader.onerror = reject\r\n reader.readAsDataURL(buf)\r\n })\r\n}","import { Dimensions, IDimensions } from '../classes/Dimensions';\r\nimport { env } from '../env';\r\n\r\nexport function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {\r\n\r\n const { Image, Video } = env.getEnv()\r\n\r\n if (input instanceof Image) {\r\n return new Dimensions(input.naturalWidth, input.naturalHeight)\r\n }\r\n if (input instanceof Video) {\r\n return new Dimensions(input.videoWidth, input.videoHeight)\r\n }\r\n return new Dimensions(input.width, input.height)\r\n}\r\n","import { IDimensions } from '../classes/Dimensions';\r\nimport { env } from '../env';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\n\r\nexport function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {\r\n\r\n const { createCanvasElement } = env.getEnv()\r\n const canvas = createCanvasElement()\r\n canvas.width = width\r\n canvas.height = height\r\n return canvas\r\n}\r\n\r\nexport function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {\r\n\r\n const { ImageData } = env.getEnv()\r\n\r\n if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\r\n throw new Error('createCanvasFromMedia - media has not finished loading yet')\r\n }\r\n\r\n const { width, height } = dims || getMediaDimensions(media)\r\n const canvas = createCanvas({ width, height })\r\n\r\n if (media instanceof ImageData) {\r\n getContext2dOrThrow(canvas).putImageData(media, 0, 0)\r\n } else {\r\n getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)\r\n }\r\n return canvas\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { env } from '../env';\r\nimport { isTensor4D } from '../utils';\r\n\r\nexport async function imageTensorToCanvas(\r\n imgTensor: tf.Tensor,\r\n canvas?: HTMLCanvasElement\r\n): Promise<HTMLCanvasElement> {\r\n\r\n const targetCanvas = canvas || env.getEnv().createCanvasElement()\r\n\r\n const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)\r\n const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())\r\n await tf.browser.toPixels(imgTensor3D, targetCanvas)\r\n\r\n imgTensor3D.dispose()\r\n\r\n return targetCanvas\r\n}","import { env } from '../env';\r\n\r\nexport function isMediaElement(input: any) {\r\n\r\n const { Image, Canvas, Video } = env.getEnv()\r\n\r\n return input instanceof Image\r\n || input instanceof Canvas\r\n || input instanceof Video\r\n}","import { env } from '../env';\r\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\n\r\nexport function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {\r\n\r\n const { Image, Canvas } = env.getEnv()\r\n\r\n if (!(input instanceof Image || input instanceof Canvas)) {\r\n throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')\r\n }\r\n\r\n const dims = getMediaDimensions(input)\r\n const scale = inputSize / Math.max(dims.height, dims.width)\r\n const width = scale * dims.width\r\n const height = scale * dims.height\r\n\r\n const targetCanvas = createCanvas({ width: inputSize, height: inputSize })\r\n const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)\r\n\r\n const offset = Math.abs(width - height) / 2\r\n const dx = centerImage && width < height ? offset : 0\r\n const dy = centerImage && height < width ? offset : 0\r\n getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)\r\n\r\n return targetCanvas\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Dimensions } from '../classes/Dimensions';\r\nimport { env } from '../env';\r\nimport { padToSquare } from '../ops/padToSquare';\r\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\r\nimport { createCanvasFromMedia } from './createCanvas';\r\nimport { imageToSquare } from './imageToSquare';\r\nimport { TResolvedNetInput } from './types';\r\n\r\nexport class NetInput {\r\n private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []\r\n private _canvases: HTMLCanvasElement[] = []\r\n private _batchSize: number\r\n private _treatAsBatchInput: boolean = false\r\n\r\n private _inputDimensions: number[][] = []\r\n private _inputSize: number\r\n\r\n constructor(\r\n inputs: Array<TResolvedNetInput>,\r\n treatAsBatchInput: boolean = false\r\n ) {\r\n if (!Array.isArray(inputs)) {\r\n throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)\r\n }\r\n\r\n this._treatAsBatchInput = treatAsBatchInput\r\n this._batchSize = inputs.length\r\n\r\n inputs.forEach((input, idx) => {\r\n\r\n if (isTensor3D(input)) {\r\n this._imageTensors[idx] = input\r\n this._inputDimensions[idx] = input.shape\r\n return\r\n }\r\n\r\n if (isTensor4D(input)) {\r\n const batchSize = input.shape[0]\r\n if (batchSize !== 1) {\r\n throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\r\n }\r\n\r\n this._imageTensors[idx] = input\r\n this._inputDimensions[idx] = input.shape.slice(1)\r\n return\r\n }\r\n\r\n const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)\r\n this._canvases[idx] = canvas\r\n this._inputDimensions[idx] = [canvas.height, canvas.width, 3]\r\n })\r\n }\r\n\r\n public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {\r\n return this._imageTensors\r\n }\r\n\r\n public get canvases(): HTMLCanvasElement[] {\r\n return this._canvases\r\n }\r\n\r\n public get isBatchInput(): boolean {\r\n return this.batchSize > 1 || this._treatAsBatchInput\r\n }\r\n\r\n public get batchSize(): number {\r\n return this._batchSize\r\n }\r\n\r\n public get inputDimensions(): number[][] {\r\n return this._inputDimensions\r\n }\r\n\r\n public get inputSize(): number | undefined {\r\n return this._inputSize\r\n }\r\n\r\n public get reshapedInputDimensions(): Dimensions[] {\r\n return range(this.batchSize, 0, 1).map(\r\n (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)\r\n )\r\n }\r\n\r\n public getInput(batchIdx: number): tf.Tensor3D | tf.Tensor4D | HTMLCanvasElement {\r\n return this.canvases[batchIdx] || this.imageTensors[batchIdx]\r\n }\r\n\r\n public getInputDimensions(batchIdx: number): number[] {\r\n return this._inputDimensions[batchIdx]\r\n }\r\n\r\n public getInputHeight(batchIdx: number): number {\r\n return this._inputDimensions[batchIdx][0]\r\n }\r\n\r\n public getInputWidth(batchIdx: number): number {\r\n return this._inputDimensions[batchIdx][1]\r\n }\r\n\r\n public getReshapedInputDimensions(batchIdx: number): Dimensions {\r\n if (typeof this.inputSize !== 'number') {\r\n throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')\r\n }\r\n\r\n const width = this.getInputWidth(batchIdx)\r\n const height = this.getInputHeight(batchIdx)\r\n return computeReshapedDimensions({ width, height }, this.inputSize)\r\n }\r\n\r\n /**\r\n * Create a batch tensor from all input canvases and tensors\r\n * with size [batchSize, inputSize, inputSize, 3].\r\n *\r\n * @param inputSize Height and width of the tensor.\r\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n * both sides of the minor dimension oof the image.\r\n * @returns The batch tensor.\r\n */\r\n public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {\r\n\r\n this._inputSize = inputSize\r\n\r\n return tf.tidy(() => {\r\n\r\n const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {\r\n const input = this.getInput(batchIdx)\r\n\r\n if (input instanceof tf.Tensor) {\r\n let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()\r\n imgTensor = padToSquare(imgTensor, isCenterInputs)\r\n\r\n if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\r\n imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])\r\n }\r\n\r\n return imgTensor.as3D(inputSize, inputSize, 3)\r\n }\r\n\r\n if (input instanceof env.getEnv().Canvas) {\r\n return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs))\r\n }\r\n\r\n throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)\r\n })\r\n\r\n const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)\r\n\r\n return batchTensor\r\n })\r\n }\r\n}","import { isTensor3D, isTensor4D } from '../utils';\r\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\r\nimport { isMediaElement } from './isMediaElement';\r\nimport { NetInput } from './NetInput';\r\nimport { resolveInput } from './resolveInput';\r\nimport { TNetInput } from './types';\r\n\r\n/**\r\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\r\n * to be finished loading.\r\n *\r\n * @param input The input, which can be a media element or an array of different media elements.\r\n * @returns A NetInput instance, which can be passed into one of the neural networks.\r\n */\r\nexport async function toNetInput(inputs: TNetInput): Promise<NetInput> {\r\n if (inputs instanceof NetInput) {\r\n return inputs\r\n }\r\n\r\n let inputArgArray = Array.isArray(inputs)\r\n ? inputs\r\n : [inputs]\r\n\r\n if (!inputArgArray.length) {\r\n throw new Error('toNetInput - empty array passed as input')\r\n }\r\n\r\n const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''\r\n\r\n const inputArray = inputArgArray.map(resolveInput)\r\n\r\n inputArray.forEach((input, i) => {\r\n if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\r\n\r\n if (typeof inputArgArray[i] === 'string') {\r\n throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)\r\n }\r\n\r\n throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)\r\n }\r\n\r\n if (isTensor4D(input)) {\r\n // if tf.Tensor4D is passed in the input array, the batch size has to be 1\r\n const batchSize = input.shape[0]\r\n if (batchSize !== 1) {\r\n throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)\r\n }\r\n }\r\n })\r\n\r\n // wait for all media elements being loaded\r\n await Promise.all(\r\n inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))\r\n )\r\n\r\n return new NetInput(inputArray, Array.isArray(inputs))\r\n}","import { FaceDetection } from '../classes/FaceDetection';\r\nimport { Rect } from '../classes/Rect';\r\nimport { env } from '../env';\r\nimport { createCanvas } from './createCanvas';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\r\nimport { toNetInput } from './toNetInput';\r\nimport { TNetInput } from './types';\r\n\r\n/**\r\n * Extracts the image regions containing the detected faces.\r\n *\r\n * @param input The image that face detection has been performed on.\r\n * @param detections The face detection results or face bounding boxes for that image.\r\n * @returns The Canvases of the corresponding image region for each detected face.\r\n */\r\nexport async function extractFaces(\r\n input: TNetInput,\r\n detections: Array<FaceDetection | Rect>\r\n): Promise<HTMLCanvasElement[]> {\r\n\r\n const { Canvas } = env.getEnv()\r\n\r\n let canvas = input as HTMLCanvasElement\r\n\r\n if (!(input instanceof Canvas)) {\r\n const netInput = await toNetInput(input)\r\n\r\n if (netInput.batchSize > 1) {\r\n throw new Error('extractFaces - batchSize > 1 not supported')\r\n }\r\n\r\n const tensorOrCanvas = netInput.getInput(0)\r\n canvas = tensorOrCanvas instanceof Canvas\r\n ? tensorOrCanvas\r\n : await imageTensorToCanvas(tensorOrCanvas)\r\n }\r\n\r\n const ctx = getContext2dOrThrow(canvas)\r\n const boxes = detections.map(\r\n det => det instanceof FaceDetection\r\n ? det.forSize(canvas.width, canvas.height).box.floor()\r\n : det\r\n )\r\n .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\r\n\r\n return boxes.map(({ x, y, width, height }) => {\r\n const faceImg = createCanvas({ width, height })\r\n getContext2dOrThrow(faceImg)\r\n .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\r\n return faceImg\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Rect } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { isTensor3D, isTensor4D } from '../utils';\r\n\r\n/**\r\n * Extracts the tensors of the image regions containing the detected faces.\r\n * Useful if you want to compute the face descriptors for the face images.\r\n * Using this method is faster then extracting a canvas for each face and\r\n * converting them to tensors individually.\r\n *\r\n * @param imageTensor The image tensor that face detection has been performed on.\r\n * @param detections The face detection results or face bounding boxes for that image.\r\n * @returns Tensors of the corresponding image region for each detected face.\r\n */\r\nexport async function extractFaceTensors(\r\n imageTensor: tf.Tensor3D | tf.Tensor4D,\r\n detections: Array<FaceDetection | Rect>\r\n): Promise<tf.Tensor3D[]> {\r\n\r\n if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\r\n throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\r\n }\r\n\r\n if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\r\n throw new Error('extractFaceTensors - batchSize > 1 not supported')\r\n }\r\n\r\n return tf.tidy(() => {\r\n const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\r\n\r\n const boxes = detections.map(\r\n det => det instanceof FaceDetection\r\n ? det.forSize(imgWidth, imgHeight).box\r\n : det\r\n )\r\n .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\r\n\r\n const faceTensors = boxes.map(({ x, y, width, height }) =>\r\n tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\r\n )\r\n\r\n return faceTensors\r\n })\r\n}","import { env } from '../env';\r\n\r\nexport async function fetchOrThrow(\r\n url: string,\r\n init?: RequestInit\r\n): Promise<Response> {\r\n\r\n const fetch = env.getEnv().fetch\r\n const res = await fetch(url, init)\r\n if (!(res.status < 400)) {\r\n throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)\r\n }\r\n return res\r\n}","import { bufferToImage } from './bufferToImage';\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\n\r\nexport async function fetchImage(uri: string): Promise<HTMLImageElement> {\r\n const res = await fetchOrThrow(uri)\r\n const blob = await (res).blob()\r\n\r\n if (!blob.type.startsWith('image/')) {\r\n throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)\r\n }\r\n return bufferToImage(blob)\r\n}\r\n","import { fetchOrThrow } from './fetchOrThrow';\r\n\r\nexport async function fetchJson<T>(uri: string): Promise<T> {\r\n return (await fetchOrThrow(uri)).json()\r\n}\r\n","import { fetchOrThrow } from './fetchOrThrow';\r\n\r\nexport async function fetchNetWeights(uri: string): Promise<Float32Array> {\r\n return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())\r\n}\r\n","export function getModelUris(uri: string | undefined, defaultModelName: string) {\r\n const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`\r\n\r\n if (!uri) {\r\n return {\r\n modelBaseUri: '',\r\n manifestUri: defaultManifestFilename\r\n }\r\n }\r\n\r\n if (uri === '/') {\r\n return {\r\n modelBaseUri: '/',\r\n manifestUri: `/${defaultManifestFilename}`\r\n }\r\n }\r\n const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\r\n uri = uri.replace(protocol, '');\r\n\r\n const parts = uri.split('/').filter(s => s)\r\n\r\n const manifestFile = uri.endsWith('.json')\r\n ? parts[parts.length - 1]\r\n : defaultManifestFilename\r\n\r\n let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')\r\n modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri\r\n\r\n return {\r\n modelBaseUri,\r\n manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { getModelUris } from '../common/getModelUris';\r\nimport { fetchJson } from './fetchJson';\r\n\r\nexport async function loadWeightMap(\r\n uri: string | undefined,\r\n defaultModelName: string,\r\n): Promise<tf.NamedTensorMap> {\r\n const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)\r\n\r\n const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)\r\n\r\n return tf.io.loadWeights(manifest, modelBaseUri)\r\n}","import { IDimensions } from '../classes';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\n\r\nexport function matchDimensions(input: IDimensions, reference: IDimensions, useMediaDimensions: boolean = false) {\r\n const { width, height } = useMediaDimensions\r\n ? getMediaDimensions(reference)\r\n : reference\r\n input.width = width\r\n input.height = height\r\n return { width, height }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ParamMapping } from './common';\r\nimport { getModelUris } from './common/getModelUris';\r\nimport { loadWeightMap } from './dom';\r\nimport { env } from './env';\r\n\r\nexport abstract class NeuralNetwork<TNetParams> {\r\n\r\n protected _params: TNetParams | undefined = undefined\r\n protected _paramMappings: ParamMapping[] = []\r\n\r\n constructor(protected _name: string) {}\r\n\r\n public get params(): TNetParams | undefined { return this._params }\r\n public get paramMappings(): ParamMapping[] { return this._paramMappings }\r\n public get isLoaded(): boolean { return !!this.params }\r\n\r\n public getParamFromPath(paramPath: string): tf.Tensor {\r\n const { obj, objProp } = this.traversePropertyPath(paramPath)\r\n return obj[objProp]\r\n }\r\n\r\n public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {\r\n const { obj, objProp } = this.traversePropertyPath(paramPath)\r\n obj[objProp].dispose()\r\n obj[objProp] = tensor\r\n }\r\n\r\n public getParamList() {\r\n return this._paramMappings.map(({ paramPath }) => ({\r\n path: paramPath,\r\n tensor: this.getParamFromPath(paramPath)\r\n }))\r\n }\r\n\r\n public getTrainableParams() {\r\n return this.getParamList().filter(param => param.tensor instanceof tf.Variable)\r\n }\r\n\r\n public getFrozenParams() {\r\n return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))\r\n }\r\n\r\n public variable() {\r\n this.getFrozenParams().forEach(({ path, tensor }) => {\r\n this.reassignParamFromPath(path, tensor.variable())\r\n })\r\n }\r\n\r\n public freeze() {\r\n this.getTrainableParams().forEach(({ path, tensor: variable }) => {\r\n const tensor = tf.tensor(variable.dataSync())\r\n variable.dispose()\r\n this.reassignParamFromPath(path, tensor)\r\n })\r\n }\r\n\r\n public dispose(throwOnRedispose: boolean = true) {\r\n this.getParamList().forEach(param => {\r\n if (throwOnRedispose && param.tensor.isDisposed) {\r\n throw new Error(`param tensor has already been disposed for path ${param.path}`)\r\n }\r\n param.tensor.dispose()\r\n })\r\n this._params = undefined\r\n }\r\n\r\n public serializeParams(): Float32Array {\r\n return new Float32Array(\r\n this.getParamList()\r\n .map(({ tensor }) => Array.from(tensor.dataSync()) as number[])\r\n .reduce((flat, arr) => flat.concat(arr))\r\n )\r\n }\r\n\r\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\r\n if (weightsOrUrl instanceof Float32Array) {\r\n this.extractWeights(weightsOrUrl)\r\n return\r\n }\r\n\r\n await this.loadFromUri(weightsOrUrl)\r\n }\r\n\r\n public async loadFromUri(uri: string | undefined) {\r\n if (uri && typeof uri !== 'string') {\r\n throw new Error(`${this._name}.loadFromUri - expected model uri`)\r\n }\r\n\r\n const weightMap = await loadWeightMap(uri, this.getDefaultModelName())\r\n this.loadFromWeightMap(weightMap)\r\n }\r\n\r\n public async loadFromDisk(filePath: string | undefined) {\r\n if (filePath && typeof filePath !== 'string') {\r\n throw new Error(`${this._name}.loadFromDisk - expected model file path`)\r\n }\r\n\r\n const { readFile } = env.getEnv()\r\n\r\n const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())\r\n\r\n const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(\r\n filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))\r\n )\r\n const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\r\n\r\n const manifest = JSON.parse((await readFile(manifestUri)).toString())\r\n const weightMap = await loadWeights(manifest, modelBaseUri)\r\n\r\n this.loadFromWeightMap(weightMap)\r\n }\r\n\r\n public loadFromWeightMap(weightMap: tf.NamedTensorMap) {\r\n const {\r\n paramMappings,\r\n params\r\n } = this.extractParamsFromWeigthMap(weightMap)\r\n\r\n this._paramMappings = paramMappings\r\n this._params = params\r\n }\r\n\r\n public extractWeights(weights: Float32Array) {\r\n const {\r\n paramMappings,\r\n params\r\n } = this.extractParams(weights)\r\n\r\n this._paramMappings = paramMappings\r\n this._params = params\r\n }\r\n\r\n private traversePropertyPath(paramPath: string) {\r\n if (!this.params) {\r\n throw new Error(`traversePropertyPath - model has no loaded params`)\r\n }\r\n\r\n const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {\r\n if (!res.nextObj.hasOwnProperty(objProp)) {\r\n throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)\r\n }\r\n\r\n return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }\r\n }, { nextObj: this.params })\r\n\r\n const { obj, objProp } = result\r\n if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\r\n throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)\r\n }\r\n\r\n return { obj, objProp }\r\n }\r\n\r\n protected abstract getDefaultModelName(): string\r\n protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }\r\n protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { SeparableConvParams } from './types';\r\n\r\nexport function depthwiseSeparableConv(\r\n x: tf.Tensor4D,\r\n params: SeparableConvParams,\r\n stride: [number, number]\r\n): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\r\n out = tf.add(out, params.bias)\r\n return out\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams, SeparableConvParams } from '../common';\r\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\r\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\r\n\r\nexport function denseBlock3(\r\n x: tf.Tensor4D,\r\n denseBlockParams: DenseBlock3Params,\r\n isFirstLayer: boolean = false\r\n): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n const out1 = tf.relu(\r\n isFirstLayer\r\n ? tf.add(\r\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\r\n denseBlockParams.conv0.bias\r\n )\r\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\r\n ) as tf.Tensor4D\r\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\r\n\r\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\r\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\r\n\r\n return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\r\n })\r\n}\r\n\r\nexport function denseBlock4(\r\n x: tf.Tensor4D,\r\n denseBlockParams: DenseBlock4Params,\r\n isFirstLayer: boolean = false,\r\n isScaleDown: boolean = true\r\n): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n const out1 = tf.relu(\r\n isFirstLayer\r\n ? tf.add(\r\n tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\r\n denseBlockParams.conv0.bias\r\n )\r\n : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\r\n ) as tf.Tensor4D\r\n const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\r\n\r\n const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\r\n