Impressum ort.webgpu.mjs
Interaktion und
Portierbarkeitunbekannt
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/*!
* ONNX Runtime Web v1.20.1
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/
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pe({location:"ml-tensor",mlTensor:e.mlTensor,type:e.type,dims:t});default:throw new Error(`tensorReshape: tensor location ${e.location} is not supported`)}}});var pe,vt=k(()=>{"use strict";gr();vr();Ir();Ar();pe=class{constructor(t,n,r){Sr();let o,i;if(typeof t=="object"&&"location"in t)switch(this.dataLocation=t.location,o=t.type,i=t.dims,t.location){case"cpu-pinned":{let a=He.get(o);if(!a)throw new TypeError(`unsupported type "${o}" to create tensor from pinned buffer`);if(!(t.data instanceof a))throw new TypeError(`buffer should be of type ${a.name}`);this.cpuData=t.data;break}case"texture":{if(o!=="float32")throw new TypeError(`unsupported type "${o}" to create tensor from texture`);this.gpuTextureData=t.texture,this.downloader=t.download,this.disposer=t.dispose;break}case"gpu-buffer":{if(o!=="float32"&&o!=="float16"&&o!=="int32"&&o!=="int64"&&o!=="uint32"&&o!=="uint8"&&o!=="bool"&&o!=="uint4"&&o!=="int4")throw new TypeError(`unsupported type "${o}" to create tensor from gpu buffer`);this.gpuBufferData=t.gpuBuffer,this.downloader=t.download,this.disposer=t.dispose;break}case"ml-tensor":{if(o!=="float32"&&o!=="float16"&&o!=="int32"&&o!=="int64"&&o!=="uint32"&&o!=="uint64"&&o!=="int8"&&o!=="uint8"&&o!=="bool")throw new TypeError(`unsupported type "${o}" to create tensor from MLTensor`);this.mlTensorData=t.mlTensor,this.downloader=t.download,this.disposer=t.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let a,u;if(typeof t=="string")if(o=t,u=r,t==="string"){if(!Array.isArray(n))throw new TypeError("A string tensor's data must be a string array.");a=n}else{let d=He.get(t);if(d===void 0)throw new TypeError(`Unsupported tensor type: ${t}.`);if(Array.isArray(n)){if(t==="float16"&&d===Uint16Array||t==="uint4"||t==="int4")throw new TypeError(`Creating a ${t} tensor from number array is not supported. Please use ${d.name} as data.`);t==="uint64"||t==="int64"?a=d.from(n,BigInt):a=d.from(n)}else if(n instanceof d)a=n;else if(n instanceof Uint8ClampedArray)if(t==="uint8")a=Uint8Array.from(n);else throw new TypeError("A Uint8ClampedArray tensor's data must be type of uint8");else throw new TypeError(`A ${o} tensor's data must be type of ${d}`)}else if(u=n,Array.isArray(t)){if(t.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");let d=typeof t[0];if(d==="string")o="string",a=t;else if(d==="boolean")o="bool",a=Uint8Array.from(t);else throw new TypeError(`Invalid element type of data array: ${d}.`)}else if(t instanceof Uint8ClampedArray)o="uint8",a=Uint8Array.from(t);else{let d=nt.get(t.constructor);if(d===void 0)throw new TypeError(`Unsupported type for tensor data: ${t.constructor}.`);o=d,a=t}if(u===void 0)u=[a.length];else if(!Array.isArray(u))throw new TypeError("A tensor's dims must be a number array");i=u,this.cpuData=a,this.dataLocation="cpu"}let s=Tr(i);if(this.cpuData&&s!==this.cpuData.length&&!((o==="uint4"||o==="int4")&&Math.ceil(s/2)===this.cpuData.length))throw new Error(`Tensor's size(${s}) does not match data length(${this.cpuData.length}).`);this.type=o,this.dims=i,this.size=s}static async fromImage(t,n){return yr(t,n)}static fromTexture(t,n){return br(t,n)}static fromGpuBuffer(t,n){return wr(t,n)}static fromMLTensor(t,n){return _r(t,n)}static fromPinnedBuffer(t,n,r){return $r(t,n,r)}toDataURL(t){return fr(this,t)}toImageData(t){return hr(this,t)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. 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we.trace>"u"?!we.wasm.trace:!we.trace)||console.timeStamp(`${e}::ORT::${t}`)},kr=(e,t)=>{let n=new Error().stack?.split(/\r\n|\r|\n/g)||[],r=!1;for(let o=0;o<n.length;o++){if(r&&!n[o].includes("TRACE_FUNC")){let i=`FUNC_${e}::${n[o].trim().split(" ")[1]}`;t&&(i+=`::${t}`),St("CPU",i);return}n[o].includes("TRACE_FUNC")&&(r=!0)}},_e=e=>{(typeof we.trace>"u"?!we.wasm.trace:!we.trace)||kr("BEGIN",e)},ye=e=>{(typeof we.trace>"u"?!we.wasm.trace:!we.trace)||kr("END",e)}});var It,Er=k(()=>{"use strict";$t();xt();_n();It=class e{constructor(t){this.handler=t}async run(t,n,r){_e();let o={},i={};if(typeof t!="object"||t===null||t instanceof me||Array.isArray(t))throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");let s=!0;if(typeof n=="object"){if(n===null)throw new TypeError("Unexpected argument[1]: cannot be null.");if(n instanceof me)throw new TypeError("'fetches' cannot be a Tensor");if(Array.isArray(n)){if(n.length===0)throw new 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