shopar-plugin
Version:
Plugin for the Web that seamlessly integrates into your webpage to create embedded virtual try-on and 3D preview capabilities.
2 lines • 421 kB
JavaScript
/*! For license information please see xzimg.js.LICENSE.txt */
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e=this.getTensorsForGradient(t,s,l);n=this.saveTensorsForBackwardMode(e)}return l}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};i=()=>{const e=this.backend.numDataIds();o=this.tidy((()=>t(this.backend,s)));const n=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,e,n),n}}const{inputs:l,attrs:p}=e,c=nt(e)?null:e.backwardsFunc;let h;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(h=this.profiler.profileKernel(u,l,(()=>i())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs):t=i()})),r&&this.addTapeNode(u,l,t,c,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map((e=>null!=l[e]?l[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const r=ge(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let a;r.saveAllInputs?(m(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),a=Object.keys(t).map((e=>t[e]))):a=e.map((e=>t[e]));const i=n.filter(((e,t)=>s[t]));return a.concat(i)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;"string"===n&&k(e[0])&&(s=e.map((e=>xe(e))));const a=r.write(s,t,n),i=new je(t,n,a,this.nextTensorId());if(this.trackTensor(i,r),"string"===n){const e=this.state.tensorInfo.get(a),t=function(e){if(null==e)return 0;let t=0;return e.forEach((e=>t+=e.length)),t}(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return i}makeTensorFromDataId(e,t,n,r){const s={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(s,r)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:r,dtype:s}=e,a=new je(r,s,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const s=new We(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[s.name])throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*S(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof We||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*S(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map((e=>e.totalBytesSnapshot))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,s,a){const i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},o=ge(e);null!=o&&(r=o.gradFunc),null!=r&&(i.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],r=A(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=et(e),n=new Set(t.map((e=>e.id)));for(let e=0;e<this.state.activeScope.track.length;e++){const t=this.state.activeScope.track[e];t.kept||n.has(t.id)||t.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach((e=>{e.kept||e.scopeId!==r.id||this.track(e)}))}gradients(e,t,n,r=!1){if(m(t.length>0,(()=>"gradients() received an empty list of xs.")),null!=n&&"float32"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const s=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));m(s instanceof je,(()=>"The result y returned by f() must be a tensor."));const a=function(e,t,n){const r={},s={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){const a=e[n],i=a.inputs;for(const e in i){const n=i[e];let o=!1;for(let e=0;e<t.length;e++)if(r[n.id]){a.outputs.forEach((e=>r[e.id]=!0)),o=!0,s[a.id]=!0;break}if(o)break}}const a={};a[n.id]=!0;const i={};for(let t=e.length-1;t>=0;t--){const n=e[t],r=n.inputs;for(let e=0;e<n.outputs.length;e++)if(a[n.outputs[e].id]){for(const e in r)a[r[e].id]=!0,i[n.id]=!0;break}}const o=[];for(let t=0;t<e.length;t++){const n=e[t];if(s[n.id]&&i[n.id]){const e={};for(const t in n.inputs){const s=n.inputs[t];r[s.id]&&(e[t]=s)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,o.push(t)}}return o}(this.state.activeTape,t,s);if(!r&&0===a.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",(()=>{const e={};e[s.id]=null==n?function(e){const t=function(e,t){const n=A(e,"float32");for(let e=0;e<n.length;e++)n[e]=1;return n}(g(e));return it.makeTensor(t,e,"float32")}(s.shape):n,function(e,t,n,r){for(let s=t.length-1;s>=0;s--){const a=t[s],i=[];if(a.outputs.forEach((t=>{const n=e[t.id];null!=n?i.push(n):i.push(null)})),null==a.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);const o=a.gradient(i);for(const t in a.inputs){if(!(t in o))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(o)}.`);const s=n((()=>o[t]()));if("float32"!==s.dtype)throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${s.dtype}'`);const i=a.inputs[t];if(!_(s.shape,i.