const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\r\n\r\n const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\r\n const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\r\n\r\n return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\r\n })\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams } from './types';\r\n\r\nexport function convLayer(\r\n x: tf.Tensor4D,\r\n params: ConvParams,\r\n padding: 'valid' | 'same' = 'same',\r\n withRelu: boolean = false\r\n): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n const out = tf.add(\r\n tf.conv2d(x, params.filters, [1, 1], padding),\r\n params.bias\r\n ) as tf.Tensor4D\r\n\r\n return withRelu ? tf.relu(out) : out\r\n })\r\n}","import { ParamMapping } from './types';\r\n\r\nexport function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {\r\n Object.keys(weightMap).forEach(path => {\r\n if (!paramMappings.some(pm => pm.originalPath === path)) {\r\n weightMap[path].dispose()\r\n }\r\n })\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams, ExtractWeightsFunction, ParamMapping } from './types';\r\n\r\nexport function extractConvParamsFactory(\r\n extractWeights: ExtractWeightsFunction,\r\n paramMappings: ParamMapping[]\r\n) {\r\n\r\n return function(\r\n channelsIn: number,\r\n channelsOut: number,\r\n filterSize: number,\r\n mappedPrefix: string\r\n ): ConvParams {\r\n\r\n const filters = tf.tensor4d(\r\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\r\n [filterSize, filterSize, channelsIn, channelsOut]\r\n )\r\n const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/filters` },\r\n { paramPath: `${mappedPrefix}/bias` }\r\n )\r\n\r\n return { filters, bias }\r\n }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ExtractWeightsFunction, FCParams, ParamMapping } from './types';\r\n\r\n\r\nexport function extractFCParamsFactory(\r\n extractWeights: ExtractWeightsFunction,\r\n paramMappings: ParamMapping[]\r\n) {\r\n\r\n return function(\r\n channelsIn: number,\r\n channelsOut: number,\r\n mappedPrefix: string\r\n ): FCParams {\r\n\r\n const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\r\n const fc_bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/weights` },\r\n { paramPath: `${mappedPrefix}/bias` }\r\n )\r\n\r\n return {\r\n weights: fc_weights,\r\n bias: fc_bias\r\n }\r\n }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport type ExtractWeightsFunction = (numWeights: number) => Float32Array\r\n\r\nexport type ParamMapping = {\r\n originalPath?: string\r\n paramPath: string\r\n}\r\n\r\nexport type ConvParams = {\r\n filters: tf.Tensor4D\r\n bias: tf.Tensor1D\r\n}\r\n\r\nexport type FCParams = {\r\n weights: tf.Tensor2D\r\n bias: tf.Tensor1D\r\n}\r\n\r\nexport class SeparableConvParams {\r\n constructor(\r\n public depthwise_filter: tf.Tensor4D,\r\n public pointwise_filter: tf.Tensor4D,\r\n public bias: tf.Tensor1D\r\n ) {}\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ExtractWeightsFunction, ParamMapping, SeparableConvParams } from './types';\r\n\r\nexport function extractSeparableConvParamsFactory(\r\n extractWeights: ExtractWeightsFunction,\r\n paramMappings: ParamMapping[]\r\n) {\r\n\r\n return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\r\n const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\r\n const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\r\n const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/depthwise_filter` },\r\n { paramPath: `${mappedPrefix}/pointwise_filter` },\r\n { paramPath: `${mappedPrefix}/bias` }\r\n )\r\n\r\n return new SeparableConvParams(\r\n depthwise_filter,\r\n pointwise_filter,\r\n bias\r\n )\r\n }\r\n\r\n}\r\n\r\nexport function loadSeparableConvParamsFactory(\r\n extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\r\n) {\r\n\r\n return function (prefix: string): SeparableConvParams {\r\n const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\r\n const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n return new SeparableConvParams(\r\n depthwise_filter,\r\n pointwise_filter,\r\n bias\r\n )\r\n }\r\n\r\n}\r\n","import { isTensor } from '../utils';\r\nimport { ParamMapping } from './types';\r\n\r\nexport function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {\r\n const tensor = weightMap[originalPath]\r\n\r\n if (!isTensor(tensor, paramRank)) {\r\n throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)\r\n }\r\n\r\n paramMappings.push(\r\n { originalPath, paramPath: mappedPath || originalPath }\r\n )\r\n\r\n return tensor\r\n }\r\n\r\n}\r\n","export function extractWeightsFactory(weights: Float32Array) {\r\n let remainingWeights = weights\r\n\r\n function extractWeights(numWeights: number): Float32Array {\r\n const ret = remainingWeights.slice(0, numWeights)\r\n remainingWeights = remainingWeights.slice(numWeights)\r\n return ret\r\n }\r\n\r\n function getRemainingWeights(): Float32Array {\r\n return remainingWeights\r\n }\r\n\r\n return {\r\n extractWeights,\r\n getRemainingWeights\r\n }\r\n}","import {\r\n extractConvParamsFactory,\r\n extractSeparableConvParamsFactory,\r\n ExtractWeightsFunction,\r\n ParamMapping,\r\n} from '../common';\r\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\r\n\r\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\r\n\r\n function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\r\n\r\n const conv0 = isFirstLayer\r\n ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\r\n : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\r\n const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\r\n const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\r\n\r\n return { conv0, conv1, conv2 }\r\n }\r\n\r\n function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\r\n\r\n const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\r\n const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\r\n\r\n return { conv0, conv1, conv2, conv3 }\r\n }\r\n\r\n return {\r\n extractDenseBlock3Params,\r\n extractDenseBlock4Params\r\n }\r\n\r\n}","import { extractWeightsFactory, ParamMapping } from '../common';\r\nimport { extractorsFactory } from './extractorsFactory';\r\nimport { FaceFeatureExtractorParams } from './types';\r\n\r\n\r\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const {\r\n extractDenseBlock4Params\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\r\n const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\r\n const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\r\n const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return {\r\n paramMappings,\r\n params: { dense0, dense1, dense2, dense3 }\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams } from './types';\r\n\r\nexport function loadConvParamsFactory(extractWeightEntry: <T>(originalPath: string, paramRank: number) => T) {\r\n return function(prefix: string): ConvParams {\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n return { filters, bias }\r\n }\r\n}","import { extractWeightEntryFactory, loadSeparableConvParamsFactory, ParamMapping } from '../common';\r\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\r\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\r\n\r\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n const extractConvParams = loadConvParamsFactory(extractWeightEntry)\r\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\r\n\r\n function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\r\n const conv0 = isFirstLayer\r\n ? extractConvParams(`${prefix}/conv0`)\r\n : extractSeparableConvParams(`${prefix}/conv0`)\r\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\r\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\r\n\r\n return { conv0, conv1, conv2 }\r\n }\r\n\r\n function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\r\n const conv0 = isFirstLayer\r\n ? extractConvParams(`${prefix}/conv0`)\r\n : extractSeparableConvParams(`${prefix}/conv0`)\r\n const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\r\n const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\r\n const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\r\n\r\n return { conv0, conv1, conv2, conv3 }\r\n }\r\n\r\n return {\r\n extractDenseBlock3Params,\r\n extractDenseBlock4Params\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\r\nimport { loadParamsFactory } from './loadParamsFactory';\r\nimport { FaceFeatureExtractorParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap\r\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractDenseBlock4Params\r\n } = loadParamsFactory(weightMap, paramMappings)\r\n\r\n const params = {\r\n dense0: extractDenseBlock4Params('dense0', true),\r\n dense1: extractDenseBlock4Params('dense1'),\r\n dense2: extractDenseBlock4Params('dense2'),\r\n dense3: extractDenseBlock4Params('dense3')\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { denseBlock4 } from './denseBlock';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\r\n\r\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\r\n\r\n constructor() {\r\n super('FaceFeatureExtractor')\r\n }\r\n\r\n public forwardInput(input: NetInput): tf.Tensor4D {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('FaceFeatureExtractor - load model before inference')\r\n }\r\n\r\n return tf.tidy(() => {\r\n const batchTensor = input.toBatchTensor(112, true)\r\n const meanRgb = [122.782, 117.001, 104.298]\r\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\r\n\r\n let out = denseBlock4(normalized, params.dense0, true)\r\n out = denseBlock4(out, params.dense1)\r\n out = denseBlock4(out, params.dense2)\r\n out = denseBlock4(out, params.dense3)\r\n out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\r\n\r\n return out\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<tf.Tensor4D> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'face_feature_extractor_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMap(weightMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n return extractParams(weights)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { FCParams } from './types';\r\n\r\nexport function fullyConnectedLayer(\r\n x: tf.Tensor2D,\r\n params: FCParams\r\n): tf.Tensor2D {\r\n return tf.tidy(() =>\r\n tf.add(\r\n tf.matMul(x, params.weights),\r\n params.bias\r\n )\r\n )\r\n}","import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\r\nimport { NetParams } from './types';\r\n\r\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\r\n\r\n const fc = extractFCParams(channelsIn, channelsOut, 'fc')\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return {\r\n paramMappings,\r\n params: { fc }\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\r\nimport { NetParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n function extractFcParams(prefix: string): FCParams {\r\n const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n return { weights, bias }\r\n }\r\n\r\n const params = {\r\n fc: extractFcParams('fc')\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\r\n\r\n const featureExtractorMap: tf.NamedTensorMap = {}\r\n const classifierMap: tf.NamedTensorMap = {}\r\n\r\n Object.keys(weightMap).forEach(key => {\r\n const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\r\n map[key] = weightMap[key]\r\n })\r\n\r\n return { featureExtractorMap, classifierMap }\r\n\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { NetInput } from '../dom';\r\nimport {\r\n FaceFeatureExtractorParams,\r\n IFaceFeatureExtractor,\r\n TinyFaceFeatureExtractorParams,\r\n} from '../faceFeatureExtractor/types';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { NetParams } from './types';\r\nimport { seperateWeightMaps } from './util';\r\n\r\nexport abstract class FaceProcessor<\r\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\r\n>\r\n extends NeuralNetwork<NetParams> {\r\n\r\n protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\r\n\r\n constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\r\n super(_name)\r\n this._faceFeatureExtractor = faceFeatureExtractor\r\n }\r\n\r\n public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\r\n return this._faceFeatureExtractor\r\n }\r\n\r\n protected abstract getDefaultModelName(): string\r\n protected abstract getClassifierChannelsIn(): number\r\n protected abstract getClassifierChannelsOut(): number\r\n\r\n public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error(`${this._name} - load model before inference`)\r\n }\r\n\r\n return tf.tidy(() => {\r\n const bottleneckFeatures = input instanceof NetInput\r\n ? this.faceFeatureExtractor.forwardInput(input)\r\n : input\r\n return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\r\n })\r\n }\r\n\r\n public dispose(throwOnRedispose: boolean = true) {\r\n this.faceFeatureExtractor.dispose(throwOnRedispose)\r\n super.dispose(throwOnRedispose)\r\n }\r\n\r\n public loadClassifierParams(weights: Float32Array) {\r\n const { params, paramMappings } = this.extractClassifierParams(weights)\r\n this._params = params\r\n this._paramMappings = paramMappings\r\n }\r\n\r\n public extractClassifierParams(weights: Float32Array) {\r\n return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n\r\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\r\n\r\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\r\n\r\n return extractParamsFromWeigthMap(classifierMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n\r\n const cIn = this.getClassifierChannelsIn()\r\n const cOut = this.getClassifierChannelsOut()\r\n const classifierWeightSize = (cOut * cIn ) + cOut\r\n\r\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\r\n const classifierWeights = weights.slice(weights.length - classifierWeightSize)\r\n\r\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\r\n return this.extractClassifierParams(classifierWeights)\r\n }\r\n}","export const FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised']\r\n\r\nexport class FaceExpressions {\r\n public neutral: number\r\n public happy: number\r\n public sad: number\r\n public angry: number\r\n public fearful: number\r\n public disgusted: number\r\n public surprised: number\r\n\r\n constructor(probabilities: number[] | Float32Array) {\r\n if (probabilities.length !== 7) {\r\n throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${probabilities.length}`)\r\n }\r\n\r\n FACE_EXPRESSION_LABELS.forEach((expression, idx) => {\r\n this[expression] = probabilities[idx]\r\n })\r\n }\r\n\r\n asSortedArray() {\r\n return FACE_EXPRESSION_LABELS\r\n .map(expression => ({ expression, probability: this[expression] as number }))\r\n .sort((e0, e1) => e1.probability - e0.probability)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\r\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\r\nimport { FaceExpressions } from './FaceExpressions';\r\n\r\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\r\n\r\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\r\n super('FaceExpressionNet', faceFeatureExtractor)\r\n }\r\n\r\n public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\r\n return tf.tidy(() => tf.softmax(this.runNet(input)))\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<tf.Tensor2D> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n public async predictExpressions(input: TNetInput) {\r\n const netInput = await toNetInput(input)\r\n const out = await this.forwardInput(netInput)\r\n const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\r\n const data = await t.data()\r\n t.dispose()\r\n return data\r\n }))\r\n out.dispose()\r\n\r\n const predictionsByBatch = probabilitesByBatch\r\n .map(probabilites => new FaceExpressions(probabilites as Float32Array))\r\n\r\n return netInput.isBatchInput\r\n ? predictionsByBatch\r\n : predictionsByBatch[0]\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'face_expression_model'\r\n }\r\n\r\n protected getClassifierChannelsIn(): number {\r\n return 256\r\n }\r\n\r\n protected getClassifierChannelsOut(): number {\r\n return 7\r\n }\r\n}","import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\r\n\r\nexport type WithFaceExpressions<TSource> = TSource & {\r\n expressions: FaceExpressions\r\n}\r\n\r\nexport function isWithFaceExpressions(obj: any): obj is WithFaceExpressions<{}> {\r\n return obj['expressions'] instanceof FaceExpressions\r\n}\r\n\r\nexport function extendWithFaceExpressions<\r\n TSource\r\n> (\r\n sourceObj: TSource,\r\n expressions: FaceExpressions\r\n): WithFaceExpressions<TSource> {\r\n\r\n const extension = { expressions }\r\n return Object.assign({}, sourceObj, extension)\r\n}","import { IPoint, Point } from '../classes';\r\nimport { FaceExpressions } from '../faceExpressionNet';\r\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { isWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\r\nimport { round } from '../utils';\r\nimport { DrawTextField } from './DrawTextField';\r\n\r\nexport type DrawFaceExpressionsInput = FaceExpressions | WithFaceExpressions<{}>\r\n\r\nexport function drawFaceExpressions(\r\n canvasArg: string | HTMLCanvasElement,\r\n faceExpressions: DrawFaceExpressionsInput | Array<DrawFaceExpressionsInput>,\r\n minConfidence = 0.1,\r\n textFieldAnchor?: IPoint\r\n) {\r\n const faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions]\r\n\r\n faceExpressionsArray.forEach(e => {\r\n const expr = e instanceof FaceExpressions\r\n ? e\r\n : (isWithFaceExpressions(e) ? e.expressions : undefined)\r\n if (!expr) {\r\n throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof')\r\n }\r\n\r\n const sorted = expr.asSortedArray()\r\n const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\r\n\r\n const anchor = isWithFaceDetection(e)\r\n ? e.detection.box.bottomLeft\r\n : (textFieldAnchor || new Point(0, 0))\r\n\r\n const drawTextField = new DrawTextField(\r\n resultsToDisplay.map(expr => `${expr.expression} (${round(expr.probability)})`),\r\n anchor\r\n )\r\n drawTextField.draw(canvasArg)\r\n })\r\n}","import { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { isWithFaceDetection, WithFaceDetection } from './WithFaceDetection';\r\n\r\nexport type WithFaceLandmarks<\r\n TSource extends WithFaceDetection<{}>,\r\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\r\n> = TSource & {\r\n landmarks: TFaceLandmarks\r\n unshiftedLandmarks: TFaceLandmarks\r\n alignedRect: FaceDetection\r\n}\r\n\r\nexport function isWithFaceLandmarks(obj: any): obj is WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks> {\r\n return isWithFaceDetection(obj)\r\n && obj['landmarks'] instanceof FaceLandmarks\r\n && obj['unshiftedLandmarks'] instanceof FaceLandmarks\r\n && obj['alignedRect'] instanceof FaceDetection\r\n}\r\n\r\nexport function extendWithFaceLandmarks<\r\n TSource extends WithFaceDetection<{}>,\r\n TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\r\n> (\r\n sourceObj: TSource,\r\n unshiftedLandmarks: TFaceLandmarks\r\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\r\n\r\n const { box: shift } = sourceObj.detection\r\n const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\r\n\r\n const rect = landmarks.align()\r\n const { imageDims } = sourceObj.detection\r\n const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\r\n\r\n const extension = {\r\n landmarks,\r\n unshiftedLandmarks,\r\n alignedRect\r\n }\r\n\r\n return Object.assign({}, sourceObj, extension)\r\n}","import { IPoint } from '../classes';\r\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { drawContour } from './drawContour';\r\n\r\nexport interface IDrawFaceLandmarksOptions {\r\n drawLines?: boolean\r\n drawPoints?: boolean\r\n lineWidth?: number\r\n pointSize?: number\r\n lineColor?: string\r\n pointColor?: string\r\n}\r\n\r\nexport class DrawFaceLandmarksOptions {\r\n public drawLines: boolean\r\n public drawPoints: boolean\r\n public lineWidth: number\r\n public pointSize: number\r\n public lineColor: string\r\n public pointColor: string\r\n\r\n constructor(options: IDrawFaceLandmarksOptions = {}) {\r\n const { drawLines = true, drawPoints = true, lineWidth, lineColor, pointSize, pointColor } = options\r\n this.drawLines = drawLines\r\n this.drawPoints = drawPoints\r\n this.lineWidth = lineWidth || 1\r\n this.pointSize = pointSize || 2\r\n this.lineColor = lineColor || 'rgba(0, 255, 255, 1)'\r\n this.pointColor = pointColor || 'rgba(255, 0, 255, 1)'\r\n }\r\n}\r\n\r\nexport class DrawFaceLandmarks {\r\n public faceLandmarks: FaceLandmarks\r\n public options: DrawFaceLandmarksOptions\r\n\r\n constructor(\r\n faceLandmarks: FaceLandmarks,\r\n options: IDrawFaceLandmarksOptions = {}\r\n ) {\r\n this.faceLandmarks = faceLandmarks\r\n this.options = new DrawFaceLandmarksOptions(options)\r\n }\r\n\r\n draw(canvasArg: string | HTMLCanvasElement | CanvasRenderingContext2D) {\r\n const ctx = getContext2dOrThrow(canvasArg)\r\n\r\n const { drawLines, drawPoints, lineWidth, lineColor, pointSize, pointColor } = this.options\r\n\r\n if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\r\n ctx.strokeStyle = lineColor\r\n ctx.lineWidth = lineWidth\r\n drawContour(ctx, this.faceLandmarks.getJawOutline())\r\n drawContour(ctx, this.faceLandmarks.getLeftEyeBrow())\r\n drawContour(ctx, this.faceLandmarks.getRightEyeBrow())\r\n drawContour(ctx, this.faceLandmarks.getNose())\r\n drawContour(ctx, this.faceLandmarks.getLeftEye(), true)\r\n drawContour(ctx, this.faceLandmarks.getRightEye(), true)\r\n drawContour(ctx, this.faceLandmarks.getMouth(), true)\r\n }\r\n\r\n if (drawPoints) {\r\n ctx.strokeStyle = pointColor\r\n ctx.fillStyle = pointColor\r\n\r\n const drawPoint = (pt: IPoint) => {\r\n ctx.beginPath()\r\n ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI)\r\n ctx.fill()\r\n }\r\n this.faceLandmarks.positions.forEach(drawPoint)\r\n }\r\n }\r\n}\r\n\r\nexport type DrawFaceLandmarksInput = FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>>\r\n\r\nexport function drawFaceLandmarks(\r\n canvasArg: string | HTMLCanvasElement,\r\n faceLandmarks: DrawFaceLandmarksInput | Array<DrawFaceLandmarksInput>\r\n) {\r\n const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\r\n faceLandmarksArray.forEach(f => {\r\n const landmarks = f instanceof FaceLandmarks\r\n ? f\r\n : (isWithFaceLandmarks(f) ? f.landmarks : undefined)\r\n if (!landmarks) {\r\n throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof')\r\n }\r\n\r\n new DrawFaceLandmarks(landmarks).draw(canvasArg)\r\n })\r\n}","import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\r\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\r\nimport { range } from '../utils';\r\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\r\n\r\n function extractReductionBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ReductionBlockParams {\r\n\r\n const separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/separable_conv0`)\r\n const separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/separable_conv1`)\r\n const expansion_conv = extractConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/expansion_conv`)\r\n\r\n return { separable_conv0, separable_conv1, expansion_conv }\r\n }\r\n\r\n function extractMainBlockParams(channels: number, mappedPrefix: string): MainBlockParams {\r\n\r\n const separable_conv0 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv0`)\r\n const separable_conv1 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv1`)\r\n const separable_conv2 = extractSeparableConvParams(channels, channels, `${mappedPrefix}/separable_conv2`)\r\n\r\n return { separable_conv0, separable_conv1, separable_conv2 }\r\n }\r\n\r\n return {\r\n extractConvParams,\r\n extractSeparableConvParams,\r\n extractReductionBlockParams,\r\n extractMainBlockParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array, numMainBlocks: number): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const {\r\n extractConvParams,\r\n extractSeparableConvParams,\r\n extractReductionBlockParams,\r\n extractMainBlockParams\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n const entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in')\r\n const entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0')\r\n const entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1')\r\n\r\n const entry_flow = {\r\n conv_in: entry_flow_conv_in,\r\n reduction_block_0: entry_flow_reduction_block_0,\r\n reduction_block_1: entry_flow_reduction_block_1\r\n }\r\n\r\n const middle_flow = {}\r\n range(numMainBlocks, 0, 1).forEach((idx) => {\r\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(128, `middle_flow/main_block_${idx}`)\r\n })\r\n\r\n const exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block')\r\n const exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv')\r\n\r\n const exit_flow = {\r\n reduction_block: exit_flow_reduction_block,\r\n separable_conv: exit_flow_separable_conv\r\n }\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return {\r\n paramMappings,\r\n params: { entry_flow, middle_flow, exit_flow }\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport {\r\n disposeUnusedWeightTensors,\r\n extractWeightEntryFactory,\r\n loadSeparableConvParamsFactory,\r\n ParamMapping,\r\n} from '../common';\r\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\r\nimport { range } from '../utils';\r\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\r\n\r\nfunction loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n const extractConvParams = loadConvParamsFactory(extractWeightEntry)\r\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\r\n\r\n function extractReductionBlockParams(mappedPrefix: string): ReductionBlockParams {\r\n\r\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\r\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\r\n const expansion_conv = extractConvParams(`${mappedPrefix}/expansion_conv`)\r\n\r\n return { separable_conv0, separable_conv1, expansion_conv }\r\n }\r\n\r\n function extractMainBlockParams(mappedPrefix: string): MainBlockParams {\r\n\r\n const separable_conv0 = extractSeparableConvParams(`${mappedPrefix}/separable_conv0`)\r\n const separable_conv1 = extractSeparableConvParams(`${mappedPrefix}/separable_conv1`)\r\n const separable_conv2 = extractSeparableConvParams(`${mappedPrefix}/separable_conv2`)\r\n\r\n return { separable_conv0, separable_conv1, separable_conv2 }\r\n }\r\n\r\n return {\r\n extractConvParams,\r\n extractSeparableConvParams,\r\n extractReductionBlockParams,\r\n extractMainBlockParams\r\n }\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap,\r\n numMainBlocks: number\r\n): { params: TinyXceptionParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractConvParams,\r\n extractSeparableConvParams,\r\n extractReductionBlockParams,\r\n extractMainBlockParams\r\n } = loadParamsFactory(weightMap, paramMappings)\r\n\r\n const entry_flow_conv_in = extractConvParams('entry_flow/conv_in')\r\n const entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0')\r\n const entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1')\r\n\r\n const entry_flow = {\r\n conv_in: entry_flow_conv_in,\r\n reduction_block_0: entry_flow_reduction_block_0,\r\n reduction_block_1: entry_flow_reduction_block_1\r\n }\r\n\r\n const middle_flow = {}\r\n range(numMainBlocks, 0, 1).forEach((idx) => {\r\n middle_flow[`main_block_${idx}`] = extractMainBlockParams(`middle_flow/main_block_${idx}`)\r\n })\r\n\r\n const exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block')\r\n const exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv')\r\n\r\n const exit_flow = {\r\n reduction_block: exit_flow_reduction_block,\r\n separable_conv: exit_flow_separable_conv\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params: { entry_flow, middle_flow, exit_flow }, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams, depthwiseSeparableConv } from '../common';\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { range } from '../utils';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { MainBlockParams, ReductionBlockParams, TinyXceptionParams } from './types';\r\n\r\nfunction conv(x: tf.Tensor4D, params: ConvParams, stride: [number, number]): tf.Tensor4D {\r\n return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias)\r\n}\r\n\r\nfunction reductionBlock(x: tf.Tensor4D, params: ReductionBlockParams, isActivateInput: boolean = true): tf.Tensor4D {\r\n let out = isActivateInput ? tf.relu(x) : x\r\n out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1])\r\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\r\n out = tf.maxPool(out, [3, 3], [2, 2], 'same')\r\n out = tf.add(out, conv(x, params.expansion_conv, [2, 2]))\r\n return out\r\n}\r\n\r\nfunction mainBlock(x: tf.Tensor4D, params: MainBlockParams): tf.Tensor4D {\r\n let out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1])\r\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1])\r\n out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1])\r\n out = tf.add(out, x)\r\n return out\r\n}\r\n\r\nexport class TinyXception extends NeuralNetwork<TinyXceptionParams> {\r\n\r\n private _numMainBlocks: number\r\n\r\n constructor(numMainBlocks: number) {\r\n super('TinyXception')\r\n this._numMainBlocks = numMainBlocks\r\n }\r\n\r\n public forwardInput(input: NetInput): tf.Tensor4D {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('TinyXception - load model before inference')\r\n }\r\n\r\n return tf.tidy(() => {\r\n const batchTensor = input.toBatchTensor(112, true)\r\n const meanRgb = [122.782, 117.001, 104.298]\r\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\r\n\r\n let out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]))\r\n out = reductionBlock(out, params.entry_flow.reduction_block_0, false)\r\n out = reductionBlock(out, params.entry_flow.reduction_block_1)\r\n\r\n range(this._numMainBlocks, 0, 1).forEach((idx) => {\r\n out = mainBlock(out, params.middle_flow[`main_block_${idx}`])\r\n })\r\n\r\n out = reductionBlock(out, params.exit_flow.reduction_block)\r\n out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]))\r\n return out\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<tf.Tensor4D> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'tiny_xception_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMap(weightMap, this._numMainBlocks)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n return extractParams(weights, this._numMainBlocks)\r\n }\r\n}","import { extractFCParamsFactory, extractWeightsFactory, ParamMapping } from '../common';\r\nimport { NetParams } from './types';\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\r\n\r\n const age = extractFCParams(512, 1, 'fc/age')\r\n const gender = extractFCParams(512, 2, 'fc/gender')\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return {\r\n paramMappings,\r\n params: { fc: { age, gender } }\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\r\nimport { NetParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n function extractFcParams(prefix: string): FCParams {\r\n const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n return { weights, bias }\r\n }\r\n\r\n const params = {\r\n fc: {\r\n age: extractFcParams('fc/age'),\r\n gender: extractFcParams('fc/gender')\r\n }\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { FCParams } from '../common';\r\n\r\nexport type AgeAndGenderPrediction = {\r\n age: number\r\n gender: Gender\r\n genderProbability: number\r\n}\r\n\r\nexport enum Gender {\r\n FEMALE = 'female',\r\n MALE = 'male'\r\n}\r\n\r\nexport type NetOutput = { age: tf.Tensor1D, gender: tf.Tensor2D }\r\n\r\nexport type NetParams = {\r\n fc: {\r\n age: FCParams\r\n gender: FCParams\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { seperateWeightMaps } from '../faceProcessor/util';\r\nimport { TinyXception } from '../xception/TinyXception';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { AgeAndGenderPrediction, Gender, NetOutput, NetParams } from './types';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\n\r\nexport class AgeGenderNet extends NeuralNetwork<NetParams> {\r\n\r\n private _faceFeatureExtractor: TinyXception\r\n\r\n constructor(faceFeatureExtractor: TinyXception = new TinyXception(2)) {\r\n super('AgeGenderNet')\r\n this._faceFeatureExtractor = faceFeatureExtractor\r\n }\r\n\r\n public get faceFeatureExtractor(): TinyXception {\r\n return this._faceFeatureExtractor\r\n }\r\n\r\n public runNet(input: NetInput | tf.Tensor4D): NetOutput {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error(`${this._name} - load model before inference`)\r\n }\r\n\r\n return tf.tidy(() => {\r\n const bottleneckFeatures = input instanceof NetInput\r\n ? this.faceFeatureExtractor.forwardInput(input)\r\n : input\r\n\r\n const pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1)\r\n const age = fullyConnectedLayer(pooled, params.fc.age).as1D()\r\n const gender = fullyConnectedLayer(pooled, params.fc.gender)\r\n return { age, gender }\r\n })\r\n }\r\n\r\n public forwardInput(input: NetInput | tf.Tensor4D): NetOutput {\r\n return tf.tidy(() => {\r\n const { age, gender } = this.runNet(input)\r\n return { age, gender: tf.softmax(gender) }\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<NetOutput> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n public async predictAgeAndGender(input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> {\r\n const netInput = await toNetInput(input)\r\n const out = await this.forwardInput(netInput)\r\n\r\n const ages = tf.unstack(out.age)\r\n const genders = tf.unstack(out.gender)\r\n const ageAndGenderTensors = ages.map((ageTensor, i) => ({\r\n ageTensor,\r\n genderTensor: genders[i]\r\n }))\r\n\r\n const predictionsByBatch = await Promise.all(\r\n ageAndGenderTensors.map(async ({ ageTensor, genderTensor }) => {\r\n const age = (await ageTensor.data())[0]\r\n const probMale = (await genderTensor.data())[0]\r\n const isMale = probMale > 0.5\r\n const gender = isMale ? Gender.MALE : Gender.FEMALE\r\n const genderProbability = isMale ? probMale : (1 - probMale)\r\n\r\n ageTensor.dispose()\r\n genderTensor.dispose()\r\n return { age, gender, genderProbability }\r\n })\r\n )\r\n out.age.dispose()\r\n out.gender.dispose()\r\n\r\n return netInput.isBatchInput\r\n ? predictionsByBatch\r\n : predictionsByBatch[0]\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'age_gender_model'\r\n }\r\n\r\n public dispose(throwOnRedispose: boolean = true) {\r\n this.faceFeatureExtractor.dispose(throwOnRedispose)\r\n super.dispose(throwOnRedispose)\r\n }\r\n\r\n public loadClassifierParams(weights: Float32Array) {\r\n const { params, paramMappings } = this.extractClassifierParams(weights)\r\n this._params = params\r\n this._paramMappings = paramMappings\r\n }\r\n\r\n public extractClassifierParams(weights: Float32Array) {\r\n return extractParams(weights)\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n\r\n const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\r\n\r\n this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\r\n\r\n return extractParamsFromWeigthMap(classifierMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n\r\n const classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2)\r\n\r\n const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\r\n const classifierWeights = weights.slice(weights.length - classifierWeightSize)\r\n\r\n this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\r\n return this.extractClassifierParams(classifierWeights)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { IDimensions, Point } from '../classes';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\r\nimport { isEven } from '../utils';\r\n\r\nexport abstract class FaceLandmark68NetBase<\r\n TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\r\n>\r\n extends FaceProcessor<TExtractorParams> {\r\n\r\n public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\r\n\r\n const inputDimensions = originalDimensions.map(({ width, height }) => {\r\n const scale = inputSize / Math.max(height, width)\r\n return {\r\n width: width * scale,\r\n height: height * scale\r\n }\r\n })\r\n\r\n const batchSize = inputDimensions.length\r\n\r\n return tf.tidy(() => {\r\n const createInterleavedTensor = (fillX: number, fillY: number) =>\r\n tf.stack([\r\n tf.fill([68], fillX),\r\n tf.fill([68], fillY)\r\n ], 1).as2D(1, 136).as1D()\r\n\r\n const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\r\n const { width, height } = inputDimensions[batchIdx]\r\n return cond(width, height) ? Math.abs(width - height) / 2 : 0\r\n }\r\n const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\r\n const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\r\n\r\n const landmarkTensors = output\r\n .mul(tf.fill([batchSize, 136], inputSize))\r\n .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\r\n createInterleavedTensor(\r\n getPaddingX(batchIdx),\r\n getPaddingY(batchIdx)\r\n )\r\n )))\r\n .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\r\n createInterleavedTensor(\r\n inputDimensions[batchIdx].width,\r\n inputDimensions[batchIdx].height\r\n )\r\n )))\r\n\r\n return landmarkTensors as tf.Tensor2D\r\n })\r\n }\r\n\r\n public forwardInput(input: NetInput): tf.Tensor2D {\r\n return tf.tidy(() => {\r\n const out = this.runNet(input)\r\n return this.postProcess(\r\n out,\r\n input.inputSize as number,\r\n input.inputDimensions.map(([height, width]) => ({ height, width }))\r\n )\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<tf.Tensor2D> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\r\n const netInput = await toNetInput(input)\r\n const landmarkTensors = tf.tidy(\r\n () => tf.unstack(this.forwardInput(netInput))\r\n )\r\n\r\n const landmarksForBatch = await Promise.all(landmarkTensors.map(\r\n async (landmarkTensor, batchIdx) => {\r\n const landmarksArray = Array.from(await landmarkTensor.data())\r\n const xCoords = landmarksArray.filter((_, i) => isEven(i))\r\n const yCoords = landmarksArray.filter((_, i) => !isEven(i))\r\n\r\n return new FaceLandmarks68(\r\n Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),\r\n {\r\n height: netInput.getInputHeight(batchIdx),\r\n width : netInput.getInputWidth(batchIdx),\r\n }\r\n )\r\n }\r\n ))\r\n\r\n landmarkTensors.forEach(t => t.dispose())\r\n\r\n return netInput.isBatchInput\r\n ? landmarksForBatch\r\n : landmarksForBatch[0]\r\n }\r\n\r\n protected getClassifierChannelsOut(): number {\r\n return 136\r\n }\r\n}","import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\r\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\r\n\r\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\r\n\r\n constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\r\n super('FaceLandmark68Net', faceFeatureExtractor)\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'face_landmark_68_model'\r\n }\r\n\r\n protected getClassifierChannelsIn(): number {\r\n return 256\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { disposeUnusedWeightTensors, ParamMapping } from '../common';\r\nimport { loadParamsFactory } from './loadParamsFactory';\r\nimport { TinyFaceFeatureExtractorParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMapTiny(\r\n weightMap: tf.NamedTensorMap\r\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractDenseBlock3Params\r\n } = loadParamsFactory(weightMap, paramMappings)\r\n\r\n const params = {\r\n dense0: extractDenseBlock3Params('dense0', true),\r\n dense1: extractDenseBlock3Params('dense1'),\r\n dense2: extractDenseBlock3Params('dense2')\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","import { extractWeightsFactory, ParamMapping } from '../common';\r\nimport { extractorsFactory } from './extractorsFactory';\r\nimport { TinyFaceFeatureExtractorParams } from './types';\r\n\r\n\r\n\r\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const {\r\n extractDenseBlock3Params\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\r\n const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\r\n const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return {\r\n paramMappings,\r\n params: { dense0, dense1, dense2 }\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { denseBlock3 } from './denseBlock';\r\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\r\nimport { extractParamsTiny } from './extractParamsTiny';\r\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\r\n\r\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\r\n\r\n constructor() {\r\n super('TinyFaceFeatureExtractor')\r\n }\r\n\r\n public forwardInput(input: NetInput): tf.Tensor4D {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('TinyFaceFeatureExtractor - load model before inference')\r\n }\r\n\r\n return tf.tidy(() => {\r\n const batchTensor = input.toBatchTensor(112, true)\r\n const meanRgb = [122.782, 117.001, 104.298]\r\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\r\n\r\n let out = denseBlock3(normalized, params.dense0, true)\r\n out = denseBlock3(out, params.dense1)\r\n out = denseBlock3(out, params.dense2)\r\n out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\r\n\r\n return out\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<tf.Tensor4D> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'face_feature_extractor_tiny_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMapTiny(weightMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n return extractParamsTiny(weights)\r\n }\r\n}","import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\r\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\r\n\r\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\r\n\r\n constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\r\n super('FaceLandmark68TinyNet', faceFeatureExtractor)\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'face_landmark_68_tiny_model'\r\n }\r\n\r\n protected getClassifierChannelsIn(): number {\r\n return 128\r\n }\r\n}","import { FaceLandmark68Net } from './FaceLandmark68Net';\r\n\r\nexport * from './FaceLandmark68Net';\r\nexport * from './FaceLandmark68TinyNet';\r\n\r\nexport class FaceLandmarkNet extends FaceLandmark68Net {}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ScaleLayerParams } from './types';\r\n\r\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\r\n return tf.add(tf.mul(x, params.weights), params.biases)\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { scale } from './scaleLayer';\r\nimport { ConvLayerParams } from './types';\r\n\r\n\r\nfunction convLayer(\r\n x: tf.Tensor4D,\r\n params: ConvLayerParams,\r\n strides: [number, number],\r\n withRelu: boolean,\r\n padding: 'valid' | 'same' = 'same'\r\n): tf.Tensor4D {\r\n const { filters, bias } = params.conv\r\n\r\n let out = tf.conv2d(x, filters, strides, padding)\r\n out = tf.add(out, bias)\r\n out = scale(out, params.scale)\r\n return withRelu ? tf.relu(out) : out\r\n}\r\n\r\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\r\n return convLayer(x, params, [1, 1], true)\r\n}\r\n\r\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\r\n return convLayer(x, params, [1, 1], false)\r\n}\r\n\r\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\r\n return convLayer(x, params, [2, 2], true, 'valid')\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams, extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from '../common';\r\nimport { isFloat } from '../utils';\r\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\r\n const weights = extractWeights(numFilterValues)\r\n const depth = weights.length / (numFilters * filterSize * filterSize)\r\n\r\n if (isFloat(depth)) {\r\n throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\r\n }\r\n\r\n return tf.tidy(\r\n () => tf.transpose(\r\n tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\r\n [2, 3, 1, 0]\r\n )\r\n )\r\n }\r\n\r\n function extractConvParams(\r\n numFilterValues: number,\r\n numFilters: number,\r\n filterSize: number,\r\n mappedPrefix: string\r\n ): ConvParams {\r\n\r\n const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\r\n const bias = tf.tensor1d(extractWeights(numFilters))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/filters` },\r\n { paramPath: `${mappedPrefix}/bias` }\r\n )\r\n\r\n return { filters, bias }\r\n }\r\n\r\n function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\r\n\r\n const weights = tf.tensor1d(extractWeights(numWeights))\r\n const biases = tf.tensor1d(extractWeights(numWeights))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/weights` },\r\n { paramPath: `${mappedPrefix}/biases` }\r\n )\r\n\r\n return {\r\n weights,\r\n biases\r\n }\r\n }\r\n\r\n function extractConvLayerParams(\r\n numFilterValues: number,\r\n numFilters: number,\r\n filterSize: number,\r\n mappedPrefix: string\r\n ): ConvLayerParams {\r\n\r\n const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\r\n const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\r\n\r\n return { conv, scale }\r\n }\r\n\r\n function extractResidualLayerParams(\r\n numFilterValues: number,\r\n numFilters: number,\r\n filterSize: number,\r\n mappedPrefix: string,\r\n isDown: boolean = false\r\n ): ResidualLayerParams {\r\n\r\n const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\r\n const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\r\n\r\n return { conv1, conv2 }\r\n }\r\n\r\n return {\r\n extractConvLayerParams,\r\n extractResidualLayerParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractConvLayerParams,\r\n extractResidualLayerParams\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\r\n const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\r\n const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\r\n const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\r\n\r\n const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\r\n const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\r\n const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\r\n const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\r\n\r\n const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\r\n const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\r\n const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\r\n\r\n const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\r\n const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\r\n const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\r\n const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\r\n\r\n const fc = tf.tidy(\r\n () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\r\n )\r\n paramMappings.push({ paramPath: `fc` })\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n const params = {\r\n conv32_down,\r\n conv32_1,\r\n conv32_2,\r\n conv32_3,\r\n conv64_down,\r\n conv64_1,\r\n conv64_2,\r\n conv64_3,\r\n conv128_down,\r\n conv128_1,\r\n conv128_2,\r\n conv256_down,\r\n conv256_1,\r\n conv256_2,\r\n conv256_down_out,\r\n fc\r\n }\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\r\nimport { isTensor2D } from '../utils';\r\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n function extractScaleLayerParams(prefix: string): ScaleLayerParams {\r\n\r\n const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\r\n const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\r\n\r\n return { weights, biases }\r\n }\r\n\r\n function extractConvLayerParams(prefix: string): ConvLayerParams {\r\n\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\r\n const scale = extractScaleLayerParams(prefix)\r\n\r\n return { conv: { filters, bias }, scale }\r\n }\r\n\r\n function extractResidualLayerParams(prefix: string): ResidualLayerParams {\r\n return {\r\n conv1: extractConvLayerParams(`${prefix}/conv1`),\r\n conv2: extractConvLayerParams(`${prefix}/conv2`)\r\n }\r\n }\r\n\r\n return {\r\n extractConvLayerParams,\r\n extractResidualLayerParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractConvLayerParams,\r\n extractResidualLayerParams\r\n } = extractorsFactory(weightMap, paramMappings)\r\n\r\n const conv32_down = extractConvLayerParams('conv32_down')\r\n const conv32_1 = extractResidualLayerParams('conv32_1')\r\n const conv32_2 = extractResidualLayerParams('conv32_2')\r\n const conv32_3 = extractResidualLayerParams('conv32_3')\r\n\r\n const conv64_down = extractResidualLayerParams('conv64_down')\r\n const conv64_1 = extractResidualLayerParams('conv64_1')\r\n const conv64_2 = extractResidualLayerParams('conv64_2')\r\n const conv64_3 = extractResidualLayerParams('conv64_3')\r\n\r\n const conv128_down = extractResidualLayerParams('conv128_down')\r\n const conv128_1 = extractResidualLayerParams('conv128_1')\r\n const conv128_2 = extractResidualLayerParams('conv128_2')\r\n\r\n const conv256_down = extractResidualLayerParams('conv256_down')\r\n const conv256_1 = extractResidualLayerParams('conv256_1')\r\n const conv256_2 = extractResidualLayerParams('conv256_2')\r\n const conv256_down_out = extractResidualLayerParams('conv256_down_out')\r\n\r\n const fc = weightMap['fc']\r\n paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\r\n\r\n if (!isTensor2D(fc)) {\r\n throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\r\n }\r\n\r\n const params = {\r\n conv32_down,\r\n conv32_1,\r\n conv32_2,\r\n conv32_3,\r\n conv64_down,\r\n conv64_1,\r\n conv64_2,\r\n conv64_3,\r\n conv128_down,\r\n conv128_1,\r\n conv128_2,\r\n conv256_down,\r\n conv256_1,\r\n conv256_2,\r\n conv256_down_out,\r\n fc\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { conv, convDown, convNoRelu } from './convLayer';\r\nimport { ResidualLayerParams } from './types';\r\n\r\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\r\n let out = conv(x, params.conv1)\r\n out = convNoRelu(out, params.conv2)\r\n out = tf.add(out, x)\r\n out = tf.relu(out)\r\n return out\r\n}\r\n\r\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\r\n let out = convDown(x, params.conv1)\r\n out = convNoRelu(out, params.conv2)\r\n\r\n let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\r\n const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\r\n const isPad = pooled.shape[3] !== out.shape[3]\r\n const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\r\n\r\n if (isAdjustShape) {\r\n const padShapeX = [...out.shape] as [number, number, number, number]\r\n padShapeX[1] = 1\r\n const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\r\n out = tf.concat([out, zerosW], 1)\r\n\r\n const padShapeY = [...out.shape] as [number, number, number, number]\r\n padShapeY[2] = 1\r\n const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\r\n out = tf.concat([out, zerosH], 2)\r\n }\r\n\r\n pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\r\n out = tf.add(pooled, out) as tf.Tensor4D\r\n\r\n out = tf.relu(out)\r\n return out\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { convDown } from './convLayer';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { residual, residualDown } from './residualLayer';\r\nimport { NetParams } from './types';\r\n\r\n\r\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\r\n\r\n constructor() {\r\n super('FaceRecognitionNet')\r\n }\r\n\r\n public forwardInput(input: NetInput): tf.Tensor2D {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('FaceRecognitionNet - load model before inference')\r\n }\r\n\r\n return tf.tidy(() => {\r\n const batchTensor = input.toBatchTensor(150, true).toFloat()\r\n\r\n const meanRgb = [122.782, 117.001, 104.298]\r\n const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\r\n\r\n let out = convDown(normalized, params.conv32_down)\r\n out = tf.maxPool(out, 3, 2, 'valid')\r\n\r\n out = residual(out, params.conv32_1)\r\n out = residual(out, params.conv32_2)\r\n out = residual(out, params.conv32_3)\r\n\r\n out = residualDown(out, params.conv64_down)\r\n out = residual(out, params.conv64_1)\r\n out = residual(out, params.conv64_2)\r\n out = residual(out, params.conv64_3)\r\n\r\n out = residualDown(out, params.conv128_down)\r\n out = residual(out, params.conv128_1)\r\n out = residual(out, params.conv128_2)\r\n\r\n out = residualDown(out, params.conv256_down)\r\n out = residual(out, params.conv256_1)\r\n out = residual(out, params.conv256_2)\r\n out = residualDown(out, params.conv256_down_out)\r\n\r\n const globalAvg = out.mean([1, 2]) as tf.Tensor2D\r\n const fullyConnected = tf.matMul(globalAvg, params.fc)\r\n\r\n return fullyConnected\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput): Promise<tf.Tensor2D> {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\r\n const netInput = await toNetInput(input)\r\n\r\n const faceDescriptorTensors = tf.tidy(\r\n () => tf.unstack(this.forwardInput(netInput))\r\n )\r\n\r\n const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\r\n t => t.data()\r\n )) as Float32Array[]\r\n\r\n faceDescriptorTensors.forEach(t => t.dispose())\r\n\r\n return netInput.isBatchInput\r\n ? faceDescriptorsForBatch\r\n : faceDescriptorsForBatch[0]\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'face_recognition_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMap(weightMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n return extractParams(weights)\r\n }\r\n}","import { FaceRecognitionNet } from './FaceRecognitionNet';\r\n\r\nexport * from './FaceRecognitionNet';\r\n\r\nexport function createFaceRecognitionNet(weights: Float32Array) {\r\n const net = new FaceRecognitionNet()\r\n net.extractWeights(weights)\r\n return net\r\n}","export type WithFaceDescriptor<TSource> = TSource & {\r\n descriptor: Float32Array\r\n}\r\n\r\nexport function extendWithFaceDescriptor<\r\n TSource\r\n> (\r\n sourceObj: TSource,\r\n descriptor: Float32Array\r\n): WithFaceDescriptor<TSource> {\r\n\r\n const extension = { descriptor }\r\n return Object.assign({}, sourceObj, extension)\r\n}\r\n\r\n","export type WithAge<TSource> = TSource & {\r\n age: number\r\n}\r\n\r\nexport function isWithAge(obj: any): obj is WithAge<{}> {\r\n return typeof obj['age'] === 'number'\r\n}\r\n\r\nexport function extendWithAge<\r\n TSource\r\n> (\r\n sourceObj: TSource,\r\n age: number\r\n): WithAge<TSource> {\r\n\r\n const extension = { age }\r\n return Object.assign({}, sourceObj, extension)\r\n}","import { Gender } from '../ageGenderNet/types';\r\nimport { isValidProbablitiy } from '../utils';\r\n\r\nexport type WithGender<TSource> = TSource & {\r\n gender: Gender\r\n genderProbability: number\r\n}\r\n\r\nexport function isWithGender(obj: any): obj is WithGender<{}> {\r\n return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\r\n && isValidProbablitiy(obj['genderProbability'])\r\n}\r\n\r\nexport function extendWithGender<\r\n TSource\r\n> (\r\n sourceObj: TSource,\r\n gender: Gender,\r\n genderProbability: number\r\n): WithGender<TSource> {\r\n\r\n const extension = { gender, genderProbability }\r\n return Object.assign({}, sourceObj, extension)\r\n}","export interface IMtcnnOptions {\r\n minFaceSize?: number\r\n scaleFactor?: number\r\n maxNumScales?: number\r\n scoreThresholds?: number[]\r\n scaleSteps?: number[]\r\n}\r\n\r\nexport class MtcnnOptions {\r\n protected _name: string = 'MtcnnOptions'\r\n\r\n private _minFaceSize: number\r\n private _scaleFactor: number\r\n private _maxNumScales: number\r\n private _scoreThresholds: number[]\r\n private _scaleSteps: number[] | undefined\r\n\r\n constructor({ minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps }: IMtcnnOptions = {}) {\r\n this._minFaceSize = minFaceSize || 20\r\n this._scaleFactor = scaleFactor || 0.709\r\n this._maxNumScales = maxNumScales || 10\r\n this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7]\r\n this._scaleSteps = scaleSteps\r\n\r\n if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {\r\n throw new Error(`${this._name} - expected minFaceSize to be a number > 0`)\r\n }\r\n\r\n if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {\r\n throw new Error(`${this._name} - expected scaleFactor to be a number between 0 and 1`)\r\n }\r\n\r\n if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {\r\n throw new Error(`${this._name} - expected maxNumScales to be a number > 0`)\r\n }\r\n\r\n if (\r\n !Array.isArray(this._scoreThresholds)\r\n || this._scoreThresholds.length !== 3\r\n || this._scoreThresholds.some(th => typeof th !== 'number')\r\n ) {\r\n throw new Error(`${this._name} - expected scoreThresholds to be an array of numbers of length 3`)\r\n }\r\n\r\n if (\r\n this._scaleSteps\r\n && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(th => typeof th !== 'number'))\r\n ) {\r\n throw new Error(`${this._name} - expected scaleSteps to be an array of numbers`)\r\n }\r\n }\r\n\r\n get minFaceSize(): number { return this._minFaceSize }\r\n get scaleFactor(): number { return this._scaleFactor }\r\n get maxNumScales(): number { return this._maxNumScales }\r\n get scoreThresholds(): number[] { return this._scoreThresholds }\r\n get scaleSteps(): number[] | undefined { return this._scaleSteps }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ExtractWeightsFunction, ParamMapping, ConvParams, extractWeightsFactory } from '../common';\r\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\r\n\r\n const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\r\n const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\r\n const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\r\n const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\r\n const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/filters` },\r\n { paramPath: `${mappedPrefix}/batch_norm_scale` },\r\n { paramPath: `${mappedPrefix}/batch_norm_offset` },\r\n { paramPath: `${mappedPrefix}/batch_norm_mean` },\r\n { paramPath: `${mappedPrefix}/batch_norm_variance` }\r\n )\r\n\r\n return {\r\n filters,\r\n batch_norm_scale,\r\n batch_norm_offset,\r\n batch_norm_mean,\r\n batch_norm_variance\r\n }\r\n }\r\n\r\n function extractConvParams(\r\n channelsIn: number,\r\n channelsOut: number,\r\n filterSize: number,\r\n mappedPrefix: string,\r\n isPointwiseConv?: boolean\r\n ): ConvParams {\r\n\r\n const filters = tf.tensor4d(\r\n extractWeights(channelsIn * channelsOut * filterSize * filterSize),\r\n [filterSize, filterSize, channelsIn, channelsOut]\r\n )\r\n const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/filters` },\r\n { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\r\n )\r\n\r\n return { filters, bias }\r\n }\r\n\r\n function extractPointwiseConvParams(\r\n channelsIn: number,\r\n channelsOut: number,\r\n filterSize: number,\r\n mappedPrefix: string\r\n ): PointwiseConvParams {\r\n\r\n const {\r\n filters,\r\n bias\r\n } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\r\n\r\n return {\r\n filters,\r\n batch_norm_offset: bias\r\n }\r\n }\r\n\r\n function extractConvPairParams(\r\n channelsIn: number,\r\n channelsOut: number,\r\n mappedPrefix: string\r\n ): MobileNetV1.ConvPairParams {\r\n\r\n const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\r\n const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\r\n\r\n return { depthwise_conv, pointwise_conv }\r\n }\r\n\r\n function extractMobilenetV1Params(): MobileNetV1.Params {\r\n\r\n const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\r\n\r\n const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\r\n const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\r\n const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\r\n const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\r\n const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\r\n const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\r\n const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\r\n const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\r\n const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\r\n const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\r\n const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\r\n const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\r\n const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\r\n\r\n return {\r\n conv_0,\r\n conv_1,\r\n conv_2,\r\n conv_3,\r\n conv_4,\r\n conv_5,\r\n conv_6,\r\n conv_7,\r\n conv_8,\r\n conv_9,\r\n conv_10,\r\n conv_11,\r\n conv_12,\r\n conv_13\r\n }\r\n }\r\n\r\n function extractPredictionLayerParams(): PredictionLayerParams {\r\n const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\r\n const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\r\n const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\r\n const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\r\n const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\r\n const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\r\n const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\r\n const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\r\n\r\n const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\r\n const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\r\n const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\r\n const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\r\n const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\r\n const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\r\n const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\r\n const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\r\n const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\r\n const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\r\n const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\r\n const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\r\n\r\n const box_predictor_0 = {\r\n box_encoding_predictor: box_encoding_0_predictor,\r\n class_predictor: class_predictor_0\r\n }\r\n const box_predictor_1 = {\r\n box_encoding_predictor: box_encoding_1_predictor,\r\n class_predictor: class_predictor_1\r\n }\r\n const box_predictor_2 = {\r\n box_encoding_predictor: box_encoding_2_predictor,\r\n class_predictor: class_predictor_2\r\n }\r\n const box_predictor_3 = {\r\n box_encoding_predictor: box_encoding_3_predictor,\r\n class_predictor: class_predictor_3\r\n }\r\n const box_predictor_4 = {\r\n box_encoding_predictor: box_encoding_4_predictor,\r\n class_predictor: class_predictor_4\r\n }\r\n const box_predictor_5 = {\r\n box_encoding_predictor: box_encoding_5_predictor,\r\n class_predictor: class_predictor_5\r\n }\r\n\r\n return {\r\n conv_0,\r\n conv_1,\r\n conv_2,\r\n conv_3,\r\n conv_4,\r\n conv_5,\r\n conv_6,\r\n conv_7,\r\n box_predictor_0,\r\n box_predictor_1,\r\n box_predictor_2,\r\n box_predictor_3,\r\n box_predictor_4,\r\n box_predictor_5\r\n }\r\n }\r\n\r\n return {\r\n extractMobilenetV1Params,\r\n extractPredictionLayerParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const {\r\n extractMobilenetV1Params,\r\n extractPredictionLayerParams\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n const mobilenetv1 = extractMobilenetV1Params()\r\n const prediction_layer = extractPredictionLayerParams()\r\n const extra_dim = tf.tensor3d(\r\n extractWeights(5118 * 4),\r\n [1, 5118, 4]\r\n )\r\n const output_layer = {\r\n extra_dim\r\n }\r\n\r\n paramMappings.push({ paramPath: 'output_layer/extra_dim' })\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return {\r\n params: {\r\n mobilenetv1,\r\n prediction_layer,\r\n output_layer\r\n },\r\n paramMappings\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from '../common';\r\nimport { isTensor3D } from '../utils';\r\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\r\n\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\r\n const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\r\n\r\n return { filters, batch_norm_offset }\r\n }\r\n\r\n function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\r\n\r\n const mappedPrefix = `mobilenetv1/conv_${idx}`\r\n const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\r\n const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\r\n const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\r\n\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\r\n const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\r\n const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\r\n const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\r\n const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\r\n\r\n return {\r\n depthwise_conv: {\r\n filters,\r\n batch_norm_scale,\r\n batch_norm_offset,\r\n batch_norm_mean,\r\n batch_norm_variance\r\n },\r\n pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\r\n }\r\n }\r\n\r\n function extractMobilenetV1Params(): MobileNetV1.Params {\r\n return {\r\n conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\r\n conv_1: extractConvPairParams(1),\r\n conv_2: extractConvPairParams(2),\r\n conv_3: extractConvPairParams(3),\r\n conv_4: extractConvPairParams(4),\r\n conv_5: extractConvPairParams(5),\r\n conv_6: extractConvPairParams(6),\r\n conv_7: extractConvPairParams(7),\r\n conv_8: extractConvPairParams(8),\r\n conv_9: extractConvPairParams(9),\r\n conv_10: extractConvPairParams(10),\r\n conv_11: extractConvPairParams(11),\r\n conv_12: extractConvPairParams(12),\r\n conv_13: extractConvPairParams(13)\r\n }\r\n }\r\n\r\n function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\r\n\r\n return { filters, bias }\r\n }\r\n\r\n function extractBoxPredictorParams(idx: number): BoxPredictionParams {\r\n\r\n const box_encoding_predictor = extractConvParams(\r\n `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\r\n `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\r\n )\r\n const class_predictor = extractConvParams(\r\n `Prediction/BoxPredictor_${idx}/ClassPredictor`,\r\n `prediction_layer/box_predictor_${idx}/class_predictor`\r\n )\r\n\r\n return { box_encoding_predictor, class_predictor }\r\n }\r\n\r\n function extractPredictionLayerParams(): PredictionLayerParams {\r\n return {\r\n conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\r\n conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\r\n conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\r\n conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\r\n conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\r\n conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\r\n conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\r\n conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\r\n box_predictor_0: extractBoxPredictorParams(0),\r\n box_predictor_1: extractBoxPredictorParams(1),\r\n box_predictor_2: extractBoxPredictorParams(2),\r\n box_predictor_3: extractBoxPredictorParams(3),\r\n box_predictor_4: extractBoxPredictorParams(4),\r\n box_predictor_5: extractBoxPredictorParams(5)\r\n }\r\n }\r\n\r\n return {\r\n extractMobilenetV1Params,\r\n extractPredictionLayerParams\r\n }\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractMobilenetV1Params,\r\n extractPredictionLayerParams\r\n } = extractorsFactory(weightMap, paramMappings)\r\n\r\n const extra_dim = weightMap['Output/extra_dim']\r\n paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\r\n\r\n if (!isTensor3D(extra_dim)) {\r\n throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\r\n }\r\n\r\n const params = {\r\n mobilenetv1: extractMobilenetV1Params(),\r\n prediction_layer: extractPredictionLayerParams(),\r\n output_layer: {\r\n extra_dim\r\n }\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { PointwiseConvParams } from './types';\r\n\r\nexport function pointwiseConvLayer(\r\n x: tf.Tensor4D,\r\n params: PointwiseConvParams,\r\n strides: [number, number]\r\n) {\r\n return