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${t}' has shape '${s.shape}', which does not match the shape of the input '${i.shape}'`);if(null==e[i.id])e[i.id]=s;else{const t=e[i.id];e[i.id]=r(t,s),t.dispose()}}}}(e,a,(e=>this.tidy(e)),ot);const r=t.map((t=>e[t.id]));return 0===this.state.gradientDepth&&(this.state.activeTape.forEach((e=>{for(const t of e.saved)t.dispose()})),this.state.activeTape=null),{value:s,grads:r}}))}customGrad(e){return m(M(e),(()=>"The f passed in customGrad(f) must be a function.")),(...t)=>{let n;m(t.every((e=>e instanceof je)),(()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors"));const r={};return t.forEach(((e,t)=>{r[t]=e})),this.runKernelFunc({forwardFunc:(r,s)=>(n=e(...t,s),m(n.value instanceof je,(()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor")),m(M(n.gradFunc),(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.")),n.value),backwardsFunc:(e,r)=>{const s=n.gradFunc(e,r),a=Array.isArray(s)?s:[s];m(a.length===t.length,(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).")),m(a.every((e=>e instanceof je)),(()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."));const i={};return a.forEach(((e,t)=>{i[t]=()=>e})),i},inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){const t=Me(),n=await this.backend.time(e);return n.wallMs=Me()-t,n}track(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new rt;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}function at(){const e=B();if(null==e._tfengine){const t=new D(e);e._tfengine=new st(t)}var t;return t=e._tfengine.ENV,P=t,ze=()=>e._tfengine,e._tfengine}st.nextTensorId=0,st.nextVariableId=0;const it=at();function ot(e,t){const n={a:e,b:t};return it.runKernel(z,n)}function ut(e,t){let n=e;if(Ie(e))return"string"===t?[]:[e.length];if(Ye(e)){const t=e.channels||"RGBA";return[e.height,e.width*t.length]}if(Xe(e))return[e.buffer.size/(null==t?4:S(t))];if(!Array.isArray(e))return[];const r=[];for(;Array.isArray(n)||Ie(n)&&"string"!==t;)r.push(n.length),n=n[0];return Array.isArray(e)&&R().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&<(e,r,[]),r}function lt(e,t,n){if(n=n||[],!Array.isArray(e)&&!Ie(e))return void m(0===t.length,(()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`));m(t.length>0,(()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`)),m(e.length===t[0],(()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`));const r=t.slice(1);for(let t=0;t<e.length;++t)lt(e[t],r,n.concat(t))}function pt(e,t,n,r){if("string_or_numeric"!==e){if(null==e)throw new Error("Expected dtype cannot be null.");if("numeric"!==e&&e!==t||"numeric"===e&&"string"===t)throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function ct(e,t,n,r="numeric"){if(e instanceof Ve())return pt(r,e.dtype,t,n),e;let s=N(e);if("string"!==s&&["bool","int32","float32"].indexOf(r)>=0&&(s=r),pt(r,s,t,n),null==e||!Ie(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e){const r=null==e?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${r}'`)}const a=ut(e,s);Ie(e)||Array.isArray(e)||(e=[e]);const i="string"!==s?Ne(e,s):Ae(e,[],!0);return it.makeTensor(i,a,s)}function ht(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map(((e,s)=>ct(e,`${t}[${s}]`,n,r)))}function dt(e){const t=Object.keys(e);if(1!==t.length)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0];const r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n+="__op";const s=(...e)=>{it.startScope(n);try{const t=r(...e);return O(t)&&console.error("Cannot return a Promise inside of tidy."),it.endScope(t),t}catch(e){throw it.endScope(null),e}};return Object.defineProperty(s,"name",{value:n,configurable:!0}),s}const ft=dt({cropAndResize_:function(e,t,n,r,s="bilinear",a=0){const i=ct(e,"image","cropAndResize"),o=ct(t,"boxes","cropAndResize","float32"),u=ct(n,"boxInd","cropAndResize","int32"),l=o.shape[0];m(4===i.rank,(()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`)),m(2===o.rank&&4===o.shape[1],(()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`)),m(1===u.rank&&u.shape[0]===l,(()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`)),m(2===r.length,(()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`)),m(r[0]>=1&&r[1]>=1,(()=>`cropSize must be atleast [1,1], but was ${r}`)),m("bilinear"===s||"nearest"===s,(()=>`method must be bilinear or nearest, but was ${s}`));const p={image:i,boxes:o,boxInd:u},c={method:s,extrapolationValue:a,cropSize:r};return it.runKernel(K,p,c)}}),mt=dt({cast_:function(e,t){const n=ct(e,"x","cast");if(!function