tf.tidy(() => {\r\n\r\n let out = tf.conv2d(x, params.filters, strides, 'same')\r\n out = tf.add(out, params.batch_norm_offset)\r\n return tf.clipByValue(out, 0, 6)\r\n\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\r\nimport { MobileNetV1 } from './types';\r\n\r\nconst epsilon = 0.0010000000474974513\r\n\r\nfunction depthwiseConvLayer(\r\n x: tf.Tensor4D,\r\n params: MobileNetV1.DepthwiseConvParams,\r\n strides: [number, number]\r\n) {\r\n return tf.tidy(() => {\r\n\r\n let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\r\n out = tf.batchNorm<tf.Rank.R4>(\r\n out,\r\n params.batch_norm_mean,\r\n params.batch_norm_variance,\r\n params.batch_norm_offset,\r\n params.batch_norm_scale,\r\n epsilon\r\n )\r\n return tf.clipByValue(out, 0, 6)\r\n\r\n })\r\n}\r\n\r\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\r\n return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\r\n}\r\n\r\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\r\n return tf.tidy(() => {\r\n\r\n let conv11 = null\r\n let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\r\n\r\n const convPairParams = [\r\n params.conv_1,\r\n params.conv_2,\r\n params.conv_3,\r\n params.conv_4,\r\n params.conv_5,\r\n params.conv_6,\r\n params.conv_7,\r\n params.conv_8,\r\n params.conv_9,\r\n params.conv_10,\r\n params.conv_11,\r\n params.conv_12,\r\n params.conv_13\r\n ]\r\n\r\n convPairParams.forEach((param, i) => {\r\n const layerIdx = i + 1\r\n const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\r\n out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\r\n out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\r\n if (layerIdx === 11) {\r\n conv11 = out\r\n }\r\n })\r\n\r\n if (conv11 === null) {\r\n throw new Error('mobileNetV1 - output of conv layer 11 is null')\r\n }\r\n\r\n return {\r\n out,\r\n conv11: conv11 as any\r\n }\r\n\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core'\r\n\r\nexport function nonMaxSuppression(\r\n boxes: tf.Tensor2D,\r\n scores: number[],\r\n maxOutputSize: number,\r\n iouThreshold: number,\r\n scoreThreshold: number\r\n): number[] {\r\n\r\n const numBoxes = boxes.shape[0]\r\n const outputSize = Math.min(\r\n maxOutputSize,\r\n numBoxes\r\n )\r\n\r\n const candidates = scores\r\n .map((score, boxIndex) => ({ score, boxIndex }))\r\n .filter(c => c.score > scoreThreshold)\r\n .sort((c1, c2) => c2.score - c1.score)\r\n\r\n const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\r\n\r\n const selected: number[] = []\r\n\r\n candidates.forEach(c => {\r\n if (selected.length >= outputSize) {\r\n return\r\n }\r\n const originalScore = c.score\r\n\r\n for (let j = selected.length - 1; j >= 0; --j) {\r\n const iou = IOU(boxes, c.boxIndex, selected[j])\r\n if (iou === 0.0) {\r\n continue\r\n }\r\n c.score *= suppressFunc(iou)\r\n if (c.score <= scoreThreshold) {\r\n break\r\n }\r\n }\r\n\r\n if (originalScore === c.score) {\r\n selected.push(c.boxIndex)\r\n }\r\n })\r\n\r\n return selected\r\n}\r\n\r\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\r\n const boxesData = boxes.arraySync()\r\n const yminI = Math.min(boxesData[i][0], boxesData[i][2])\r\n const xminI = Math.min(boxesData[i][1], boxesData[i][3])\r\n const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])\r\n const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])\r\n const yminJ = Math.min(boxesData[j][0], boxesData[j][2])\r\n const xminJ = Math.min(boxesData[j][1], boxesData[j][3])\r\n const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])\r\n const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])\r\n const areaI = (ymaxI - yminI) * (xmaxI - xminI)\r\n const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\r\n if (areaI <= 0 || areaJ <= 0) {\r\n return 0.0\r\n }\r\n const intersectionYmin = Math.max(yminI, yminJ)\r\n const intersectionXmin = Math.max(xminI, xminJ)\r\n const intersectionYmax = Math.min(ymaxI, ymaxJ)\r\n const intersectionXmax = Math.min(xmaxI, xmaxJ)\r\n const intersectionArea =\r\n Math.max(intersectionYmax - intersectionYmin, 0.0) *\r\n Math.max(intersectionXmax - intersectionXmin, 0.0)\r\n return intersectionArea / (areaI + areaJ - intersectionArea)\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { OutputLayerParams } from './types';\r\n\r\n\r\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\r\n const vec = tf.unstack(tf.transpose(x, [1, 0]))\r\n\r\n const sizes = [\r\n tf.sub(vec[2], vec[0]),\r\n tf.sub(vec[3], vec[1])\r\n ]\r\n\r\n const centers = [\r\n tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\r\n tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\r\n ]\r\n\r\n return {\r\n sizes,\r\n centers\r\n }\r\n}\r\n\r\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\r\n const {\r\n sizes,\r\n centers\r\n } = getCenterCoordinatesAndSizesLayer(x0)\r\n\r\n const vec = tf.unstack(tf.transpose(x1, [1, 0]))\r\n\r\n const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\r\n const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\r\n\r\n const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\r\n const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\r\n\r\n return tf.transpose(\r\n tf.stack([\r\n tf.sub(add0_out, div0_out),\r\n tf.sub(add1_out, div1_out),\r\n tf.add(add0_out, div0_out),\r\n tf.add(add1_out, div1_out)\r\n ]),\r\n [1, 0]\r\n )\r\n}\r\n\r\nexport function outputLayer(\r\n boxPredictions: tf.Tensor4D,\r\n classPredictions: tf.Tensor4D,\r\n params: OutputLayerParams\r\n) {\r\n return tf.tidy(() => {\r\n\r\n const batchSize = boxPredictions.shape[0]\r\n\r\n let boxes = decodeBoxesLayer(\r\n tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\r\n tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\r\n )\r\n boxes = tf.reshape(\r\n boxes,\r\n [batchSize, (boxes.shape[0] / batchSize), 4]\r\n )\r\n\r\n const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\r\n let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\r\n\r\n scores = tf.reshape(\r\n scores,\r\n [batchSize, scores.shape[1] as number]\r\n )\r\n\r\n const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\r\n const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\r\n\r\n return {\r\n boxes: boxesByBatch,\r\n scores: scoresByBatch\r\n }\r\n\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { convLayer } from '../common';\r\nimport { BoxPredictionParams } from './types';\r\n\r\n\r\nexport function boxPredictionLayer(\r\n x: tf.Tensor4D,\r\n params: BoxPredictionParams\r\n) {\r\n return tf.tidy(() => {\r\n\r\n const batchSize = x.shape[0]\r\n\r\n const boxPredictionEncoding = tf.reshape(\r\n convLayer(x, params.box_encoding_predictor),\r\n [batchSize, -1, 1, 4]\r\n )\r\n const classPrediction = tf.reshape(\r\n convLayer(x, params.class_predictor),\r\n [batchSize, -1, 3]\r\n )\r\n\r\n return {\r\n boxPredictionEncoding,\r\n classPrediction\r\n }\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { boxPredictionLayer } from './boxPredictionLayer';\r\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\r\nimport { PredictionLayerParams } from './types';\r\n\r\nexport function predictionLayer(\r\n x: tf.Tensor4D,\r\n conv11: tf.Tensor4D,\r\n params: PredictionLayerParams\r\n) {\r\n return tf.tidy(() => {\r\n\r\n const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\r\n const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\r\n const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\r\n const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\r\n const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\r\n const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\r\n const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\r\n const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\r\n\r\n const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\r\n const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\r\n const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\r\n const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\r\n const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\r\n const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\r\n\r\n const boxPredictions = tf.concat([\r\n boxPrediction0.boxPredictionEncoding,\r\n boxPrediction1.boxPredictionEncoding,\r\n boxPrediction2.boxPredictionEncoding,\r\n boxPrediction3.boxPredictionEncoding,\r\n boxPrediction4.boxPredictionEncoding,\r\n boxPrediction5.boxPredictionEncoding\r\n ], 1) as tf.Tensor4D\r\n\r\n const classPredictions = tf.concat([\r\n boxPrediction0.classPrediction,\r\n boxPrediction1.classPrediction,\r\n boxPrediction2.classPrediction,\r\n boxPrediction3.classPrediction,\r\n boxPrediction4.classPrediction,\r\n boxPrediction5.classPrediction\r\n ], 1) as tf.Tensor4D\r\n\r\n return {\r\n boxPredictions,\r\n classPredictions\r\n }\r\n })\r\n}","export interface ISsdMobilenetv1Options {\r\n minConfidence?: number\r\n maxResults?: number\r\n}\r\n\r\nexport class SsdMobilenetv1Options {\r\n protected _name: string = 'SsdMobilenetv1Options'\r\n\r\n private _minConfidence: number\r\n private _maxResults: number\r\n\r\n constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\r\n this._minConfidence = minConfidence || 0.5\r\n this._maxResults = maxResults || 100\r\n\r\n if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\r\n throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\r\n }\r\n\r\n if (typeof this._maxResults !== 'number') {\r\n throw new Error(`${this._name} - expected maxResults to be a number`)\r\n }\r\n }\r\n\r\n get minConfidence(): number { return this._minConfidence }\r\n get maxResults(): number { return this._maxResults }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Rect } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { mobileNetV1 } from './mobileNetV1';\r\nimport { nonMaxSuppression } from './nonMaxSuppression';\r\nimport { outputLayer } from './outputLayer';\r\nimport { predictionLayer } from './predictionLayer';\r\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\r\nimport { NetParams } from './types';\r\n\r\n\r\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\r\n\r\n constructor() {\r\n super('SsdMobilenetv1')\r\n }\r\n\r\n public forwardInput(input: NetInput) {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('SsdMobilenetv1 - load model before inference')\r\n }\r\n\r\n return tf.tidy(() => {\r\n const batchTensor = input.toBatchTensor(512, false).toFloat()\r\n\r\n const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\r\n const features = mobileNetV1(x, params.mobilenetv1)\r\n\r\n const {\r\n boxPredictions,\r\n classPredictions\r\n } = predictionLayer(features.out, features.conv11, params.prediction_layer)\r\n\r\n return outputLayer(boxPredictions, classPredictions, params.output_layer)\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput) {\r\n return this.forwardInput(await toNetInput(input))\r\n }\r\n\r\n public async locateFaces(\r\n input: TNetInput,\r\n options: ISsdMobilenetv1Options = {}\r\n ): Promise<FaceDetection[]> {\r\n\r\n const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\r\n\r\n const netInput = await toNetInput(input)\r\n\r\n const {\r\n boxes: _boxes,\r\n scores: _scores\r\n } = this.forwardInput(netInput)\r\n\r\n\r\n // TODO batches\r\n const boxes = _boxes[0]\r\n const scores = _scores[0]\r\n for (let i = 1; i < _boxes.length; i++) {\r\n _boxes[i].dispose()\r\n _scores[i].dispose()\r\n }\r\n\r\n // TODO find a better way to filter by minConfidence\r\n const scoresData = Array.from(await scores.data())\r\n\r\n const iouThreshold = 0.5\r\n const indices = nonMaxSuppression(\r\n boxes,\r\n scoresData,\r\n maxResults,\r\n iouThreshold,\r\n minConfidence\r\n )\r\n\r\n const reshapedDims = netInput.getReshapedInputDimensions(0)\r\n const inputSize = netInput.inputSize as number\r\n const padX = inputSize / reshapedDims.width\r\n const padY = inputSize / reshapedDims.height\r\n\r\n const boxesData = boxes.arraySync()\r\n const results = indices\r\n .map(idx => {\r\n const [top, bottom] = [\r\n Math.max(0, boxesData[idx][0]),\r\n Math.min(1.0, boxesData[idx][2])\r\n ].map(val => val * padY)\r\n const [left, right] = [\r\n Math.max(0, boxesData[idx][1]),\r\n Math.min(1.0, boxesData[idx][3])\r\n ].map(val => val * padX)\r\n return new FaceDetection(\r\n scoresData[idx],\r\n new Rect(\r\n left,\r\n top,\r\n right - left,\r\n bottom - top\r\n ),\r\n {\r\n height: netInput.getInputHeight(0),\r\n width: netInput.getInputWidth(0)\r\n }\r\n )\r\n })\r\n\r\n boxes.dispose()\r\n scores.dispose()\r\n\r\n return results\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'ssd_mobilenetv1_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMap(weightMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n return extractParams(weights)\r\n }\r\n}","import { SsdMobilenetv1 } from './SsdMobilenetv1';\r\n\r\nexport * from './SsdMobilenetv1';\r\nexport * from './SsdMobilenetv1Options';\r\n\r\nexport function createSsdMobilenetv1(weights: Float32Array) {\r\n const net = new SsdMobilenetv1()\r\n net.extractWeights(weights)\r\n return net\r\n}\r\n\r\nexport function createFaceDetectionNet(weights: Float32Array) {\r\n return createSsdMobilenetv1(weights)\r\n}\r\n\r\n// alias for backward compatibily\r\nexport class FaceDetectionNet extends SsdMobilenetv1 {}","import { Point } from '../classes';\r\n\r\nexport const IOU_THRESHOLD = 0.4\r\n\r\nexport const BOX_ANCHORS = [\r\n new Point(0.738768, 0.874946),\r\n new Point(2.42204, 2.65704),\r\n new Point(4.30971, 7.04493),\r\n new Point(10.246, 4.59428),\r\n new Point(12.6868, 11.8741)\r\n]\r\n\r\nexport const BOX_ANCHORS_SEPARABLE = [\r\n new Point(1.603231, 2.094468),\r\n new Point(6.041143, 7.080126),\r\n new Point(2.882459, 3.518061),\r\n new Point(4.266906, 5.178857),\r\n new Point(9.041765, 10.66308)\r\n]\r\n\r\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\r\n\r\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\r\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'","import { Point } from '../classes/Point';\r\n\r\nexport type TinyYolov2Config = {\r\n withSeparableConvs: boolean\r\n iouThreshold: number\r\n anchors: Point[]\r\n classes: string[]\r\n meanRgb?: [number, number, number]\r\n withClassScores?: boolean,\r\n filterSizes?: number[]\r\n isFirstLayerConv2d?: boolean\r\n}\r\n\r\nconst isNumber = (arg: any) => typeof arg === 'number'\r\n\r\nexport function validateConfig(config: any) {\r\n if (!config) {\r\n throw new Error(`invalid config: ${config}`)\r\n }\r\n\r\n if (typeof config.withSeparableConvs !== 'boolean') {\r\n throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\r\n }\r\n\r\n if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\r\n throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\r\n }\r\n\r\n if (\r\n !Array.isArray(config.classes)\r\n || !config.classes.length\r\n || !config.classes.every((c: any) => typeof c === 'string')\r\n ) {\r\n\r\n throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\r\n }\r\n\r\n if (\r\n !Array.isArray(config.anchors)\r\n || !config.anchors.length\r\n || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\r\n ) {\r\n\r\n throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\r\n }\r\n\r\n if (config.meanRgb && (\r\n !Array.isArray(config.meanRgb)\r\n || config.meanRgb.length !== 3\r\n || !config.meanRgb.every(isNumber)\r\n )) {\r\n\r\n throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n const min = tf.mul(x, tf.scalar(0.10000000149011612))\r\n return tf.add(tf.relu(tf.sub(x, min)), min)\r\n //return tf.maximum(x, min)\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { leaky } from './leaky';\r\nimport { ConvWithBatchNorm } from './types';\r\n\r\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\r\n\r\n out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\r\n out = tf.sub(out, params.bn.sub)\r\n out = tf.mul(out, params.bn.truediv)\r\n out = tf.add(out, params.conv.bias)\r\n\r\n return leaky(out)\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { SeparableConvParams } from '../common/types';\r\nimport { leaky } from './leaky';\r\n\r\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\r\n\r\n out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\r\n out = tf.add(out, params.bias)\r\n\r\n return leaky(out)\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { extractConvParamsFactory } from '../common';\r\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\r\nimport { ExtractWeightsFunction, ParamMapping } from '../common/types';\r\nimport { TinyYolov2Config } from './config';\r\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n\r\n function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\r\n\r\n const sub = tf.tensor1d(extractWeights(size))\r\n const truediv = tf.tensor1d(extractWeights(size))\r\n\r\n paramMappings.push(\r\n { paramPath: `${mappedPrefix}/sub` },\r\n { paramPath: `${mappedPrefix}/truediv` }\r\n )\r\n\r\n return { sub, truediv }\r\n }\r\n\r\n function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\r\n\r\n const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\r\n const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\r\n\r\n return { conv, bn }\r\n }\r\n const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\r\n\r\n return {\r\n extractConvParams,\r\n extractConvWithBatchNormParams,\r\n extractSeparableConvParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParams(\r\n weights: Float32Array,\r\n config: TinyYolov2Config,\r\n boxEncodingSize: number,\r\n filterSizes: number[]\r\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractConvParams,\r\n extractConvWithBatchNormParams,\r\n extractSeparableConvParams\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n let params: TinyYolov2NetParams\r\n\r\n if (config.withSeparableConvs) {\r\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\r\n\r\n const conv0 = config.isFirstLayerConv2d\r\n ? extractConvParams(s0, s1, 3, 'conv0')\r\n : extractSeparableConvParams(s0, s1, 'conv0')\r\n const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\r\n const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\r\n const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\r\n const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\r\n const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\r\n const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\r\n const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\r\n const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\r\n params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\r\n } else {\r\n const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\r\n const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\r\n const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\r\n const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\r\n const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\r\n const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\r\n const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\r\n const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\r\n const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\r\n const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\r\n params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\r\n }\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n\r\n return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams } from '../common';\r\nimport { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\r\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\r\nimport { ParamMapping } from '../common/types';\r\nimport { TinyYolov2Config } from './config';\r\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n function extractBatchNormParams(prefix: string): BatchNorm {\r\n const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\r\n const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\r\n return { sub, truediv }\r\n }\r\n\r\n function extractConvParams(prefix: string): ConvParams {\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n return { filters, bias }\r\n }\r\n\r\n function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\r\n const conv = extractConvParams(`${prefix}/conv`)\r\n const bn = extractBatchNormParams(`${prefix}/bn`)\r\n return { conv, bn }\r\n }\r\n\r\n const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\r\n\r\n return {\r\n extractConvParams,\r\n extractConvWithBatchNormParams,\r\n extractSeparableConvParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap,\r\n config: TinyYolov2Config\r\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractConvParams,\r\n extractConvWithBatchNormParams,\r\n extractSeparableConvParams\r\n } = extractorsFactory(weightMap, paramMappings)\r\n\r\n let params: TinyYolov2NetParams\r\n\r\n if (config.withSeparableConvs) {\r\n const numFilters = (config.filterSizes && config.filterSizes.length || 9)\r\n params = {\r\n conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\r\n conv1: extractSeparableConvParams('conv1'),\r\n conv2: extractSeparableConvParams('conv2'),\r\n conv3: extractSeparableConvParams('conv3'),\r\n conv4: extractSeparableConvParams('conv4'),\r\n conv5: extractSeparableConvParams('conv5'),\r\n conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\r\n conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\r\n conv8: extractConvParams('conv8')\r\n }\r\n } else {\r\n params = {\r\n conv0: extractConvWithBatchNormParams('conv0'),\r\n conv1: extractConvWithBatchNormParams('conv1'),\r\n conv2: extractConvWithBatchNormParams('conv2'),\r\n conv3: extractConvWithBatchNormParams('conv3'),\r\n conv4: extractConvWithBatchNormParams('conv4'),\r\n conv5: extractConvWithBatchNormParams('conv5'),\r\n conv6: extractConvWithBatchNormParams('conv6'),\r\n conv7: extractConvWithBatchNormParams('conv7'),\r\n conv8: extractConvParams('conv8')\r\n }\r\n }\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params, paramMappings }\r\n}","export enum TinyYolov2SizeType {\r\n XS = 224,\r\n SM = 320,\r\n MD = 416,\r\n LG = 608\r\n}\r\n\r\nexport interface ITinyYolov2Options {\r\n inputSize?: number\r\n scoreThreshold?: number\r\n}\r\n\r\nexport class TinyYolov2Options {\r\n protected _name: string = 'TinyYolov2Options'\r\n\r\n private _inputSize: number\r\n private _scoreThreshold: number\r\n\r\n constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\r\n this._inputSize = inputSize || 416\r\n this._scoreThreshold = scoreThreshold || 0.5\r\n\r\n if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\r\n throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\r\n }\r\n\r\n if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\r\n throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\r\n }\r\n }\r\n\r\n get inputSize(): number { return this._inputSize }\r\n get scoreThreshold(): number { return this._scoreThreshold }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { BoundingBox } from '../classes/BoundingBox';\r\nimport { Dimensions } from '../classes/Dimensions';\r\nimport { ObjectDetection } from '../classes/ObjectDetection';\r\nimport { convLayer } from '../common';\r\nimport { ConvParams, SeparableConvParams } from '../common/types';\r\nimport { toNetInput } from '../dom';\r\nimport { NetInput } from '../dom/NetInput';\r\nimport { TNetInput } from '../dom/types';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { sigmoid } from '../ops';\r\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\r\nimport { normalize } from '../ops/normalize';\r\nimport { TinyYolov2Config, validateConfig } from './config';\r\nimport { convWithBatchNorm } from './convWithBatchNorm';\r\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { leaky } from './leaky';\r\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\r\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\r\n\r\nexport class TinyYolov2Base extends NeuralNetwork<TinyYolov2NetParams> {\r\n\r\n public static DEFAULT_FILTER_SIZES = [\r\n 3, 16, 32, 64, 128, 256, 512, 1024, 1024\r\n ]\r\n\r\n private _config: TinyYolov2Config\r\n\r\n constructor(config: TinyYolov2Config) {\r\n super('TinyYolov2')\r\n validateConfig(config)\r\n this._config = config\r\n }\r\n\r\n public get config(): TinyYolov2Config {\r\n return this._config\r\n }\r\n\r\n public get withClassScores(): boolean {\r\n return this.config.withClassScores || this.config.classes.length > 1\r\n }\r\n\r\n public get boxEncodingSize(): number {\r\n return 5 + (this.withClassScores ? this.config.classes.length : 0)\r\n }\r\n\r\n public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\r\n\r\n let out = convWithBatchNorm(x, params.conv0)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = convWithBatchNorm(out, params.conv1)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = convWithBatchNorm(out, params.conv2)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = convWithBatchNorm(out, params.conv3)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = convWithBatchNorm(out, params.conv4)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = convWithBatchNorm(out, params.conv5)\r\n out = tf.maxPool(out, [2, 2], [1, 1], 'same')\r\n out = convWithBatchNorm(out, params.conv6)\r\n out = convWithBatchNorm(out, params.conv7)\r\n\r\n return convLayer(out, params.conv8, 'valid', false)\r\n }\r\n\r\n public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\r\n\r\n let out = this.config.isFirstLayerConv2d\r\n ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\r\n : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = depthwiseSeparableConv(out, params.conv1)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = depthwiseSeparableConv(out, params.conv2)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = depthwiseSeparableConv(out, params.conv3)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = depthwiseSeparableConv(out, params.conv4)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = depthwiseSeparableConv(out, params.conv5)\r\n out = tf.maxPool(out, [2, 2], [1, 1], 'same')\r\n out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\r\n out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\r\n\r\n return convLayer(out, params.conv8, 'valid', false)\r\n }\r\n\r\n public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('TinyYolov2 - load model before inference')\r\n }\r\n\r\n return tf.tidy(() => {\r\n\r\n let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\r\n batchTensor = this.config.meanRgb\r\n ? normalize(batchTensor, this.config.meanRgb)\r\n : batchTensor\r\n batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\r\n\r\n return this.config.withSeparableConvs\r\n ? this.runMobilenet(batchTensor, params as MobilenetParams)\r\n : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\r\n })\r\n }\r\n\r\n public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\r\n return await this.forwardInput(await toNetInput(input), inputSize)\r\n }\r\n\r\n public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\r\n\r\n const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\r\n\r\n const netInput = await toNetInput(input)\r\n const out = await this.forwardInput(netInput, inputSize)\r\n const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\r\n\r\n const inputDimensions = {\r\n width: netInput.getInputWidth(0),\r\n height: netInput.getInputHeight(0)\r\n }\r\n\r\n const results = await this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\r\n out.dispose()\r\n out0.dispose()\r\n\r\n const boxes = results.map(res => res.box)\r\n const scores = results.map(res => res.score)\r\n const classScores = results.map(res => res.classScore)\r\n const classNames = results.map(res => this.config.classes[res.label])\r\n\r\n const indices = nonMaxSuppression(\r\n boxes.map(box => box.rescale(inputSize)),\r\n scores,\r\n this.config.iouThreshold,\r\n true\r\n )\r\n\r\n const detections = indices.map(idx =>\r\n new ObjectDetection(\r\n scores[idx],\r\n classScores[idx],\r\n classNames[idx],\r\n boxes[idx],\r\n inputDimensions\r\n )\r\n )\r\n\r\n return detections\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return ''\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMap(weightMap, this.config)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n const filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES\r\n\r\n const numFilters = filterSizes ? filterSizes.length : undefined\r\n if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\r\n throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\r\n }\r\n return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\r\n }\r\n\r\n protected async extractBoxes(\r\n outputTensor: tf.Tensor4D,\r\n inputBlobDimensions: Dimensions,\r\n scoreThreshold?: number\r\n ) {\r\n\r\n const { width, height } = inputBlobDimensions\r\n const inputSize = Math.max(width, height)\r\n const correctionFactorX = inputSize / width\r\n const correctionFactorY = inputSize / height\r\n\r\n const numCells = outputTensor.shape[1]\r\n const numBoxes = this.config.anchors.length\r\n\r\n const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\r\n const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\r\n\r\n const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\r\n const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\r\n const classScores = this.withClassScores\r\n ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\r\n : tf.scalar(0)\r\n return [boxes, scores, classScores]\r\n })\r\n\r\n const results = []\r\n\r\n const scoresData = await scoresTensor.array()\r\n const boxesData = await boxesTensor.array()\r\n for (let row = 0; row < numCells; row ++) {\r\n for (let col = 0; col < numCells; col ++) {\r\n for (let anchor = 0; anchor < numBoxes; anchor ++) {\r\n\r\n const score = sigmoid(scoresData[row][col][anchor][0]);\r\n if (!scoreThreshold || score > scoreThreshold) {\r\n const ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX\r\n const ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY\r\n const width = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\r\n const height = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\r\n\r\n const x = (ctX - (width / 2))\r\n const y = (ctY - (height / 2))\r\n\r\n const pos = { row, col, anchor }\r\n const { classScore, label } = this.withClassScores\r\n ? await this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\r\n : { classScore: 1, label: 0 }\r\n\r\n results.push({\r\n box: new BoundingBox(x, y, x + width, y + height),\r\n score: score,\r\n classScore: score * classScore,\r\n label,\r\n ...pos\r\n })\r\n }\r\n }\r\n }\r\n }\r\n\r\n boxesTensor.dispose()\r\n scoresTensor.dispose()\r\n classScoresTensor.dispose()\r\n\r\n return results\r\n }\r\n\r\n private async extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number },) {\r\n const { row, col, anchor } = pos\r\n const classesData = await classesTensor.array()\r\n return Array(this.config.classes.length).fill(0)\r\n .map((_, i) => classesData[row][col][anchor][i])\r\n .map((classScore, label) => ({\r\n classScore,\r\n label\r\n }))\r\n .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { FaceDetection, Point } from '../classes';\r\nimport { ParamMapping } from '../common/types';\r\nimport { TNetInput } from '../dom/types';\r\nimport {\r\n BOX_ANCHORS,\r\n BOX_ANCHORS_SEPARABLE,\r\n DEFAULT_MODEL_NAME,\r\n DEFAULT_MODEL_NAME_SEPARABLE_CONV,\r\n IOU_THRESHOLD,\r\n MEAN_RGB_SEPARABLE,\r\n} from './const';\r\nimport { TinyYolov2Base } from './TinyYolov2Base';\r\nimport { ITinyYolov2Options } from './TinyYolov2Options';\r\nimport { TinyYolov2NetParams } from './types';\r\n\r\nexport class TinyYolov2 extends TinyYolov2Base {\r\n\r\n constructor(withSeparableConvs: boolean = true) {\r\n const config = Object.assign({}, {\r\n withSeparableConvs,\r\n iouThreshold: IOU_THRESHOLD,\r\n classes: ['face']\r\n },\r\n withSeparableConvs\r\n ? {\r\n anchors: BOX_ANCHORS_SEPARABLE,\r\n meanRgb: MEAN_RGB_SEPARABLE\r\n }\r\n : {\r\n anchors: BOX_ANCHORS,\r\n withClassScores: true\r\n }\r\n )\r\n\r\n super(config)\r\n }\r\n\r\n public get withSeparableConvs(): boolean {\r\n return this.config.withSeparableConvs\r\n }\r\n\r\n public get anchors(): Point[] {\r\n return this.config.anchors\r\n }\r\n\r\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\r\n const objectDetections = await this.detect(input, forwardParams)\r\n return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n return super.extractParamsFromWeigthMap(weightMap)\r\n }\r\n}","import { TinyYolov2 } from './TinyYolov2';\r\n\r\nexport * from './TinyYolov2Options';\r\n\r\nexport * from './config'\r\nexport * from './types'\r\n\r\nexport { TinyYolov2 }\r\n\r\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\r\n const net = new TinyYolov2(withSeparableConvs)\r\n net.extractWeights(weights)\r\n return net\r\n}","import { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\r\n\r\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\r\n\r\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\r\n protected _name: string = 'TinyFaceDetectorOptions'\r\n}","export class ComposableTask<T> {\r\n\r\n public async then(\r\n onfulfilled: (value: T) => T | PromiseLike<T>\r\n ): Promise<T> {\r\n return onfulfilled(await this.run())\r\n }\r\n\r\n public async run(): Promise<T> {\r\n throw new Error('ComposableTask - run is not implemented')\r\n }\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { isWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\n\r\nexport async function extractAllFacesAndComputeResults<TSource extends WithFaceDetection<{}>, TResult>(\r\n parentResults: TSource[],\r\n input: TNetInput,\r\n computeResults: (faces: Array<HTMLCanvasElement | tf.Tensor3D>) => Promise<TResult>,\r\n extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\r\n getRectForAlignment: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection = ({ alignedRect }) => alignedRect\r\n) {\r\n const faceBoxes = parentResults.map(parentResult =>\r\n isWithFaceLandmarks(parentResult)\r\n ? getRectForAlignment(parentResult)\r\n : parentResult.detection\r\n )\r\n const faces: Array<HTMLCanvasElement | tf.Tensor3D> = extractedFaces || (\r\n input instanceof tf.Tensor\r\n ? await extractFaceTensors(input, faceBoxes)\r\n : await extractFaces(input, faceBoxes)\r\n )\r\n\r\n const results = await computeResults(faces)\r\n\r\n faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n return results\r\n}\r\n\r\nexport async function extractSingleFaceAndComputeResult<TSource extends WithFaceDetection<{}>, TResult>(\r\n parentResult: TSource,\r\n input: TNetInput,\r\n computeResult: (face: HTMLCanvasElement | tf.Tensor3D) => Promise<TResult>,\r\n extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D> | null,\r\n getRectForAlignment?: (parentResult: WithFaceLandmarks<TSource, any>) => FaceDetection\r\n) {\r\n return extractAllFacesAndComputeResults<TSource, TResult>(\r\n [parentResult],\r\n input,\r\n async faces => computeResult(faces[0]),\r\n extractedFaces,\r\n getRectForAlignment\r\n )\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function bgrToRgbTensor(tensor: tf.Tensor4D): tf.Tensor4D {\r\n return tf.tidy(\r\n () => tf.stack(tf.unstack(tensor, 3).reverse(), 3)\r\n ) as tf.Tensor4D\r\n}","export const CELL_STRIDE = 2\r\nexport const CELL_SIZE = 12","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport {\r\n extractConvParamsFactory,\r\n extractFCParamsFactory,\r\n extractWeightsFactory,\r\n ExtractWeightsFunction,\r\n ParamMapping,\r\n} from '../common';\r\nimport { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\r\n\r\n function extractPReluParams(size: number, paramPath: string): tf.Tensor1D {\r\n const alpha = tf.tensor1d(extractWeights(size))\r\n paramMappings.push({ paramPath })\r\n return alpha\r\n }\r\n\r\n function extractSharedParams(numFilters: number[], mappedPrefix: string, isRnet: boolean = false): SharedParams {\r\n\r\n const conv1 = extractConvParams(numFilters[0], numFilters[1], 3, `${mappedPrefix}/conv1`)\r\n const prelu1_alpha = extractPReluParams(numFilters[1], `${mappedPrefix}/prelu1_alpha`)\r\n const conv2 = extractConvParams(numFilters[1], numFilters[2], 3, `${mappedPrefix}/conv2`)\r\n const prelu2_alpha = extractPReluParams(numFilters[2], `${mappedPrefix}/prelu2_alpha`)\r\n const conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, `${mappedPrefix}/conv3`)\r\n const prelu3_alpha = extractPReluParams(numFilters[3], `${mappedPrefix}/prelu3_alpha`)\r\n\r\n return { conv1, prelu1_alpha, conv2, prelu2_alpha, conv3, prelu3_alpha }\r\n }\r\n\r\n function extractPNetParams(): PNetParams {\r\n\r\n const sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet')\r\n const conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1')\r\n const conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2')\r\n\r\n return { ...sharedParams, conv4_1, conv4_2 }\r\n }\r\n\r\n function extractRNetParams(): RNetParams {\r\n\r\n const sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true)\r\n const fc1 = extractFCParams(576, 128, 'rnet/fc1')\r\n const prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha')\r\n const fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1')\r\n const fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2')\r\n\r\n return { ...sharedParams, fc1, prelu4_alpha, fc2_1, fc2_2 }\r\n }\r\n\r\n function extractONetParams(): ONetParams {\r\n\r\n const sharedParams = extractSharedParams([3, 32, 64, 64], 'onet')\r\n const conv4 = extractConvParams(64, 128, 2, 'onet/conv4')\r\n const prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha')\r\n const fc1 = extractFCParams(1152, 256, 'onet/fc1')\r\n const prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha')\r\n const fc2_1 = extractFCParams(256, 2, 'onet/fc2_1')\r\n const fc2_2 = extractFCParams(256, 4, 'onet/fc2_2')\r\n const fc2_3 = extractFCParams(256, 10, 'onet/fc2_3')\r\n\r\n return { ...sharedParams, conv4, prelu4_alpha, fc1, prelu5_alpha, fc2_1, fc2_2, fc2_3 }\r\n }\r\n\r\n return {\r\n extractPNetParams,\r\n extractRNetParams,\r\n extractONetParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const {\r\n extractWeights,\r\n getRemainingWeights\r\n } = extractWeightsFactory(weights)\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractPNetParams,\r\n extractRNetParams,\r\n extractONetParams\r\n } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n const pnet = extractPNetParams()\r\n const rnet = extractRNetParams()\r\n const onet = extractONetParams()\r\n\r\n if (getRemainingWeights().length !== 0) {\r\n throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n }\r\n\r\n return { params: { pnet, rnet, onet }, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams, disposeUnusedWeightTensors, extractWeightEntryFactory, FCParams, ParamMapping } from '../common';\r\nimport { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n function extractConvParams(prefix: string): ConvParams {\r\n const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${prefix}/filters`)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n return { filters, bias }\r\n }\r\n\r\n function extractFCParams(prefix: string): FCParams {\r\n const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\r\n const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n return { weights, bias }\r\n }\r\n\r\n function extractPReluParams(paramPath: string): tf.Tensor1D {\r\n return extractWeightEntry<tf.Tensor1D>(paramPath, 1)\r\n }\r\n\r\n function extractSharedParams(prefix: string): SharedParams {\r\n\r\n const conv1 = extractConvParams(`${prefix}/conv1`)\r\n const prelu1_alpha = extractPReluParams(`${prefix}/prelu1_alpha`)\r\n const conv2 = extractConvParams(`${prefix}/conv2`)\r\n const prelu2_alpha = extractPReluParams(`${prefix}/prelu2_alpha`)\r\n const conv3 = extractConvParams(`${prefix}/conv3`)\r\n const prelu3_alpha = extractPReluParams(`${prefix}/prelu3_alpha`)\r\n\r\n return { conv1, prelu1_alpha, conv2, prelu2_alpha, conv3, prelu3_alpha }\r\n }\r\n\r\n function extractPNetParams(): PNetParams {\r\n\r\n const sharedParams = extractSharedParams('pnet')\r\n const conv4_1 = extractConvParams('pnet/conv4_1')\r\n const conv4_2 = extractConvParams('pnet/conv4_2')\r\n\r\n return { ...sharedParams, conv4_1, conv4_2 }\r\n }\r\n\r\n function extractRNetParams(): RNetParams {\r\n\r\n const sharedParams = extractSharedParams('rnet')\r\n const fc1 = extractFCParams('rnet/fc1')\r\n const prelu4_alpha = extractPReluParams('rnet/prelu4_alpha')\r\n const fc2_1 = extractFCParams('rnet/fc2_1')\r\n const fc2_2 = extractFCParams('rnet/fc2_2')\r\n\r\n return { ...sharedParams, fc1, prelu4_alpha, fc2_1, fc2_2 }\r\n }\r\n\r\n function extractONetParams(): ONetParams {\r\n\r\n const sharedParams = extractSharedParams('onet')\r\n const conv4 = extractConvParams('onet/conv4')\r\n const prelu4_alpha = extractPReluParams('onet/prelu4_alpha')\r\n const fc1 = extractFCParams('onet/fc1')\r\n const prelu5_alpha = extractPReluParams('onet/prelu5_alpha')\r\n const fc2_1 = extractFCParams('onet/fc2_1')\r\n const fc2_2 = extractFCParams('onet/fc2_2')\r\n const fc2_3 = extractFCParams('onet/fc2_3')\r\n\r\n return { ...sharedParams, conv4, prelu4_alpha, fc1, prelu5_alpha, fc2_1, fc2_2, fc2_3 }\r\n }\r\n\r\n return {\r\n extractPNetParams,\r\n extractRNetParams,\r\n extractONetParams\r\n }\r\n\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n const paramMappings: ParamMapping[] = []\r\n\r\n const {\r\n extractPNetParams,\r\n extractRNetParams,\r\n extractONetParams\r\n } = extractorsFactory(weightMap, paramMappings)\r\n\r\n const pnet = extractPNetParams()\r\n const rnet = extractRNetParams()\r\n const onet = extractONetParams()\r\n\r\n disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n return { params: { pnet, rnet, onet }, paramMappings }\r\n}","export function getSizesForScale(scale: number, [height, width]: number[]) {\r\n return {\r\n height: Math.floor(height * scale),\r\n width: Math.floor(width * scale)\r\n }\r\n}","import { CELL_SIZE } from './config'\r\n\r\nexport function pyramidDown(minFaceSize: number, scaleFactor: number, dims: number[]): number[] {\r\n\r\n const [height, width] = dims\r\n const m = CELL_SIZE / minFaceSize\r\n\r\n const scales = []\r\n\r\n let minLayer = Math.min(height, width) * m\r\n let exp = 0\r\n while (minLayer >= 12) {\r\n scales.push(m * Math.pow(scaleFactor, exp))\r\n minLayer = minLayer * scaleFactor\r\n exp += 1\r\n }\r\n\r\n return scales\r\n}","import { Box } from '../classes';\r\n\r\nexport class MtcnnBox extends Box<MtcnnBox> {\r\n constructor(left: number, top: number, right: number, bottom: number) {\r\n super({ left, top, right, bottom }, true)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function normalize(x: tf.Tensor4D): tf.Tensor4D {\r\n return tf.tidy(\r\n () => tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125))\r\n )\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function prelu<T extends tf.Tensor>(x: T, alpha: tf.Tensor1D): T {\r\n return tf.tidy(() =>\r\n tf.add(\r\n tf.relu(x),\r\n tf.mul(alpha, tf.neg(tf.relu(tf.neg(x))))\r\n )\r\n )\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { convLayer } from '../common';\r\nimport { prelu } from './prelu';\r\nimport { SharedParams } from './types';\r\n\r\nexport function sharedLayer(x: tf.Tensor4D, params: SharedParams, isPnet: boolean = false) {\r\n return tf.tidy(() => {\r\n\r\n let out = convLayer(x, params.conv1, 'valid')\r\n out = prelu<tf.Tensor4D>(out, params.prelu1_alpha)\r\n out = tf.maxPool(out, isPnet ? [2, 2]: [3, 3], [2, 2], 'same')\r\n out = convLayer(out, params.conv2, 'valid')\r\n out = prelu<tf.Tensor4D>(out, params.prelu2_alpha)\r\n out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid')\r\n out = convLayer(out, params.conv3, 'valid')\r\n out = prelu<tf.Tensor4D>(out, params.prelu3_alpha)\r\n\r\n return out\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { convLayer } from '../common';\r\nimport { sharedLayer } from './sharedLayers';\r\nimport { PNetParams } from './types';\r\n\r\nexport function PNet(x: tf.Tensor4D, params: PNetParams): { prob: tf.Tensor4D, regions: tf.Tensor4D } {\r\n return tf.tidy(() => {\r\n\r\n let out = sharedLayer(x, params, true)\r\n const conv = convLayer(out, params.conv4_1, 'valid')\r\n const max = tf.expandDims(tf.max(conv, 3), 3)\r\n const prob = tf.softmax(tf.sub(conv, max), 3) as tf.Tensor4D\r\n const regions = convLayer(out, params.conv4_2, 'valid')\r\n\r\n return { prob, regions }\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { BoundingBox, Point } from '../classes';\r\nimport { nonMaxSuppression } from '../ops';\r\nimport { CELL_SIZE, CELL_STRIDE } from './config';\r\nimport { getSizesForScale } from './getSizesForScale';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { normalize } from './normalize';\r\nimport { PNet } from './PNet';\r\nimport { PNetParams } from './types';\r\n\r\nfunction rescaleAndNormalize(x: tf.Tensor4D, scale: number): tf.Tensor4D {\r\n return tf.tidy(() => {\r\n\r\n const { height, width } = getSizesForScale(scale, x.shape.slice(1))\r\n const resized = tf.image.resizeBilinear(x, [height, width])\r\n const normalized = normalize(resized)\r\n\r\n return (tf.transpose(normalized, [0, 2, 1, 3]) as tf.Tensor4D)\r\n })\r\n}\r\n\r\nfunction extractBoundingBoxes(\r\n scoresTensor: tf.Tensor2D,\r\n regionsTensor: tf.Tensor3D,\r\n scale: number,\r\n scoreThreshold: number\r\n) {\r\n\r\n // TODO: fix this!, maybe better to use tf.gather here\r\n const indices: Point[] = []\r\n const scoresData = scoresTensor.arraySync();\r\n for (let y = 0; y < scoresTensor.shape[0]; y++) {\r\n for (let x = 0; x < scoresTensor.shape[1]; x++) {\r\n if (scoresData[y][x] >= scoreThreshold) {\r\n indices.push(new Point(x, y))\r\n }\r\n }\r\n }\r\n\r\n const boundingBoxes = indices.map(idx => {\r\n const cell = new BoundingBox(\r\n Math.round((idx.y * CELL_STRIDE + 1) / scale),\r\n Math.round((idx.x * CELL_STRIDE + 1) / scale),\r\n Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale),\r\n Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)\r\n )\r\n\r\n const score = scoresData[idx.y][idx.x]\r\n\r\n const regionsData = regionsTensor.arraySync()\r\n const region = new MtcnnBox(\r\n regionsData[idx.y][idx.x][0],\r\n regionsData[idx.y][idx.x][1],\r\n regionsData[idx.y][idx.x][2],\r\n regionsData[idx.y][idx.x][3]\r\n )\r\n\r\n return {\r\n cell,\r\n score,\r\n region\r\n }\r\n })\r\n\r\n return boundingBoxes\r\n}\r\n\r\nexport function stage1(\r\n imgTensor: tf.Tensor4D,\r\n scales: number[],\r\n scoreThreshold: number,\r\n params: PNetParams,\r\n stats: any\r\n) {\r\n stats.stage1 = []\r\n\r\n const pnetOutputs = scales.map((scale) => tf.tidy(() => {\r\n const statsForScale: any = { scale }\r\n const resized = rescaleAndNormalize(imgTensor, scale)\r\n\r\n let ts = Date.now()\r\n const { prob, regions } = PNet(resized, params)\r\n statsForScale.pnet = Date.now() - ts\r\n\r\n const scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0] as tf.Tensor2D\r\n const regionsTensor = tf.unstack(regions)[0] as tf.Tensor3D\r\n\r\n return {\r\n scoresTensor,\r\n regionsTensor,\r\n scale,\r\n statsForScale\r\n }\r\n }))\r\n\r\n const boxesForScale = pnetOutputs.map(({ scoresTensor, regionsTensor, scale, statsForScale }) => {\r\n const boundingBoxes = extractBoundingBoxes(\r\n scoresTensor,\r\n regionsTensor,\r\n scale,\r\n scoreThreshold\r\n )\r\n\r\n scoresTensor.dispose()\r\n regionsTensor.dispose()\r\n\r\n if (!boundingBoxes.length) {\r\n stats.stage1.push(statsForScale)\r\n return []\r\n }\r\n\r\n let ts = Date.now()\r\n const indices = nonMaxSuppression(\r\n boundingBoxes.map(bbox => bbox.cell),\r\n boundingBoxes.map(bbox => bbox.score),\r\n 0.5\r\n )\r\n statsForScale.nms = Date.now() - ts\r\n statsForScale.numBoxes = indices.length\r\n\r\n stats.stage1.push(statsForScale)\r\n return indices.map(boxIdx => boundingBoxes[boxIdx])\r\n })\r\n\r\n const allBoxes = boxesForScale.reduce(\r\n (all, boxes) => all.concat(boxes), []\r\n )\r\n\r\n let finalBoxes: BoundingBox[] = []\r\n let finalScores: number[] = []\r\n\r\n if (allBoxes.length > 0) {\r\n let ts = Date.now()\r\n const indices = nonMaxSuppression(\r\n allBoxes.map(bbox => bbox.cell),\r\n allBoxes.map(bbox => bbox.score),\r\n 0.7\r\n )\r\n stats.stage1_nms = Date.now() - ts\r\n\r\n finalScores = indices.map(idx => allBoxes[idx].score)\r\n finalBoxes = indices\r\n .map(idx => allBoxes[idx])\r\n .map(({ cell, region }) =>\r\n new BoundingBox(\r\n cell.left + (region.left * cell.width),\r\n cell.top + (region.top * cell.height),\r\n cell.right + (region.right * cell.width),\r\n cell.bottom + (region.bottom * cell.height)\r\n ).toSquare().round()\r\n )\r\n\r\n }\r\n\r\n return {\r\n boxes: finalBoxes,\r\n scores: finalScores\r\n }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Box, IDimensions } from '../classes';\r\nimport { createCanvas, createCanvasFromMedia, getContext2dOrThrow } from '../dom';\r\nimport { env } from '../env';\r\nimport { normalize } from './normalize';\r\n\r\nexport async function extractImagePatches(\r\n img: HTMLCanvasElement,\r\n boxes: Box[],\r\n { width, height }: IDimensions\r\n): Promise<tf.Tensor4D[]> {\r\n\r\n\r\n const imgCtx = getContext2dOrThrow(img)\r\n\r\n const bitmaps = await Promise.all(boxes.map(async box => {\r\n // TODO: correct padding\r\n const { y, ey, x, ex } = box.padAtBorders(img.height, img.width)\r\n\r\n const fromX = x - 1\r\n const fromY = y - 1\r\n const imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY))\r\n\r\n return env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)\r\n }))\r\n\r\n const imagePatchesDatas: number[][] = []\r\n\r\n bitmaps.forEach(bmp => {\r\n const patch = createCanvas({ width, height })\r\n const patchCtx = getContext2dOrThrow(patch)\r\n patchCtx.drawImage(bmp, 0, 0, width, height)\r\n const { data } = patchCtx.getImageData(0, 0, width, height)\r\n\r\n const currData = []\r\n // RGBA -> BGR\r\n for(let i = 0; i < data.length; i+=4) {\r\n currData.push(data[i + 2])\r\n currData.push(data[i + 1])\r\n currData.push(data[i])\r\n }\r\n imagePatchesDatas.push(currData)\r\n })\r\n\r\n\r\n return imagePatchesDatas.map(data => {\r\n const t = tf.tidy(() => {\r\n const imagePatchTensor = tf.transpose(\r\n tf.tensor4d(data, [1, width, height, 3]),\r\n [0, 2, 1, 3]\r\n ).toFloat() as tf.Tensor4D\r\n\r\n return normalize(imagePatchTensor)\r\n })\r\n return t\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { prelu } from './prelu';\r\nimport { sharedLayer } from './sharedLayers';\r\nimport { RNetParams } from './types';\r\n\r\nexport function RNet(x: tf.Tensor4D, params: RNetParams): { scores: tf.Tensor1D, regions: tf.Tensor2D } {\r\n return tf.tidy(() => {\r\n\r\n const convOut = sharedLayer(x, params)\r\n const vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]) as tf.Tensor2D\r\n const fc1 = fullyConnectedLayer(vectorized, params.fc1)\r\n const prelu4 = prelu<tf.Tensor2D>(fc1, params.prelu4_alpha)\r\n const fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1)\r\n const max = tf.expandDims(tf.max(fc2_1, 1), 1)\r\n const prob = tf.softmax(tf.sub(fc2_1, max), 1) as tf.Tensor2D\r\n const regions = fullyConnectedLayer(prelu4, params.fc2_2)\r\n\r\n const scores = tf.unstack(prob, 1)[1] as tf.Tensor1D\r\n return { scores, regions }\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Box } from '../classes';\r\nimport { nonMaxSuppression } from '../ops';\r\nimport { extractImagePatches } from './extractImagePatches';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { RNet } from './RNet';\r\nimport { RNetParams } from './types';\r\n\r\nexport async function stage2(\r\n img: HTMLCanvasElement,\r\n inputBoxes: Box[],\r\n scoreThreshold: number,\r\n params: RNetParams,\r\n stats: any\r\n) {\r\n\r\n let ts = Date.now()\r\n const rnetInputs = await extractImagePatches(img, inputBoxes, { width: 24, height: 24 })\r\n stats.stage2_extractImagePatches = Date.now() - ts\r\n\r\n ts = Date.now()\r\n const rnetOuts = rnetInputs.map(\r\n rnetInput => {\r\n const out = RNet(rnetInput, params)\r\n rnetInput.dispose()\r\n return out\r\n }\r\n )\r\n stats.stage2_rnet = Date.now() - ts\r\n\r\n const scoresTensor = rnetOuts.length > 1\r\n ? tf.concat(rnetOuts.map(out => out.scores))\r\n : rnetOuts[0].scores\r\n const scores = Array.from(await scoresTensor.data())\r\n scoresTensor.dispose()\r\n\r\n const indices = scores\r\n .map((score, idx) => ({ score, idx }))\r\n .filter(c => c.score > scoreThreshold)\r\n .map(({ idx }) => idx)\r\n\r\n const filteredBoxes = indices.map(idx => inputBoxes[idx])\r\n const filteredScores = indices.map(idx => scores[idx])\r\n\r\n let finalBoxes: Box[] = []\r\n let finalScores: number[] = []\r\n\r\n if (filteredBoxes.length > 0) {\r\n ts = Date.now()\r\n const indicesNms = nonMaxSuppression(\r\n filteredBoxes,\r\n filteredScores,\r\n 0.7\r\n )\r\n stats.stage2_nms = Date.now() - ts\r\n\r\n const regions = indicesNms.map(idx =>{\r\n const regionsData = rnetOuts[indices[idx]].regions.arraySync()\r\n return new MtcnnBox(\r\n regionsData[0][0],\r\n regionsData[0][1],\r\n regionsData[0][2],\r\n regionsData[0][3]\r\n )\r\n }\r\n )\r\n\r\n finalScores = indicesNms.map(idx => filteredScores[idx])\r\n finalBoxes = indicesNms.map((idx, i) => filteredBoxes[idx].calibrate(regions[i]))\r\n }\r\n\r\n rnetOuts.forEach(t => {\r\n t.regions.dispose()\r\n t.scores.dispose()\r\n })\r\n\r\n return {\r\n boxes: finalBoxes,\r\n scores: finalScores\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { convLayer } from '../common';\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { prelu } from './prelu';\r\nimport { sharedLayer } from './sharedLayers';\r\nimport { ONetParams } from './types';\r\n\r\nexport function ONet(x: tf.Tensor4D, params: ONetParams): { scores: tf.Tensor1D, regions: tf.Tensor2D, points: tf.Tensor2D } {\r\n return tf.tidy(() => {\r\n\r\n let out = sharedLayer(x, params)\r\n out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n out = convLayer(out, params.conv4, 'valid')\r\n out = prelu<tf.Tensor4D>(out, params.prelu4_alpha)\r\n\r\n const vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]) as tf.Tensor2D\r\n const fc1 = fullyConnectedLayer(vectorized, params.fc1)\r\n const prelu5 = prelu<tf.Tensor2D>(fc1, params.prelu5_alpha)\r\n const fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1)\r\n const max = tf.expandDims(tf.max(fc2_1, 1), 1)\r\n\r\n const prob = tf.softmax(tf.sub(fc2_1, max), 1) as tf.Tensor2D\r\n const regions = fullyConnectedLayer(prelu5, params.fc2_2)\r\n const points = fullyConnectedLayer(prelu5, params.fc2_3)\r\n\r\n const scores = tf.unstack(prob, 1)[1] as tf.Tensor1D\r\n return { scores, regions, points }\r\n })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { BoundingBox, Box, Point } from '../classes';\r\nimport { nonMaxSuppression } from '../ops';\r\nimport { extractImagePatches } from './extractImagePatches';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { ONet } from './ONet';\r\nimport { ONetParams } from './types';\r\n\r\nexport async function stage3(\r\n img: HTMLCanvasElement,\r\n inputBoxes: BoundingBox[],\r\n scoreThreshold: number,\r\n params: ONetParams,\r\n stats: any\r\n) {\r\n\r\n let ts = Date.now()\r\n const onetInputs = await extractImagePatches(img, inputBoxes, { width: 48, height: 48 })\r\n stats.stage3_extractImagePatches = Date.now() - ts\r\n\r\n ts = Date.now()\r\n const onetOuts = onetInputs.map(\r\n onetInput => {\r\n const out = ONet(onetInput, params)\r\n onetInput.dispose()\r\n return out\r\n }\r\n )\r\n stats.stage3_onet = Date.now() - ts\r\n\r\n const scoresTensor = onetOuts.length > 1\r\n ? tf.concat(onetOuts.map(out => out.scores))\r\n : onetOuts[0].scores\r\n const scores = Array.from(await scoresTensor.data())\r\n scoresTensor.dispose()\r\n\r\n const indices = scores\r\n .map((score, idx) => ({ score, idx }))\r\n .filter(c => c.score > scoreThreshold)\r\n .map(({ idx }) => idx)\r\n\r\n const filteredRegions = indices.map(idx => {\r\n const regionsData = onetOuts[idx].regions.arraySync();\r\n return new MtcnnBox(\r\n regionsData[0][0],\r\n regionsData[0][1],\r\n regionsData[0][2],\r\n regionsData[0][3]\r\n )})\r\n const filteredBoxes = indices\r\n .map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))\r\n const filteredScores = indices.map(idx => scores[idx])\r\n\r\n let finalBoxes: Box[] = []\r\n let finalScores: number[] = []\r\n let points: Point[][] = []\r\n\r\n if (filteredBoxes.length > 0) {\r\n\r\n ts = Date.now()\r\n const indicesNms = nonMaxSuppression(\r\n filteredBoxes,\r\n filteredScores,\r\n 0.7,\r\n false\r\n )\r\n stats.stage3_nms = Date.now() - ts\r\n\r\n finalBoxes = indicesNms.map(idx => filteredBoxes[idx])\r\n finalScores = indicesNms.map(idx => filteredScores[idx])\r\n points = indicesNms.map((idx, i) =>\r\n Array(5).fill(0).map((_, ptIdx) =>{\r\n const pointsData = onetOuts[idx].points.arraySync()\r\n return new Point(\r\n ((pointsData[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,\r\n ((pointsData[0][ptIdx+5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top)\r\n )\r\n }\r\n )\r\n )\r\n }\r\n\r\n onetOuts.forEach(t => {\r\n t.regions.dispose()\r\n t.scores.dispose()\r\n t.points.dispose()\r\n })\r\n\r\n return {\r\n boxes: finalBoxes,\r\n scores: finalScores,\r\n points\r\n }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { Point, Rect } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\r\nimport { NetInput, TNetInput, toNetInput } from '../dom';\r\nimport { extendWithFaceDetection, extendWithFaceLandmarks } from '../factories';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { bgrToRgbTensor } from './bgrToRgbTensor';\r\nimport { CELL_SIZE } from './config';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { getSizesForScale } from './getSizesForScale';\r\nimport { IMtcnnOptions, MtcnnOptions } from './MtcnnOptions';\r\nimport { pyramidDown } from './pyramidDown';\r\nimport { stage1 } from './stage1';\r\nimport { stage2 } from './stage2';\r\nimport { stage3 } from './stage3';\r\nimport { MtcnnResult, NetParams } from './types';\r\n\r\nexport class Mtcnn extends NeuralNetwork<NetParams> {\r\n\r\n constructor() {\r\n super('Mtcnn')\r\n }\r\n\r\n public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\r\n console.warn('mtcnn is deprecated and will be removed soon')\r\n return super.load(weightsOrUrl)\r\n }\r\n\r\n public async loadFromDisk(filePath: string | undefined) {\r\n console.warn('mtcnn is deprecated and will be removed soon')\r\n return super.loadFromDisk(filePath)\r\n }\r\n\r\n public async forwardInput(\r\n input: NetInput,\r\n forwardParams: IMtcnnOptions = {}\r\n ): Promise<{ results: MtcnnResult[], stats: any }> {\r\n\r\n const { params } = this\r\n\r\n if (!params) {\r\n throw new Error('Mtcnn - load model before inference')\r\n }\r\n\r\n const inputCanvas = input.canvases[0]\r\n\r\n if (!inputCanvas) {\r\n throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.')\r\n }\r\n\r\n const stats: any = {}\r\n\r\n const tsTotal = Date.now()\r\n\r\n const imgTensor = tf.tidy(() =>\r\n bgrToRgbTensor(\r\n tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D\r\n )\r\n )\r\n\r\n const onReturn = (results: any) => {\r\n // dispose tensors on return\r\n imgTensor.dispose()\r\n stats.total = Date.now() - tsTotal\r\n return results\r\n }\r\n\r\n const [height, width] = imgTensor.shape.slice(1)\r\n\r\n const {\r\n minFaceSize,\r\n scaleFactor,\r\n maxNumScales,\r\n scoreThresholds,\r\n scaleSteps\r\n } = new MtcnnOptions(forwardParams)\r\n\r\n const scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))\r\n .filter(scale => {\r\n const sizes = getSizesForScale(scale, [height, width])\r\n return Math.min(sizes.width, sizes.height) > CELL_SIZE\r\n })\r\n .slice(0, maxNumScales)\r\n\r\n stats.scales = scales\r\n stats.pyramid = scales.map(scale => getSizesForScale(scale, [height, width]))\r\n\r\n let ts = Date.now()\r\n const out1 = await stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)\r\n stats.total_stage1 = Date.now() - ts\r\n\r\n if (!out1.boxes.length) {\r\n return onReturn({ results: [], stats })\r\n }\r\n\r\n stats.stage2_numInputBoxes = out1.boxes.length\r\n // using the inputCanvas to extract and resize the image patches, since it is faster\r\n // than doing this on the gpu\r\n ts = Date.now()\r\n const out2 = await stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)\r\n stats.total_stage2 = Date.now() - ts\r\n\r\n if (!out2.boxes.length) {\r\n return onReturn({ results: [], stats })\r\n }\r\n\r\n stats.stage3_numInputBoxes = out2.boxes.length\r\n\r\n ts = Date.now()\r\n const out3 = await stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)\r\n stats.total_stage3 = Date.now() - ts\r\n\r\n const results = out3.boxes.map((box, idx) => extendWithFaceLandmarks(\r\n extendWithFaceDetection<{}>(\r\n {},\r\n new FaceDetection(\r\n out3.scores[idx],\r\n new Rect(\r\n box.left / width,\r\n box.top / height,\r\n box.width / width,\r\n box.height / height\r\n ),\r\n {\r\n height,\r\n width\r\n }\r\n )\r\n ),\r\n new FaceLandmarks5(\r\n out3.points[idx].map(pt => pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height))),\r\n { width: box.width, height: box.height }\r\n )\r\n ))\r\n\r\n return onReturn({ results, stats })\r\n }\r\n\r\n public async forward(\r\n input: TNetInput,\r\n forwardParams: IMtcnnOptions = {}\r\n ): Promise<MtcnnResult[]> {\r\n return (\r\n await this.forwardInput(\r\n await toNetInput(input),\r\n forwardParams\r\n )\r\n ).results\r\n }\r\n\r\n public async forwardWithStats(\r\n input: TNetInput,\r\n forwardParams: IMtcnnOptions = {}\r\n ): Promise<{ results: MtcnnResult[], stats: any }> {\r\n return this.forwardInput(\r\n await toNetInput(input),\r\n forwardParams\r\n )\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'mtcnn_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n return extractParamsFromWeigthMap(weightMap)\r\n }\r\n\r\n protected extractParams(weights: Float32Array) {\r\n return extractParams(weights)\r\n }\r\n}","import { Point } from '../classes'\r\n\r\nexport const IOU_THRESHOLD = 0.4\r\n\r\nexport const BOX_ANCHORS = [\r\n new Point(1.603231, 2.094468),\r\n new Point(6.041143, 7.080126),\r\n new Point(2.882459, 3.518061),\r\n new Point(4.266906, 5.178857),\r\n new Point(9.041765, 10.66308)\r\n]\r\n\r\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { FaceDetection, Point } from '../classes';\r\nimport { ParamMapping } from '../common';\r\nimport { TNetInput } from '../dom';\r\nimport { ITinyYolov2Options } from '../tinyYolov2';\r\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\r\nimport { TinyYolov2NetParams } from '../tinyYolov2/types';\r\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\r\n\r\nexport class TinyFaceDetector extends TinyYolov2Base {\r\n\r\n constructor() {\r\n const config = {\r\n withSeparableConvs: true,\r\n iouThreshold: IOU_THRESHOLD,\r\n classes: ['face'],\r\n anchors: BOX_ANCHORS,\r\n meanRgb: MEAN_RGB,\r\n isFirstLayerConv2d: true,\r\n filterSizes: [3, 16, 32, 64, 128, 256, 512]\r\n }\r\n\r\n super(config)\r\n }\r\n\r\n public get anchors(): Point[] {\r\n return this.config.anchors\r\n }\r\n\r\n public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\r\n const objectDetections = await this.detect(input, forwardParams)\r\n return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\r\n }\r\n\r\n protected getDefaultModelName(): string {\r\n return 'tiny_face_detector_model'\r\n }\r\n\r\n protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n return super.extractParamsFromWeigthMap(weightMap)\r\n }\r\n}","import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\r\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { TNetInput } from '../dom';\r\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\r\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\r\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\r\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\r\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { Mtcnn } from '../mtcnn/Mtcnn';\r\nimport { MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\r\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\r\nimport { ITinyYolov2Options, TinyYolov2 } from '../tinyYolov2';\r\n\r\nexport const nets = {\r\n ssdMobilenetv1: new SsdMobilenetv1(),\r\n tinyFaceDetector: new TinyFaceDetector(),\r\n tinyYolov2: new TinyYolov2(),\r\n mtcnn: new Mtcnn(),\r\n faceLandmark68Net: new FaceLandmark68Net(),\r\n faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\r\n faceRecognitionNet: new FaceRecognitionNet(),\r\n faceExpressionNet: new FaceExpressionNet(),\r\n ageGenderNet: new AgeGenderNet()\r\n}\r\n\r\n/**\r\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\r\n nets.ssdMobilenetv1.locateFaces(input, options)\r\n\r\n/**\r\n * Attempts to detect all faces in an image using the Tiny Face Detector.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\r\n nets.tinyFaceDetector.locateFaces(input, options)\r\n\r\n/**\r\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\r\n nets.tinyYolov2.locateFaces(input, options)\r\n\r\n/**\r\n * Attempts to detect all faces in an image and the 5 point face landmarks\r\n * of each detected face using the MTCNN Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see MtcnnOptions constructor for default parameters).\r\n * @returns Bounding box of each face with score and 5 point face landmarks.\r\n */\r\nexport const mtcnn = (input: TNetInput, options: MtcnnOptions): Promise<WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks5>[]> =>\r\n nets.mtcnn.forward(input, options)\r\n\r\n/**\r\n * Detects the 68 point face landmark positions of the face shown in an image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns 68 point face landmarks or array thereof in case of batch input.\r\n */\r\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\r\n nets.faceLandmark68Net.detectLandmarks(input)\r\n\r\n/**\r\n * Detects the 68 point face landmark positions of the face shown in an image\r\n * using a tinier version of the 68 point face landmark model, which is slightly\r\n * faster at inference, but also slightly less accurate.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns 68 point face landmarks or array thereof in case of batch input.\r\n */\r\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\r\n nets.faceLandmark68TinyNet.detectLandmarks(input)\r\n\r\n/**\r\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\r\n * which uniquely represents the features of that persons face. The computed face descriptor can\r\n * be used to measure the similarity between faces, by computing the euclidean distance of two\r\n * face descriptors.\r\n *\r\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\r\n */\r\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]> =>\r\n nets.faceRecognitionNet.computeFaceDescriptor(input)\r\n\r\n\r\n/**\r\n * Recognizes the facial expressions from a face image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\r\n */\r\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressions | FaceExpressions[]> =>\r\n nets.faceExpressionNet.predictExpressions(input)\r\n\r\n/**\r\n * Predicts age and gender from a face image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\r\n */\r\nexport const predictAgeAndGender = (input: TNetInput): Promise<AgeAndGenderPrediction | AgeAndGenderPrediction[]> =>\r\n nets.ageGenderNet.predictAgeAndGender(input)\r\n\r\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\r\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\r\nexport const loadMtcnnModel = (url: string) => nets.mtcnn.load(url)\r\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\r\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\r\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\r\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\r\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\r\nexport const loadAgeGenderModel = (url: string) => nets.ageGenderNet.load(url)\r\n\r\n// backward compatibility\r\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\r\nexport const locateFaces = ssdMobilenetv1\r\nexport const detectLandmarks = detectFaceLandmarks","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { TNetInput } from '../dom';\r\nimport { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\r\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\r\nimport { nets } from './nets';\r\nimport {\r\n PredictAllAgeAndGenderTask,\r\n PredictAllAgeAndGenderWithFaceAlignmentTask,\r\n PredictSingleAgeAndGenderTask,\r\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\r\n} from './PredictAgeAndGenderTask';\r\n\r\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n constructor(\r\n protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n protected input: TNetInput,\r\n protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\r\n ) {\r\n super()\r\n }\r\n}\r\n\r\nexport class PredictAllFaceExpressionsTask<\r\n TSource extends WithFaceDetection<{}>\r\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\r\n\r\n public async run(): Promise<WithFaceExpressions<TSource>[]> {\r\n\r\n const parentResults = await this.parentTask\r\n\r\n const faceExpressionsByFace = await extractAllFacesAndComputeResults<TSource, FaceExpressions[]>(\r\n parentResults,\r\n this.input,\r\n async faces => await Promise.all(faces.map(\r\n face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>\r\n )),\r\n this.extractedFaces\r\n )\r\n\r\n return parentResults.map(\r\n (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\r\n )\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictAllAgeAndGenderTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class PredictSingleFaceExpressionsTask<\r\n TSource extends WithFaceDetection<{}>\r\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\r\n\r\n public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\r\n\r\n const parentResult = await this.parentTask\r\n if (!parentResult) {\r\n return\r\n }\r\n\r\n const faceExpressions = await extractSingleFaceAndComputeResult<TSource, FaceExpressions>(\r\n parentResult,\r\n this.input,\r\n face => nets.faceExpressionNet.predictExpressions(face) as Promise<FaceExpressions>,\r\n this.extractedFaces\r\n )\r\n\r\n return extendWithFaceExpressions(parentResult, faceExpressions)\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictSingleAgeAndGenderTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class PredictAllFaceExpressionsWithFaceAlignmentTask<\r\n TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends PredictAllFaceExpressionsTask<TSource> {\r\n\r\n withAgeAndGender() {\r\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withFaceDescriptors() {\r\n return new ComputeAllFaceDescriptorsTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class PredictSingleFaceExpressionsWithFaceAlignmentTask<\r\n TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends PredictSingleFaceExpressionsTask<TSource> {\r\n\r\n withAgeAndGender() {\r\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withFaceDescriptor() {\r\n return new ComputeSingleFaceDescriptorTask(this, this.input)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { AgeAndGenderPrediction } from '../ageGenderNet/types';\r\nimport { TNetInput } from '../dom';\r\nimport { extendWithAge, WithAge } from '../factories/WithAge';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { extendWithGender, WithGender } from '../factories/WithGender';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\r\nimport { nets } from './nets';\r\nimport {\r\n PredictAllFaceExpressionsTask,\r\n PredictAllFaceExpressionsWithFaceAlignmentTask,\r\n PredictSingleFaceExpressionsTask,\r\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\r\n} from './PredictFaceExpressionsTask';\r\n\r\nexport class PredictAgeAndGenderTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n constructor(\r\n protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n protected input: TNetInput,\r\n protected extractedFaces?: Array<HTMLCanvasElement | tf.Tensor3D>\r\n ) {\r\n super()\r\n }\r\n}\r\n\r\nexport class PredictAllAgeAndGenderTask<\r\n TSource extends WithFaceDetection<{}>\r\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>>[], TSource[]> {\r\n\r\n public async run(): Promise<WithAge<WithGender<TSource>>[]> {\r\n\r\n const parentResults = await this.parentTask\r\n\r\n const ageAndGenderByFace = await extractAllFacesAndComputeResults<TSource, AgeAndGenderPrediction[]>(\r\n parentResults,\r\n this.input,\r\n async faces => await Promise.all(faces.map(\r\n face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>\r\n )),\r\n this.extractedFaces\r\n )\r\n\r\n return parentResults.map((parentResult, i) => {\r\n const { age, gender, genderProbability } = ageAndGenderByFace[i]\r\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\r\n })\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictAllFaceExpressionsTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class PredictSingleAgeAndGenderTask<\r\n TSource extends WithFaceDetection<{}>\r\n> extends PredictAgeAndGenderTaskBase<WithAge<WithGender<TSource>> | undefined, TSource | undefined> {\r\n\r\n public async run(): Promise<WithAge<WithGender<TSource>> | undefined> {\r\n\r\n const parentResult = await this.parentTask\r\n if (!parentResult) {\r\n return\r\n }\r\n\r\n const { age, gender, genderProbability } = await extractSingleFaceAndComputeResult<TSource, AgeAndGenderPrediction>(\r\n parentResult,\r\n this.input,\r\n face => nets.ageGenderNet.predictAgeAndGender(face) as Promise<AgeAndGenderPrediction>,\r\n this.extractedFaces\r\n )\r\n\r\n return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictSingleFaceExpressionsTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class PredictAllAgeAndGenderWithFaceAlignmentTask<\r\n TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends PredictAllAgeAndGenderTask<TSource> {\r\n\r\n withFaceExpressions() {\r\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withFaceDescriptors() {\r\n return new ComputeAllFaceDescriptorsTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class PredictSingleAgeAndGenderWithFaceAlignmentTask<\r\n TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends PredictSingleAgeAndGenderTask<TSource> {\r\n\r\n withFaceExpressions() {\r\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withFaceDescriptor() {\r\n return new ComputeSingleFaceDescriptorTask(this, this.input)\r\n }\r\n}","import { TNetInput } from '../dom';\r\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\r\nimport { nets } from './nets';\r\nimport {\r\n PredictAllAgeAndGenderWithFaceAlignmentTask,\r\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\r\n} from './PredictAgeAndGenderTask';\r\nimport {\r\n PredictAllFaceExpressionsWithFaceAlignmentTask,\r\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\r\n} from './PredictFaceExpressionsTask';\r\n\r\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n constructor(\r\n protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n protected input: TNetInput\r\n ) {\r\n super()\r\n }\r\n}\r\n\r\nexport class ComputeAllFaceDescriptorsTask<\r\n TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\r\n\r\n public async run(): Promise<WithFaceDescriptor<TSource>[]> {\r\n\r\n const parentResults = await this.parentTask\r\n\r\n const descriptors = await extractAllFacesAndComputeResults<TSource, Float32Array[]>(\r\n parentResults,\r\n this.input,\r\n faces => Promise.all(faces.map(face =>\r\n nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>\r\n )),\r\n null,\r\n parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\r\n )\r\n\r\n return descriptors.map((descriptor, i) => extendWithFaceDescriptor<TSource>(parentResults[i], descriptor))\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class ComputeSingleFaceDescriptorTask<\r\n TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\r\n\r\n public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\r\n\r\n const parentResult = await this.parentTask\r\n if (!parentResult) {\r\n return\r\n }\r\n const descriptor = await extractSingleFaceAndComputeResult<TSource, Float32Array>(\r\n parentResult,\r\n this.input,\r\n face => nets.faceRecognitionNet.computeFaceDescriptor(face) as Promise<Float32Array>,\r\n null,\r\n parentResult => parentResult.landmarks.align(null, { useDlibAlignment: true })\r\n )\r\n\r\n return extendWithFaceDescriptor(parentResult, descriptor)\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\r\n }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { extractFaces, extractFaceTensors, TNetInput } from '../dom';\r\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\r\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { nets } from './nets';\r\nimport {\r\n PredictAllAgeAndGenderWithFaceAlignmentTask,\r\n PredictSingleAgeAndGenderWithFaceAlignmentTask,\r\n} from './PredictAgeAndGenderTask';\r\nimport {\r\n PredictAllFaceExpressionsWithFaceAlignmentTask,\r\n PredictSingleFaceExpressionsWithFaceAlignmentTask,\r\n} from './PredictFaceExpressionsTask';\r\n\r\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n constructor(\r\n protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n protected input: TNetInput,\r\n protected useTinyLandmarkNet: boolean\r\n ) {\r\n super()\r\n }\r\n\r\n protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\r\n return this.useTinyLandmarkNet\r\n ? nets.faceLandmark68TinyNet\r\n : nets.faceLandmark68Net\r\n }\r\n}\r\n\r\nexport class DetectAllFaceLandmarksTask<\r\n TSource extends WithFaceDetection<{}>\r\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\r\n\r\n public async run(): Promise<WithFaceLandmarks<TSource>[]> {\r\n\r\n const parentResults = await this.parentTask\r\n const detections = parentResults.map(res => res.detection)\r\n\r\n const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n ? await extractFaceTensors(this.input, detections)\r\n : await extractFaces(this.input, detections)\r\n\r\n const faceLandmarksByFace = await Promise.all(faces.map(\r\n face => this.landmarkNet.detectLandmarks(face)\r\n )) as FaceLandmarks68[]\r\n\r\n faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n return parentResults.map((parentResult, i) =>\r\n extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\r\n )\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withFaceDescriptors() {\r\n return new ComputeAllFaceDescriptorsTask(this, this.input)\r\n }\r\n}\r\n\r\nexport class DetectSingleFaceLandmarksTask<\r\n TSource extends WithFaceDetection<{}>\r\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\r\n\r\n public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\r\n\r\n const parentResult = await this.parentTask\r\n if (!parentResult) {\r\n return\r\n }\r\n\r\n const { detection } = parentResult\r\n const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n ? await extractFaceTensors(this.input, [detection])\r\n : await extractFaces(this.input, [detection])\r\n\r\n const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\r\n\r\n faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input)\r\n }\r\n\r\n withFaceDescriptor() {\r\n return new ComputeSingleFaceDescriptorTask(this, this.input)\r\n }\r\n}","import { FaceDetection } from '../classes/FaceDetection';\r\nimport { TNetInput } from '../dom';\r\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\r\nimport { TinyYolov2Options } from '../tinyYolov2';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\r\nimport { nets } from './nets';\r\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\r\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\r\nimport { FaceDetectionOptions } from './types';\r\n\r\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\r\n constructor(\r\n protected input: TNetInput,\r\n protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\r\n ) {\r\n super()\r\n }\r\n}\r\n\r\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\r\n\r\n public async run(): Promise<FaceDetection[]> {\r\n\r\n const { input, options } = this\r\n\r\n if (options instanceof MtcnnOptions) {\r\n return (await nets.mtcnn.forward(input, options))\r\n .map(result => result.detection)\r\n }\r\n\r\n const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\r\n ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\r\n : (\r\n options instanceof SsdMobilenetv1Options\r\n ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\r\n : (\r\n options instanceof TinyYolov2Options\r\n ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\r\n : null\r\n )\r\n )\r\n\r\n if (!faceDetectionFunction) {\r\n throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\r\n }\r\n\r\n return faceDetectionFunction(input)\r\n }\r\n\r\n private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\r\n return new Promise<WithFaceDetection<{}>[]>(async res => {\r\n const detections = await this.run()\r\n return res(detections.map(detection => extendWithFaceDetection({}, detection)))\r\n })\r\n }\r\n\r\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\r\n return new DetectAllFaceLandmarksTask(\r\n this.runAndExtendWithFaceDetections(),\r\n this.input,\r\n useTinyLandmarkNet\r\n )\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictAllFaceExpressionsTask (\r\n this.runAndExtendWithFaceDetections(),\r\n this.input\r\n )\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictAllAgeAndGenderTask(\r\n this.runAndExtendWithFaceDetections(),\r\n this.input\r\n )\r\n }\r\n}\r\n\r\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\r\n\r\n public async run(): Promise<FaceDetection | undefined> {\r\n const faceDetections = await new DetectAllFacesTask(this.input, this.options);\r\n let faceDetectionWithHighestScore = faceDetections[0];\r\n faceDetections.forEach(faceDetection => {\r\n if (faceDetection.score > faceDetectionWithHighestScore.score) {\r\n faceDetectionWithHighestScore = faceDetection;\r\n }\r\n });\r\n return faceDetectionWithHighestScore;\r\n }\r\n\r\n private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}>> {\r\n return new Promise<WithFaceDetection<{}>>(async res => {\r\n const detection = await this.run()\r\n return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\r\n })\r\n }\r\n\r\n withFaceLandmarks(useTinyLandmarkNet: boolean = false) {\r\n return new DetectSingleFaceLandmarksTask(\r\n this.runAndExtendWithFaceDetection(),\r\n this.input,\r\n useTinyLandmarkNet\r\n )\r\n }\r\n\r\n withFaceExpressions() {\r\n return new PredictSingleFaceExpressionsTask(\r\n this.runAndExtendWithFaceDetection(),\r\n this.input\r\n )\r\n }\r\n\r\n withAgeAndGender() {\r\n return new PredictSingleAgeAndGenderTask(\r\n this.runAndExtendWithFaceDetection(),\r\n this.input\r\n )\r\n }\r\n}","import { TNetInput } from '../dom';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\r\nimport { FaceDetectionOptions } from './types';\r\n\r\nexport function detectSingleFace(\r\n input: TNetInput,\r\n options: FaceDetectionOptions = new SsdMobilenetv1Options()\r\n): DetectSingleFaceTask {\r\n return new DetectSingleFaceTask(input, options)\r\n}\r\n\r\nexport function detectAllFaces(\r\n input: TNetInput,\r\n options: FaceDetectionOptions = new SsdMobilenetv1Options()\r\n): DetectAllFacesTask {\r\n return new DetectAllFacesTask(input, options)\r\n}","import { TNetInput } from '../dom';\r\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\r\nimport { IMtcnnOptions, MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\r\nimport { ITinyYolov2Options, TinyYolov2Options } from '../tinyYolov2';\r\nimport { detectAllFaces } from './detectFaces';\r\n\r\n// export allFaces API for backward compatibility\r\n\r\nexport async function allFacesSsdMobilenetv1(\r\n input: TNetInput,\r\n minConfidence?: number\r\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\r\n console.warn('allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead')\r\n return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\r\n .withFaceLandmarks()\r\n .withFaceDescriptors()\r\n}\r\n\r\nexport async function allFacesTinyYolov2(\r\n input: TNetInput,\r\n forwardParams: ITinyYolov2Options = {}\r\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\r\n console.warn('allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead')\r\n return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\r\n .withFaceLandmarks()\r\n .withFaceDescriptors()\r\n}\r\n\r\nexport async function allFacesMtcnn(\r\n input: TNetInput,\r\n forwardParams: IMtcnnOptions = {}\r\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\r\n console.warn('allFacesMtcnn is deprecated and will be removed soon, use the high level api instead')\r\n return await detectAllFaces(input, new MtcnnOptions(forwardParams))\r\n .withFaceLandmarks()\r\n .withFaceDescriptors()\r\n}\r\n\r\nexport const allFaces = allFacesSsdMobilenetv1\r\n","export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\r\n if (arr1.length !== arr2.length)\r\n throw new Error('euclideanDistance: arr1.length !== arr2.length')\r\n\r\n const desc1 = Array.from(arr1)\r\n const desc2 = Array.from(arr2)\r\n\r\n return Math.sqrt(\r\n desc1\r\n .map((val, i) => val - desc2[i])\r\n .reduce((res, diff) => res + Math.pow(diff, 2), 0)\r\n )\r\n}","import { FaceMatch } from '../classes/FaceMatch';\r\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\r\nimport { euclideanDistance } from '../euclideanDistance';\r\nimport { WithFaceDescriptor } from '../factories';\r\n\r\nexport class FaceMatcher {\r\n\r\n private _labeledDescriptors: LabeledFaceDescriptors[]\r\n private _distanceThreshold: number\r\n\r\n constructor(\r\n inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\r\n distanceThreshold: number = 0.6\r\n ) {\r\n\r\n this._distanceThreshold = distanceThreshold\r\n\r\n const inputArray = Array.isArray(inputs) ? inputs : [inputs]\r\n\r\n if (!inputArray.length) {\r\n throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\r\n }\r\n\r\n let count = 1\r\n const createUniqueLabel = () => `person ${count++}`\r\n\r\n this._labeledDescriptors = inputArray.map((desc) => {\r\n if (desc instanceof LabeledFaceDescriptors) {\r\n return desc\r\n }\r\n\r\n if (desc instanceof Float32Array) {\r\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\r\n }\r\n\r\n if (desc.descriptor && desc.descriptor instanceof Float32Array) {\r\n return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\r\n }\r\n\r\n throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\r\n })\r\n }\r\n\r\n public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\r\n public get distanceThreshold(): number { return this._distanceThreshold }\r\n\r\n public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\r\n return descriptors\r\n .map(d => euclideanDistance(d, queryDescriptor))\r\n .reduce((d1, d2) => d1 + d2, 0)\r\n / (descriptors.length || 1)\r\n }\r\n\r\n public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\r\n return this.labeledDescriptors\r\n .map(({ descriptors, label }) => new FaceMatch(\r\n label,\r\n this.computeMeanDistance(queryDescriptor, descriptors)\r\n ))\r\n .reduce((best, curr) => best.distance < curr.distance ? best : curr)\r\n }\r\n\r\n public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\r\n const bestMatch = this.matchDescriptor(queryDescriptor)\r\n return bestMatch.distance < this.distanceThreshold\r\n ? bestMatch\r\n : new FaceMatch('unknown', bestMatch.distance)\r\n }\r\n\r\n public toJSON(): any {\r\n return {\r\n distanceThreshold: this.distanceThreshold,\r\n labeledDescriptors: this.labeledDescriptors.map((ld) => ld.toJSON())\r\n };\r\n }\r\n\r\n public static fromJSON(json: any): FaceMatcher {\r\n const labeledDescriptors = json.labeledDescriptors\r\n .map((ld: any) => LabeledFaceDescriptors.fromJSON(ld));\r\n return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\r\n }\r\n\r\n}","import { Mtcnn } from './Mtcnn';\r\n\r\nexport * from './Mtcnn';\r\nexport * from './MtcnnOptions';\r\n\r\nexport function createMtcnn(weights: Float32Array) {\r\n const net = new Mtcnn()\r\n net.extractWeights(weights)\r\n return net\r\n}","import { TinyFaceDetector } from './TinyFaceDetector';\r\n\r\nexport * from './TinyFaceDetector';\r\nexport * from './TinyFaceDetectorOptions';\r\n\r\nexport function createTinyFaceDetector(weights: Float32Array) {\r\n const net = new TinyFaceDetector()\r\n net.extractWeights(weights)\r\n return net\r\n}","import { Dimensions, IDimensions } from './classes';\r\nimport { FaceDetection } from './classes/FaceDetection';\r\nimport { FaceLandmarks } from './classes/FaceLandmarks';\r\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\r\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\r\n\r\nexport function resizeResults<T>(results: T, dimensions: IDimensions): T {\r\n\r\n const { width, height } = new Dimensions(dimensions.width, dimensions.height)\r\n\r\n if (width <= 0 || height <= 0) {\r\n throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({ width, height })}`)\r\n }\r\n\r\n if (Array.isArray(results)) {\r\n return results.map(obj => resizeResults(obj, { width, height })) as any as T\r\n }\r\n\r\n if (isWithFaceLandmarks(results)) {\r\n const resizedDetection = results.detection.forSize(width, height)\r\n const resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height)\r\n\r\n return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks)\r\n }\r\n\r\n if (isWithFaceDetection(results)) {\r\n return extendWithFaceDetection(results, results.detection.forSize(width, height))\r\n }\r\n\r\n if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\r\n return (results as any).forSize(width, height)\r\n }\r\n\r\n return 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